DRIVERLESS CAR

June 12th, 2016

DRIVERLESS CAR:

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Prologue:

Karl Benz invented the automobile in 1885. Later that year Benz took it out for the first public test drive and he crashed into a wall. Car crash trend continues till date. In spite of stronger cars, extra seat belts, and air bags, 1.25 million people are killed on the world’s roads every year. What could have prevented the accident? The obvious answer is that you could have — by paying attention. Driver error is the most common cause of traffic accidents, and with cell phones, drunken driving, in-car entertainment systems, more traffic and more complicated road systems, it isn’t likely to go away. But if drivers aren’t going to concentrate on the road, who is? If technology continues on its current course, your car will do the concentrating for you. Automakers are developing complex systems that allow cars to drive themselves. Machines are much better at following rules than humans; motorway signs advising drivers to slow down or not change lane to avoid creating jams are often ignored by motorists – not so by computers. Cars are already a feat in engineering, but that’s not enough – now we want cars to drive themselves. This technology offers the possibility of significant benefits to society including saving lives; reducing crashes, traffic congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. Furthermore high and full automation represents a promising application of the Internet of Things (IoT) in the mobility sector. Driverless car is compared to smartphone. It will be indispensable to your life. It will do all sorts of things we can’t even think of today. Industry analysts seem pretty certain that driverless cars will be on the road within a decade. The driverless technology industry is expected to be worth £900 billion globally by 2025 and is currently growing by 16 percent a year. Human drivers may be forgiven for making an instinctive but nonetheless bad split-second decision, such as swerving into incoming traffic rather than the other way into a field. But programmers and designers of automated cars don’t have that luxury, since they do have the time to get it right and therefore bear more responsibility for bad outcomes.

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Abbreviations and synonyms:

AD = automated driving

AV = automated vehicle

SDV = self-driving vehicle

NHTSA = National Highway Traffic Safety Administration of the U.S.

SAE = Society of Automotive Engineers International

ADAS = advanced driver assistance systems

ACC = adaptive cruise control

LDW = lane departure warning

LKA = lane keeping assistant

ABS = anti-lock brakes system

ESC = electronic stability control

V2V = vehicle to vehicle

V2I = vehicle to infrastructure

IoT = internet of things

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Introduction to driverless car:

A silver BMW 5 Series is weaving through traffic at roughly 120 kilometers per hour (75 mph) on a freeway that cuts northeast through Bavaria between Munich and Ingolstadt. You are in the driver’s seat, watching cars and trucks pass by, but you haven’t touched the steering wheel, the brake, or the gas pedal for at least 10 minutes. The BMW approaches a truck that is moving slowly. To maintain its speed, the car activates its turn signal and begins steering to the left, toward the passing lane. Just as it does, another car swerves into the passing lane from several cars behind. The BMW quickly switches off its signal and pulls back to the center of the lane, waiting for the speeding car to pass before trying again. Putting your life in the hands of a robot chauffeur offers an unnerving glimpse into how driving is about to be upended. The automobile, which has followed a path of steady but slow technological evolution for the past 130 years, is on course to change dramatically in the next few years, in ways that could have radical economic, environmental, and social impacts. The first autonomous systems, which are able to control steering, braking, and accelerating, are already starting to appear in cars; these systems require drivers to keep an eye on the road and hands on the wheel. But the next generation, such as BMW’s self-driving prototype, could be available in less than a decade and free drivers to work, text, or just relax. Ford, GM, Toyota, Nissan, Volvo, and Audi have all shown off cars that can drive themselves, and they have all declared that within a decade they plan to sell some form of advanced automation—cars able to take over driving on highways or to park themselves in a garage. Google, meanwhile, is investing millions in autonomous driving software, and its driverless cars have become a familiar sight on the highways around Silicon Valley over the last several years.

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An autonomous car (driverless car, self-driving car, robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Autonomous vehicles detect surroundings using radar, Lidar, GPS, Odometry, and computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.  Autonomous cars have control systems that are capable of analyzing sensory data to distinguish between different cars on the road, which is very useful in planning a path to the desired destination. Whether you call them self-driving, driverless, automated, or autonomous, these vehicles are on the move. The switch from horse-drawn carriages to motor cars provides an instructive analogy. Cars were originally known as “horseless carriages”—defined, like driverless cars today, by the removal of a characteristic. Recent announcements by Google (which drove over 500,000 miles on its original prototype vehicles) and other major automakers indicate the potential for development in this area. Driverless cars are often discussed as “disruptive technology” with the ability to transform transportation infrastructure, expand access, and deliver benefits to a variety of users. Some observers estimate limited availability of driverless cars by 2020 with wide availability to the public by 2040.

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Features of self-driving cars:

Self-driving cars are capable of

1. Sensing its environment

2. Navigating without human input

3. Fulfilling the human transportation capabilities of a traditional car

4. Making intelligent decisions

5. Maintaining an internal map and using that map to find optimal paths; e.g. road structures, pedestrians and other vehicles

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Key differences between autonomous cars and regular cars:

Today’s autonomous cars are inferior to human drivers in important ways:

1. If a construction worker uses hand gestures to tell a car to either go or to stop, no autonomous car today can reliably make the right decision.

2. When the sun is immediately behind a traffic light, most cameras won’t be able to recognize the color of the signal through the glare.

3. If we see a truck with a “Makes Wide Turns” sign, we know how to adjust our driving accordingly. If we see children distracted by the ice cream truck across the street, we know to slow down, as they may dash toward it. Today’s computers aren’t nearly as skilled at interpreting complex situations like these.

Autonomous vehicles also have advantages over humans:

1. We can build them to have no blind spots, to maintain full 360-degree awareness.

2. They will never drive drunk or be distracted while driving. They don’t get tired, and need breaks only for maintenance.

3. They have much faster reaction time than humans.

Because computers see and understand the world differently, they will drive differently than people. Artificial intelligence (AI) is making tremendous strides, but for the near future, we should not expect computers to drive in the same way as humans.

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The story is often told in this way: The year is 2035 and you have just woken up and it is time to go to work. You prepare for the day; take a shower, eat breakfast, grab the notes for this morning’s meeting, and head for the car. You slip inside, set the destination, and sit back to do some light reading in the 20-minute ride to work. Your car will drive for you, no problem. Not only can you daydream and read on your way, your commute has gotten faster and gridlock is relatively rare. In addition, “driving” has become much safer than the millions of crashes and thousands of fatalities of several decades ago. That reduction was part of a trend that culminated with autonomous and connected vehicles. Driver error was the cause of most of those crashes and after years of technology improvement that provided more assistance to the driver, the driver was taken out of the equation altogether. In the most advanced examples of this story, after dropping you off at work, the car is instructed to gather another family member such as an elderly parent or child who could not normally navigate the roadways. In some truly transformational examples, the car is not owned by the user, instead a municipality or a private company owns a fleet of vehicles that can be summoned at a moment’s notice. In consumer technology, the self-driving vehicle is often called disruptive and transformational. Observers have noted that self-driving vehicles (SDVs) may change not only the way we drive but also how we use time and how urban landscapes are developed, and people are starting to take notice. Car manufacturers have found a way to incorporate more computer technology into their vehicles over the last several decades to enhance vehicle offerings. Now, back-up cameras, assisted braking, GPS, and stability control systems come standard in many models and have improved performance and safety. These lower level forays into computerized or smart vehicles signal the potential for a more cooperative relationship. With technology companies like Google developing their own self-driving technology for use in existing vehicle models, it appears that technology and car manufacturers may work together on SDV development.  2013 turned into the year of the self-driving car. Manufacturers from Bosch to Mercedes to Tesla gave updates on their self-driving car plans. Government regulators, such as NHTSA, issued rules and recommendations for the potential self-driving car market. 2020 is the most often quoted time frame for the availability of the next level of self-driving vehicles, with wider adoption in 2040-2050. However, there are many obstacles to overcome to make this technology viable, widely available, and permissible. These include developing technology affordable enough for the consumer market, creating a framework to deal with legal and insurance challenges, adapting roadways to vehicle use if necessary, and addressing issues of driver trust and adoption of the new technology. There is even some question as to who will be considered the ‘driver’ in the self-driving realm.

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Here are two things we know about human beings: First, they are nowhere near as good at driving as they like to think. Second, they are very resistant to change, even when that change will prevent millions of deaths. How bad at driving are we? In the U.S. alone, people get into roughly 5.5 million crashes a year, at an estimated cost of $450 billion. The last year that fewer than 30,000 people died on American roads was 1945. Compared to the rest of the world, however, America has about 12 people per 100,000 residents die each year in car accidents. In India, that number is 20. Thailand, Libya, the Dominican Republic and Eritrea have around 40 deaths per 100,000 people each year. In total, human error behind the wheel accounts for 1.25 million deaths per year or one every 25 seconds. The good news is that the technology already exists to alleviate this problem. The technology is already mature. Nowhere has the cars’ ability to look out for pedestrians and all the hazards of the road, been better illustrated than in a story about a duck being shooed across a pedestrian crossing by a woman in an electric wheelchair. The car saw everything and stopped in front of the duck without a second thought. Would a human driver have even seen it? Unpredictable pedestrian behavior was the last frontier in driverless car tech; we thought it would take years longer to solve. Apparently it hasn’t.  One of the biggest problems Google now must overcome is regulatory: Driverless cars have to stick to speed limits, because it would be illegal to program them otherwise. But practically no one on the road sticks to the speed limit, so the Google car is stuck in the slow lane. Google’s self-driving cars are programmed to exceed speed limits by up to 10mph (16km/h), according to the project’s lead software engineer. Dmitri Dolgov told Reuters that when surrounding vehicles were breaking the speed limit, going more slowly could actually present a danger, and the Google car would accelerate to keep up. Commenting on Google self-drive cars’ ability to exceed the speed limit, a Department for Transport spokesman said: “There are no plans to change speed limits, which will still apply to driverless cars”.  We have a deeply-rooted misconception that getting to your destination in less time means you have to reach a higher speed. This is patently false. In much of our commutes and day-to-day trips, the top speed we reach at any given point is more than negated by the amount of time we spend not moving or moving slowly due to our inefficient behavior or infrastructure. The autonomous vehicle traffic will be faster without increasing speed, and could even do so with lower speed limits.

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Cars that can drive themselves under limited conditions are expected to be available within five to 10 years. Versions able to navigate under most conditions may take 10 to 20 years. Autonomous “mobility-on-demand” services — think Uber and Lyft without a driver — will result in double-digit increases in travel by people in two age groups: those over 65, and those 16 to 24. scenario depends on a societal shift from private vehicle ownership to commercial fleets of driverless cars that can be quickly summoned with a phone app. Driverless fleets would have to become super-efficient carpools, picking up and dropping off multiple passengers traveling in the same direction. When people are no longer in control of their cars they will not need driver insurance—so goodbye to motor insurers and brokers. Traffic accidents now cause about 2 million hospital visits a year in America alone, so autonomous vehicles will mean much less work for emergency rooms and orthopaedic wards. Roads will need fewer signs, signals, guard rails and other features designed for the human driver; their makers will lose business too. When commuters can work, rest or play while the car steers itself, longer commutes will become more bearable, the suburbs will spread even farther and house prices in the sticks will rise. When self-driving cars can ferry children to and from school, more mothers may be freed to re-enter the workforce. The popularity of the country pub, which has been undermined by strict drink-driving laws, may be revived. And so on. Ending car accidents, reducing pollution, saving work hours, and reducing people’s transport costs are no doubt the kind of causes for which any government should care. But so are this approaching revolution’s victims and they will be numerous.

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The 19th century rise of the railroad gave citizens a faster and safer mode of transportation than the horse, and transformed society. But we had to learn how to behave around trains. Today we are saddened if a pet dog runs onto a railway track and is killed. Yet we do not blame the train for this accident, because we understand it is the nature of the machine to run on its tracks. We accommodated that nature by developing new ways to behave around trains—such as not standing on the tracks—along with new infrastructure like railway crossings with distinctive lights and bells to keep people out of danger. Train travel today is far safer than horseback travel. In the future, computer driven cars will be far safer than human driven cars. But just as trains are different than horses, we should recognize that computer driven cars are different from human driven cars, and find novel ways to safely incorporate them into our lives. Driverless vehicles, otherwise known as autonomous, automated or self-driving cars, are no longer science fiction. The technology is here, and several companies are already testing them on the roads. Not all of these vehicles are fully autonomous today. Many are considered “partially” or “highly” automated and still require some driver intervention. Predictions vary on when fully autonomous vehicles will be available for purchase but it could happen by 2025. Even then humans still will be required to remain in the driver seat, ready to take control at a moment’s notice when the car’s technology stops working its magic. It could be decades before cars come standard without a steering wheel or pedals.  For the past hundred years, innovation within the automotive sector has created safer, cleaner, and more affordable vehicles, but progress has been incremental. The industry now appears close to substantial change, engendered by autonomous, or “self-driving,” vehicle technologies. And there are great social benefits. The advantages of driver –assisting technology for disabled people or those with poor eyesight are clear. We saw a Google video showing a man who was reported to have lost 95% of his vision driving a Google-car.

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Owning a car means car payments, insurance, registration, maintenance, gas prices, smog, tickets, accidents, finding parking, and dealing with the stress of traffic. Imagine a future without congestion, car crashes, smog, or road rage. It’s coming sooner than you think. Summoned with an Uber-like smartphone app, driverless cars will revolutionize transportation. For less than bus fare you’ll enjoy the quiet, comfortable door-to-door service you’d get from a personal chauffeur. A chauffeur that is never distracted, never tired or testy, and always knows the fastest and safest route to get you where you’re going. No cash, no tipping, no crowds, no congestion – just hop in, enjoy the ride, hop out, and be on your way. These cars will be electric: quiet, clean, and so safe that deaths and disabilities will be rare. Instead of dealing with road rage and the frustration of bumper-to-bumper traffic, you’ll be free to text, Facebook with friends, or get a head start on your workday. Since you can cut your cost in half by riding with another passenger, seamlessly arranged by your mobility provider, traffic congestion will slowly fade away. Driverless car Revolution explains the benefits for people of all ages, from kids through seniors, plus the disabled, the working poor, tourists and other special groups.

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Telematics and the Self-Driving Car:

Telematics is the blending of computers and wireless telecommunications technologies, ostensibly with the goal of efficiently conveying information over vast networks to improve a host of business functions or government-related public services. The most notable example of telematics may be the Internet itself, since it depends on a number of computer networks connected globally through telecommunication backbones. The term has evolved to refer to automobile systems that combine global positioning satellite (GPS) tracking and other wireless communications for automatic roadside assistance and remote diagnostics. General Motors Corp. first popularized automotive telematics with its OnStar system. Major automakers are equipping new prototype vehicles with wireless-based services controlled by voice commands. This kind of telematics could enable motorists to perform a variety of wireless functions such as accessing the Internet, receiving or sending e-mail, downloading digital audio and video files, or obtaining “smart” transportation information. With the recent issuing of self-driving car permits (applied for by Mercedes-Benz, Audi, and Google to name a few), we are now the closest we have ever been to seeing the “autonomous vehicle,” also known as the self-driving car, in our everyday lives. The car itself essentially becomes a telematics device complete with the ability to send, receive, store, and use a vast amount of telematics information and for various purposes. That being said, the world of telematics is about to experience an interesting challenge in accounting for these new arrivals.

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Autonomous cars work best as fleets rather than as private property, because a car that can drive itself can be put to use even when you aren’t in it, and the tech companies making them prefer to sell services rather than products. Eventually, car ownership could become a thing of the past. That would mean an end to the pride and personalization of owning a car. Not to mention living with one. Perhaps the garage, that great cornerstone of suburban architecture, will become a relic. Likewise parking spaces and lots, freeing up valuable real estate for greener and denser urban living. (Meanwhile, the exurbs could prosper if people no longer dread a long drive to work.) Your children might give as little thought to the kind of car they ride in as you do to the brand of subway train you take. As idyllic as it might seem not to have to finance, drive, or park a car, there will be downsides. Once autonomous vehicles are everywhere, letting humans share the roads as pedestrians, bicyclists, or drivers could be seen as too dangerous. Driving conventions like traffic lights and dedicated turn lanes could become obsolete, and transit could develop into a pretzeled web of robotics that no human brain can navigate.

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The UK government has announced that driverless cars will be allowed on public roads from January 2015. The US States of California, Nevada and Florida have all approved tests of the vehicles. In California alone, Google’s driverless car has done more than 300,000 miles on the open road.  In 2013, Nissan carried out Japan’s first public road test of an autonomous vehicle on a highway. And in Europe, the Swedish city of Gothenburg has given Volvo permission to test 100 driverless cars – although that trial is not scheduled to occur until 2017.  In May 2014, Google unveiled plans to manufacture 100 self-driving vehicles. The search-giant exhibited a prototype which has no steering wheel or pedals – just a stop-go button. Google has also put its autonomous driving technology in cars built by other companies, including Toyota, Audi and Lexus. Other major manufacturers, including BMW, Mercedes-Benz, Nissan and General Motors, are developing their own models. Most recently, the Chinese search engine Baidu also declared an interest, saying its research labs were at an “early stage of development” on a driverless car project.

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Oxford University unveiled robot car in 2013. Scientists say the technology could be installed in cars within 15 years Robot car is adapted Nissan Leaf electric that has inbuilt lasers and cameras. They act as ‘eyes’ mapping out 3D routes fed into computer in the car’s boot. Car can be put into ‘autodrive’ on familiar routes by tapping on an iPad. Car slows to a controlled stop if there is an obstruction in the road. It asks the driver via an iPad on the dashboard whether they want to engage the autopilot and, at a touch of the screen, the car takes over the controls. A laser under the front bumper scans the direction of travel around 13 times per second for obstacles, such as pedestrians, cyclists, or other cars, up to 164ft ahead and in an 85 degree field of view. If the car sees an obstacle, it slows and comes to a controlled stop. The driver can also tap the brake pedal, like in current cruise control systems, to regain control from the computer at any time. Right now, the technology alone adds $70,000 to $100,000 to the cost of a vehicle. Few people could pay that much more for a magic flying carpet, let alone a car. Automakers are wrestling to make it affordable, and there are projections that by the time autocars go into mass production, the additional cost might fall to between $3,000 and $5,000. Lots of sensory equipment feed into the vehicle’s computers. Radar, lasers and cameras collect data on the distance to objects and their speed if they’re moving. GPS helps and an inertial navigation system in the computer uses dead reckoning to continuously calculate position, orientation, direction and speed of the vehicle and surrounding objects. Without getting too deep into the weeds: Cloud-based data could be used to continually update the on-board computer, including data collected from other cars.

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Motivations and Objectives of automated driving (AD):

The motivations for implementing AD are versatile and consider among others environmental, demo-graphic, social and economic aspects. The statistics say that 68% of the EU population including associated states live in urban areas, whereas six European cities count more than three million inhabitants. According to the World Health Organization (WHO) the predictions are that almost one third of the overall world’s population will live in cities by 2030. This justifies the need for technologies that will support urban mobility. AD offers an excellent solution due to its capability to optimize traffic flows, thus decreasing traffic jams and accidents. In this way, the reduction of fuel consumption and thus, the reduction of carbon dioxide and other noxious emissions, could be considerable. Also a significant increase of road safety and comfort is to be expected due to avoidance of human driving errors. This can boost the flexibility of unconfident and elderly drivers and provide their inclusion in mobility. In a way we can speak about “personalized” transport where a certain level of automation can be individually adapted according to customer’s needs. This implies that the time in which the driver doesn’t fully pay attention onto the driving process, can be used either to rest or to work, for instance, increasing the comfort for employees who are frequently on the move. Therefore, one can expect that AD will induce an increase of productivity.

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History of driverless car:

Experiments have been conducted on automating cars since at least the 1920s; promising trials took place in the 1950s and work has proceeded since then. The first self-sufficient and truly autonomous cars appeared in the 1980s, with Carnegie Mellon University’s Navlab and ALV projects in 1984 and Mercedes-Benz and Bundeswehr University Munich’s EUREKA Prometheus Project in 1987. Since then, numerous major companies and research organizations have developed working prototype autonomous vehicles, including Mercedes-Benz, General Motors, Continental Automotive Systems, IAV, Autoliv Inc., Bosch, Nissan, Renault, Toyota, Audi, Volvo, Tesla Motors, Peugeot, AKKA Technologies, Vislab from University of Parma, Oxford University and Google. In July 2013, Vislab demonstrated BRAiVE, a vehicle that moved autonomously on a mixed traffic route open to public traffic. In 2015 four states in the U.S. allowed testing of fully autonomous cars on public roads. While autonomous cars have generally been tested in regular weather on normal roads, Ford has been testing its autonomous cars on snow-covered roads.

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In August 2013, Daimler R&D with Karlsruhe Institute of Technology/FZI, made a Mercedes-Benz S-class vehicle with close-to-production stereo cameras and radars drive completely autonomously for about 100 km from Mannheim to Pforzheim, Germany, following the historic Bertha Benz Memorial Route. In August 2013 Nissan announced its plans to launch several driverless cars by 2020. On May 27, 2014, Google announced plans to unveil 100 autonomous car prototypes built from scratch inside Google’s secret X lab, as manifestations of years of work that began by modifying existing vehicles.  In October 2014 Tesla Motors announced its first version of AutoPilot Model S cars equipped with the system capable of lane control with autonomous steering, braking and speed limit adjustment based on signals image recognition. The system also provides autonomous parking and is able to receive software updates to improve skills over time. As of March 2015, Tesla has been testing the autopilot system on the highway between San Francisco and Seattle with a driver but letting the car to drive the car almost unassisted. In February 2015, the UK Government announced it would oversee public trials of the LUTZ Pathfinder driverless pod in Milton Keynes. In April 2015, a car designed by Delphi Automotive became the first automated vehicle to complete a coast-to-coast journey across North America. It travelled from San Francisco to New York, under computer control for 99% of that distance. As of July 2015, The Economist notes that automobile manufacturers draw a distinction between autonomous cars and self-driving cars: “Carmakers…are keen to draw a distinction between [autonomous cars and self-driving cars] and with good reasons of self-interest. Autonomous cars will look like the vehicles we drive today, [taking] over from the driver under certain circumstances…. Self-driving cars are a stage further on. The steering wheel will disappear completely and the vehicle will do all the driving using the same system of sensors, radar and GPS mapping that autonomous vehicles employ. While some personal cars will remain, a fleet of shared vehicles will likely fill the streets of towns and cities”. However, better nomenclature states that self-driving cars need driver’s attention while driverless car is fully autonomous having no steering wheel and car navigates, accelerates or brakes under computer control without any need of driver at all.

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Classification and nomenclature of driverless or autonomous cars:

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It is important to understand at the outset that autonomous driving technologies do not represent a farfetched, futuristic concept. There is a continuum of these technologies, and many of them are already available today. As current advanced crash avoidance technologies become more developed and are able to work in conjunction with each other, vehicles will increasingly become able to drive autonomously.

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Advanced driver assistance systems (ADAS):

It wasn’t that long ago when we had no vision behind or below our sight lines when we backed cars, or in blind spots when we wanted to change lanes. We had to cancel or reset our cruise control for slower traffic in our lanes. And if we were momentarily drowsy or distracted, we risked drifting out of our lane or plowing into someone slowing in front of us. These readily available technologies — back-up cameras and warning systems, blind spot warnings, adaptive cruise control, lane-keeping assist — can effectively reduce these risks, and others. While back-up cameras will be federally mandated beginning in mid-2018 in the U.S., unfortunately most of these wonders are not available on most vehicles, and they are usually offered as extra-cost options. The three most popular today, likely because they are readily available and reasonably affordable (typically $200-350 each), are back-up cameras, back-up warnings and blind spot alerts. The back-up or review camera provides a clear picture on your infotainment screen or center mirror of what is behind your vehicle, often with lines that move with the steering to indicate your intended path and distance to objects behind. Back-up warning is often paired with the camera and gives audible indications of how close your vehicle is to something behind it. And blind spot warning provides both aural and visual alerts to the presence of vehicles beside and just behind you that may not appear in side mirrors.

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Advanced Driver Assistance Systems, or ADAS, are systems to help the driver in the driving process. When designed with a safe Human-Machine Interface, they should increase car safety and more generally road safety.  Advanced driver assistance systems (ADAS) are systems developed to automate/adapt/enhance vehicle systems for safety and better driving. Safety features are designed to avoid collisions and accidents by offering technologies that alert the driver to potential problems, or to avoid collisions by implementing safeguards and taking over control of the vehicle. Adaptive features may automate lighting, provide adaptive cruise control, automate braking, incorporate GPS/ traffic warnings, connect to smartphones, alert driver to other cars or dangers, keep the driver in the correct lane, or show what is in blind spots. There are many forms of ADAS available; some features are built into cars or are available as an add-on package. Some of the technology is available on the market, or ready to be marketed, some is developed but as a prototype still under test. The purpose of Advanced Driver Assistance Systems (ADAS) is that driver error will be reduced or even eliminated, and efficiency in traffic and transport is enhanced. The benefits of ADAS implementations are potentially considerable because of a significant decrease in human suffering, economical cost and pollution. ADAS is to be considered as the collection of systems and subsystems on the way to a fully automated highway system, if ever realised. Only when on a fully automated traffic lane, the vehicle can be operated under fully automated control, which is very similar to the automatic pilot in aeroplanes, bailing out the human factor. So the driverless technology is not wholly new. In fact, it’s mostly an adaptation of familiar systems drivers use every day. Like anti-lock braking, adaptive cruise control, automated parking and lane warning. Less a revolution, and more of an evolution. Today’s vehicles are so technically advanced that there is the real prospect that driverless cars could be on our roads in a relatively short amount of time. Auto manufacturers are focused on driver assistance systems and expect to have someone in the driver seat to take charge in between “self-driving” modes. Their strategy is to enhance the driving experience in the automobile and remove the “stress” aspect of it.

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Although fully autonomous vehicles are not yet available to the public, many contemporary car models have features offering limited autonomous functionality. These include adaptive cruise control, a system that monitors distances to adjacent vehicles in the same lane, adjusting the speed with the flow of traffic; lane assist, which monitors the vehicle’s position in the lane, and either warns the driver when the vehicle is leaving its lane, or, less commonly, takes corrective actions; and parking assist, which assists the driver in the task of parallel parking.

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The 2014 Mercedes S-Class has options for autonomous steering, lane keeping, acceleration/braking, parking, accident avoidance, and driver fatigue detection, in both city traffic and highway speeds of up to 124 miles (200 km) per hour. The 2014 Infiniti Q50 uses cameras, radar and other technology to deliver various lane-keeping, collision avoidance and cruise control features. One reviewer remarked, “With the Q50 managing its own speed and adjusting course, I could sit back and simply watch, even on mildly curving highways, for three or more miles at a stretch,” adding that he wasn’t touching the steering wheel or pedals. These ADAS now available on BMW models include Lane Departure Warning, Active Cruise Control and Traffic Jam Assist – the last two of which control distance to the car in front by braking and accelerating without the driver doing anything but holding the steering wheel. The driver remains ‘in the loop’ as a central principle of current ADAS strategies, although BMW and others have foreseen the next stage as the vehicle’s computer systems taking lateral and longitudinal control. This could provide a comfort aid when driving in heavy traffic. Or, an emergency stop function will take the car to a safe halt at the side of the road if the driver loses consciousness – a safety feature BMW has stated as a long-term aim for some time.

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Figure below shows evolution of fully automated car from today’s car:

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Installations of some of the most sophisticated active safety gear on passenger cars built in the U.S. are modest but rising. In the 2014 model year, 1.4% of new vehicles had adaptive cruise control, up from 1.1% in 2013; 8.4% had lane-departure prevention technology, compared with 3.4%; and 10.1% had blind-spot alert, compared with 6.3% the prior model year.

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Now I will discuss some of the ADAS in detail:

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Adaptive Cruise Control:

Adaptive cruise control (ACC) is an intelligent form of cruise control that slows down and speeds up automatically to keep pace with the car in front of you. A small radar unit behind the front grille or under the bumper measures the distance. Some cars employ a laser while others use an optical system based on stereoscopic cameras. ACC is ideal for stop-and-go traffic and rush hour commuting that swings from 60 mph to a standstill. Regardless of the technology, ACC works day and night but its abilities are hampered by heavy rain, fog or snow. In an autonomous driving car, ACC needs to track not only the car in front but also the cars in adjacent lanes in case a lane change becomes necessary.  Audi, Volkswagen, BMW, Toyota, and Subaru have deployed this technology in a variety of ways in their vehicles. Super Cruise is a GPS oriented intelligent navigation technology that predicts freeway entries and exits; it aids ACC in assessing freeway conditions and making intelligent decisions. It also integrates additional sensors in order to make autonomous decisions if a car cuts into the lane ahead.

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Collision avoidance system:

A collision avoidance system is an automobile safety system designed to reduce the severity of a collision. It uses radar (all-weather) and sometimes laser and camera (both sensor types are ineffective during bad weather) to detect an imminent crash. Once the detection is done, these systems either provide a warning to the driver when there is an imminent collision or take action autonomously without any driver input (by braking or steering or both). Collision avoidance by braking is appropriate at low vehicle speeds (e.g. below 50 km/h), while collision avoidance by steering is appropriate at higher vehicle speeds. Cars with collision avoidance may also be equipped with adaptive cruise control, and use the same forward-looking sensors. In March of 2016, the National Highway Traffic Safety Administration (NHTSA) and the Insurance Institute for Highway Safety announced the manufacturers of 99% of U.S. automobiles had agreed to include automatic emergency braking systems as a standard feature on virtually all new cars sold in the U.S. by 2022. NHTSA projected that the ensuing acceleration of the rollout of automatic emergency braking would prevent an estimated 28,000 collisions and 12,000 injuries.

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Anti-lock brakes system (ABS):

Anti-lock brakes, a standard feature in most cars, are a basic form of driverless technology. Technically, anti-lock brakes do need the driver to step on the brake pedal in order to work, but they perform a function that drivers used to have to do themselves. When a car is braking hard and doesn’t have anti-lock brakes, the wheels can lock up, sending the car into an out-of-control skid. In a car without anti-lock brakes, the driver has to pump the brake pedal to keep the wheels from locking up. With anti-lock brakes, the system does the pumping for you — and it does it better and much faster than you ever could, thanks to speed sensors in the wheels.

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Electronic stability control (ESC):

Electronic stability control (ESC), also referred to as electronic stability program (ESP) or dynamic stability control (DSC), is a computerized technology that improves a vehicle’s stability by detecting and reducing loss of traction (skidding).  When ESC detects loss of steering control, it automatically applies the brakes to help “steer” the vehicle where the driver intends to go. Braking is automatically applied to wheels individually, such as the outer front wheel to counter over-steer or the inner rear wheel to counter under-steer. Some ESC systems also reduce engine power until control is regained. ESC does not improve a vehicle’s cornering performance; instead, it helps to minimize the loss of control. According to Insurance Institute for Highway Safety and the U.S. National Highway Traffic Safety Administration, one-third of fatal accidents could be prevented by the use of the technology.

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Lane departure warning system:

In road-transport terminology, a lane departure warning system is a mechanism designed to warn the driver when the vehicle begins to move out of its lane (unless a turn signal is on in that direction) on freeways and arterial roads. These systems are designed to minimize accidents by addressing the main causes of collisions: driver error, distractions and drowsiness. In 2009 the U.S. National Highway Traffic Safety Administration (NHTSA) began studying whether to mandate lane departure warning systems and frontal collision warning systems on automobiles.

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Lane warning/keeping systems are based on:

•Video sensors in the visual domain (mounted behind the windshield, typically integrated beside the rear mirror)

•Laser sensors (mounted on the front of the vehicle)

•Infrared sensors (mounted either behind the windshield or under the vehicle)

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There are two main types of systems:

•Systems which warn the driver (lane departure warning, LDW) if the vehicle is leaving its lane (visual, audible, and/or vibration warnings)

•Systems which warn the driver and, if no action is taken, automatically take steps to ensure the vehicle stays in its lane (lane keeping system, LKS)

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Lane Keeping Assist:

Lane Keeping Assist is a feature that in addition to Lane Departure Warning System automatically takes steps to ensure the vehicle stays in its lane. Some vehicles combine adaptive cruise control with lane keeping systems to provide additional safety. While the combination of these features creates a semi-autonomous vehicle, most require the driver to remain in control of the vehicle while it is in use. This is because of the limitations associated with the lane-keeping feature.  A lane keeping assist mechanism can either reactively turn a vehicle back into the lane if it starts to leave or proactively keep the vehicle in the center of the lane. Vehicle companies often use the term “Lane Keep(ing) Assist” to refer to both reactive Lane Keep Assist (LKA) and proactive Lane Centering Assist (LCA) but the terms are beginning to be differentiated.

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Lane Change Assist:

This driver assistance system consists of two radar units. The devices are invisibly mounted in the corners of the rear bumper. One sensor operates as system master; the second unit is configured as slave. By using a private data link, the data of both radars are combined in a sensor data fusion-tracking algorithm. This technology is in volume production since 2006 and is used for example by Audi, Volkswagen, BMW, Porsche and Mazda.

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Parking Assist:

Fully Assisted Parking Aid is now available in Ford. It can now park cars in tight spaces and back into perpendicular and angled parking spaces. This is particularly much needed in Europe and Asia. The Ford automated park assist can be operated from outside of the car. It is available in all Ford models manufactured after 20113. European companies such as BMW and Volkswagen have also produced initial versions of automated park assist technology. Most recently, Tesla announced that their Model D electric car will include park assist technology. This technology uses ultrasonic sensors to scan for an open parking space at speeds as high as 19mph. When the car finds a suitable spot, it alerts the driver, who can stay in the car or get out and use a remote to finish the parking job. The car then backs itself into the parking space. Other automakers such as Mercedes also have similar technology available in their cars.

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Automatic parking is an autonomous car-manoeuvring system that moves a vehicle from a traffic lane into a parking spot to perform parallel, perpendicular or angle parking. The automatic parking system aims to enhance the comfort and safety of driving in constrained environments where much attention and experience is required to steer the car. The parking manoeuvre is achieved by means of coordinated control of the steering angle and speed which takes into account the actual situation in the environment to ensure collision-free motion within the available space. You don’t need fully autonomous cars to get big reductions in parking. Already some cars can parallel park themselves. Carmakers could soon produce vehicles that you drive yourself but that, once you’re at a parking lot, you send off to find a space by themselves. Since nobody would need to get in or out of them after they parked, they could position themselves as snugly together as Tetris bricks, fitting far more cars into our existing parking lots and garages. Achieve even this small feat of self-driving, and it could be possible to never build another piece of parking.

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Automated Highway Driving Assistant (AHDA):

Toyota’s Automated Highway Driving Assistant is a two-part system that takes over acceleration, deceleration, and lane maintenance on highways. The AHDA system represents a more capable, next generation version of features that are available today. The Toyota cars with this feature will be available by 2016. BMW recently unveiled one of the most advanced driverless technology pilot projects in early 2014. BMW’s ActiveAssist is one of the most advanced autopilots unveiled to date. It is able to navigate its way at breathtaking speeds on a test track, avoiding all obstacles. While the commercial version of an autopilot is years away from availability to the public, the predicted time-line is 2018.

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Autonomous Highway Driving (by 2020):

In autonomous highway driving, the driver can fully cede control of all safety-critical functions in certain conditions. The car senses when conditions require the driver to retake control and provides a “sufficiently comfortable transition time” for the driver to do so. This is identical to the Level 3 definition put forward by NHTSA (vide infra). Currently, Mercedes-Benz, Nissan, Volvo, BMW, and Audi have test models, which are slated to go to production by 2020.

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Current ADAS uses radar and sonar:

Radar works in any type of weather and has short-, medium-, and long-range characteristics. For example, adaptive cruise control works in the long range, looking 200m in front of the car, tracking the car, and accelerating or braking the car to maintain a certain distance. Radar also provides blind-spot detection and lane-departure warning. Early versions of these systems audibly warned the driver of an impending problem, but some implementations now take control of the car to avoid the problem. For example, the 2011 Infiniti M56 has an optional blind-spot-warning/intervention system that relies on radar scans from the left and the right rear quadrant of a car. If the radar system detects a car in the driver’s blind spot, a light comes on. If the driver activates the turn signal, an audible beep comes on. If the driver persists and starts to move into another lane, the car gently applies brakes on the opposite side of the car, moving the car back into the center of the lane. Although ultrasonic-sensor technology is more mature and less expensive than radar, car designers who care about the aesthetics of the car’s appearance are reluctant to have too many sensor apertures visible on the car’s exterior. As a more powerful and more flexible technology, radar should begin to replace ultrasonic sensors in future designs.

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ADAS vis-à-vis speed of car:

 

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Connected car:

Connected car means the presence of devices in an automobile that connect the devices to other devices within the car/vehicles and or devices, networks and services outside the car including other cars, home, office or infrastructure. Internet access is usually connected to a local area network. Internet connections can provide connections that warn of traffic, collisions and other safety alerts. Concierge services from apps or automakers alert the driver of the time to leave to arrive on time from a calendar and sends text message alerts to friends or business associates to alert them of arrival times. Typically, a connected car made after 2010 has a head-unit, in car entertainment unit, in-dash system with a screen from which the operations of the connections can be seen or managed by the driver. Types of functions that can be made include music/audio playing, smartphone apps, navigation, roadside assistance, voice commands, contextual help/offers, parking apps, engine controls and car diagnosis. Increasingly, Connected Cars (and especially electric cars) are taking advantage of the rise of smartphones, and apps are available to interact with the car from any distance. Users can unlock their cars, check the status of batteries on electric cars, find the location of the car, or remotely activate the climate control system. Despite various market drivers, there are also barriers that have prevented the ultimate breakthrough of the connected car in the past few years. One of these is the fact that customers are reluctant to pay the extra costs associated with embedded connectivity and instead use their smartphones as solution for their in-car connectivity needs. Because this barrier is likely to continue, at least in the short-term, car manufacturers are turning to smartphone integration in an effort to satisfy consumer demand for connectivity. The connected car, typically with a touchscreen console, has one major flaw — it’s potentially dangerous to interact with while driving. The answer to this is voice control, in which you speak commands to the system. Ford has employed voice recognition into its connected car platform. The future of connected cars includes Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication– for everything from traffic management to stopping a car from entering a dangerous intersection, V2V and V2I are communications systems that interact for the safety of the auto and its surrounding environment. The U.S. Department of Transportation is working on guidelines to enable V2V systems that connect to municipalities and for safety and better traffic control.

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Are autonomous cars and connected cars the same thing?

No.

An autonomous car uses on-board technology to find its way around and keep from running into things. Connected cars, a concept also in active development, provide direct short-range communication between vehicles (and highway beacons) to help them coexist better. The connected-car technology could be an asset to autonomous cars, but it’s not the steppingstone some experts once thought necessary. Connected cars still will need an active driver.

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Nomenclature of driverless cars:

The terms ‘Autonomous Vehicles’, ‘Self-driving Cars’, and ‘Driverless Cars’ tend to be used interchangeably, to mean a vehicle which can navigate between two points safely without human intervention. However, there is a continuum of autonomy. Some (existing) vehicles can take over basic functions, much like cruise control takes over speed-setting, but cannot take any responsive actions. Some hypothesized vehicles will be able to respond to a text message, turn themselves on, and drive (unoccupied) to their owner.

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Self-driving vs. driverless car:

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Typical self-driving car:

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Typical driverless car:


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Vehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent system to assist a vehicle’s operator. These features and the vehicles employing them may be labelled as intelligent or smart. A vehicle using automation for difficult tasks, especially navigation, may be referred to as semi-autonomous. A vehicle relying solely on automation is consequently referred to as robotic or autonomous. After the invention of the integrated circuit, the sophistication of automation technology increased. Manufacturers and researchers subsequently added a variety of automated functions to automobiles and other vehicles.

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Autonomous vs. automated car:

Autonomous means having the power for self-governance.  Many historical projects related to vehicle autonomy have in fact only been automated (made to be automatic) due to a heavy reliance on artificial hints in their environment, such as magnetic strips. Autonomous control implies good performance under significant uncertainties in the environment for extended periods of time and the ability to compensate for system failures without external intervention.  As can be seen from many projects mentioned, it is often suggested to extend the capabilities of an autonomous car by implementing communication networks both in the immediate vicinity (for collision avoidance) and far away (for congestion management). By bringing in these outside influences in the decision process, some would no longer regard the car’s behaviour or capabilities as autonomous; instead use the term “automated.” The term “autonomous” was chosen because it is the term that is currently in more widespread use (and thus is more familiar to the general public). However, the latter term is arguably more accurate. “Automated” connotes control or operation by a machine, while “autonomous” connotes acting alone or independently. Most of the vehicle concepts (that we are currently aware of) have a person in the driver’s seat, utilize a communication connection to the Cloud or other vehicles, and do not independently select either destinations or routes for reaching them. Thus, the term “automated” would more accurately describe these vehicle concepts.

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The label “driverless vehicle” actually covers a lot of different premises. Indeed, the cruise control, automatic braking, anti-lane drift and self-parking functions already built into many vehicles offer a certain degree of autonomy. But the term is generally used to refer to vehicles that take charge of steering, accelerating, indicating and braking during most if not all of a journey between two points, much in the same way aeroplanes can be set to autopilot. Unlike the skies, however, the roads are much more crowded, and a range of technologies are being developed to tackle the problem. One of the leading innovations is Lidar (light detection and ranging), a system that measures how lasers bounce off reflective surfaces to capture information about millions of small points surrounding the vehicle every second. The technology is already used to create the online maps used by Google and Nokia. Another complimentary technique is “computer vision” – the use of software to make sense of 360-degree images captured by cameras attached to the vehicle, which can warn of pedestrians, cyclists, roadworks and other objects that might be in the vehicle’s path. Autonomous vehicles can also make use of global-positioning system (GPS) location data from satellites; radar; ultrasonic sensors to detect objects close to the car; and further sensors to accurately measure the vehicle’s orientation and the rotation of its wheels, to help it understand its exact location. The debate now is whether to allow cars, like the prototype unveiled by Google, to abandon controls including a steering wheel and pedals and rely on the vehicle’s computer.  Or whether, instead, to allow the machine to drive, but insist a passenger be ready to wrest back control at a moment’s notice.

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A driverless car is an automobile that has an autopilot system allowing it to safely move from one place to another without help from a human driver. Ideally, the only role of a human in such a vehicle would be indicating the destination. The implementation of driverless cars could theoretically lead to many improvements in transportation, including a reduction in car accidents, more efficient transportation, and an increase in road capacity. There are, however, many obstacles to successfully implementing the concept as a common and effective method of transportation. This is especially true in situations in which a car on autopilot would need to safely navigate alongside normal cars directed by human drivers. To be useful, a driverless car must be able to navigate to a given destination based on passenger-provided instructions, avoid environmental obstacles, and safely avoid other vehicles.

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Jump from ADAS to driverless car:

All of the driver assistance systems which are in use today operates only for short times and in extremely limited settings. Auto-parking operates for a few seconds with the driver watching. Emergency braking kicks in at the last moment before an inevitable crash. Lane warning comes on briefly when a car veers out of its lane. This changes drastically once the car drives itself continuously for minutes or hours. Here from the moment that a car drives continuously, there is no margin for error; no room for gradual improvement, learning by doing or evolution. It needs to be able to cope with all short-term eventualities and crisis situations that may arise on the spot. People often argue that such assistance systems need to be supervised by the driver. This makes sense for assistance systems that operate for a few seconds or minutes (such as a parking assistant) but it cannot work for systems that drive continuously. Humans are not capable to maintain the state of alert for hours and hours which would be required to immediately counteract possible deficiencies of a driver-assistance system or to take over from it in a split-second. We can only entrust the driving task to a driver assistance system when we are sure that this system can handle all situations which arise suddenly and require immediate reaction. This means that driver assistance systems operating continuously on a highway need to be able to cope with rare situations including pedestrians and bicyclists on the highway (they do appear sometimes on highways), accidents unfolding, animals, sudden rainfall etc. No matter how capable the vehicle might be, a semiautonomous car cannot be left unattended. And the chance of someone falling asleep is a serious concern for carmakers. To prevent a driver from spacing out or worse, GM has developed a monitoring system that will use audial, visual, and possibly haptic feedback to prompt the driver to take over if it detects an emergency or that the driver is too distracted. For example, the light bar at the top of the steering wheel turns bright red when the driver needs to take control of the car. However, all the literature in psychology tells us that we are terrible overseers of highly autonomous systems. We fall asleep. We get bored. We’re inattentive.

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The defining difference between existing “driver assistance” systems and the higher levels of automation is that when using any existing driver assistance systems on the market today, the driver should be “engaged” or “in the loop” at all times. This means the driver should constantly monitor road, traffic and weather conditions, remain ready to resume manual control and be responsible for the overall safe operation of the vehicle. In the higher levels of automation, the systems are designed to allow the driver to completely ‘disengage’ from the driving task and undertake other tasks. This is sometimes known as the driver coming “out of the loop”.

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My view:

Any car that has ADAS is autonomous car and they are subdivided into semi-autonomous (having ADAS but need human to drive) and fully autonomous. Fully autonomous cars are further divided into user-operated (self-driving) and driverless cars.

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Progression of automated technology:

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Classification of autonomous car:

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According to the Institute of Mechanical Engineers (IME), there are five levels of vehicle autonomy: driver control, assisted driving, partial autonomy, high autonomy and full autonomy.  We’re currently between Assisted Driving and Partial Autonomy, where some systems are automated – adaptive cruise control, lane assist, self-parking – but the cars don’t drive themselves. That’s the next step, High Autonomy.  With High Autonomy the driver can still step in, but the car can automatically evade hazards, follow roads, anticipate what other road users are doing and make ethical decisions.  The IME predicts that automation will come in stages. By 2018 we should see manufacturers such as Volvo and Mercedes offering vehicles that can follow the road automatically – Mercedes already has prototypes that can automatically overtake slow-moving traffic – and Bosch aims to have a self-driving solution for motorway driving by 2020. Google’s autonomous car isn’t expected before 2025, but the Institute of Electrical and Electronic Engineers (IEEE) predicts that by 2040, up to 75% of all vehicles will be autonomous.

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Automation Levels:

As a foundation for a deeper analysis, the levels of automation and the criteria for their definition have to be considered since these may be a source of confusion when developments on automated driving (AD) are discussed. There are three such fundamental criteria to be considered when defining the level of vehicle automation. The first important criterion refers to the controlling functions, i.e., the ability of the system to take over none, either longitudinal or lateral control, or both at the same time. The second criterion is related to the human driver and whether he is allowed to dedicate his attention partially or completely to other activities except driving. The third criterion considers performances of the vehicle and its ability to independently “understand” the processes that appear during driving.

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An important parameter for the classification of automated driving and parking functions is their level of automation. Figure below shows an automation scale, composed of graduated levels of automation, recently published by SAE international.

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According to SAE International road vehicle automation can be classified into six different levels as per the figure above and below. Levels 0-2 take into account the human driver as the main actor responsible during driving. In case of faults, the human driver has less than one second to react and he or she isn’t allowed to divert his/ her attention towards any other activities except driving. While the European suppliers of automotive smart components and systems invented and further improved driver assistance systems for lateral and longitudinal control of levels 0 and 1 in recent years, systems for partial automation of level 2 are currently under demonstration and in the early market place phase. The most advanced solution, a combination of driver assistance systems like adaptive cruise control (ACC) and lane departure warning (LDW), is applied in high-end vehicles today. For higher levels of automation, as Levels 3-5, complicated driving and decision making processes will be adopted by the vehicle in a stepwise manner. For level 3 or conditional automation, the vehicle is becoming aware of its surroundings. The reaction time for the human driver increases to several seconds, i.e. the vehicle will alarm the driver with a request to intervene, if necessary. For automation levels 4 and 5, the reaction of the human driver extends to the couple of minutes, as the vehicle is becoming able to react independently during the entire drive. Level 3 of automation thus allows the human driver to do other activities while driving, whereas, levels 4 and 5 consider a complete adoption of the driving process by the vehicle while the driver is even able to fall sleep.

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The National Highway Traffic Safety Administration (NHTSA) in the US uses five different subclasses instead of the described six. A vehicle having the driver only and no other assistance systems or automation classifies as level 0 and the fully automated classifies vehicle as level 4. In other words, with this arrangement no difference will be made between “high” and “full” automation as by SAE.

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NHTSA defines vehicle automation as having five levels:

1. Non-Automated (level 0):

In this category, the human is in complete and sole fundamental control of the vehicle at all times. While current vehicles (without any advanced crash avoidance technologies) can be included in this category, vehicles with warning systems that assist drivers also fall into this category. Vehicles equipped with these technologies will not assume control for any driving tasks, but will provide additional information to the driver and/or warn the driver of situations requiring immediate attention. Navigational global positioning systems (GPS) are an example of a currently available technology which provides information useful to the overall task of driving, and potentially highly valuable to V2V communications.  Lane Departure Warning (LDW) is an example of a currently available warning technology. This technology alerts the driver when his or her vehicle begins to drift out of the lane of travel. Like other information and warning technologies, a LDW system does not intervene to prevent the driver from departing the lane. It merely monitors the lane markings on the roadway to determine whether the vehicle is keeping within its current driving lane. LDW systems can also warn the driver if lane makings cannot be detected or if the system malfunctions.

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2. Automation-Assisted (level 1):

The “automation-assisted” category still leaves the driving authority squarely with the driver. However, under limited normal driving or crash imminent circumstances, technologies in this category will take control away from the driver. An example of this type of technology is the electronic stability control (ESC). ESC systems use automatic computer-controlled braking of individual wheels to assist a driver in maintaining control in critical driving scenarios in which the vehicle is beginning to lose directional stability at the rear wheels (spin out) or directional control at the front wheels (plow out).  Another advanced example of automation-assisted driving is a lane-keeping system that will actively steer a vehicle back toward the center of its lane when the system detects that the vehicle is drifting into an adjacent lane or is on a collision course with a vehicle in an adjacent lane.

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3. Monitored Automation (level 2):

The “monitored automation” category is the first category in which the technology will share the driving responsibility with the driver. However, in this category, the human driver is expected to be ready to take control of the vehicle at all times. Thus, the autonomous technology is only able to assume the responsibility of driving when the conditions permit. For example, some vehicles on the market today are available with automatic parallel parking systems. This type of technology differs from automation-assisted driving technologies because the driver gives a general command to the vehicle (e.g., “park in this space”) and the vehicle effectuates that command by assuming control of the steering and making the necessary steering calculations.  Another potential example is the combination of adaptive cruise control with lane-keeping. The combination of these two technologies would potentially enable vehicles to proceed down the freeway with little or no input from the driver. However, depending on the level of sophistication in this system, drivers might still be required to intervene at any moment (e.g., lane markings disappear and the vehicle can no longer position itself in the center of the lane).

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4. Conditional Automation (level3):

In this category, the technology is sufficiently reliable such that the human driver is able to completely cede the driving responsibility to the autonomous driving system under certain circumstances. This category differs from “monitored automation” systems as drivers using “conditional automation” systems would not need to be able to assume control of the vehicle within a moment’s notice. In theory, the vehicle would be able to warn the driver of an impending condition sufficiently in advance so that the driver can safely take control. However, the drivers would be expected to be available to take control when so warned.

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5. Full Automation (level 4):

As the final category in the above diagram, “full automation” driving encompasses all of the systems necessary for the vehicle to perform automatically and independently all driving tasks in all driving scenarios. This vehicle would integrate various technologies from the previous three categories to perform all driving tasks such that a person is no longer driving. An example of a technology in this category would be a vehicle that is capable of bringing the driver anywhere. The only driver input would be the destination. The vehicle would be responsible for all driving decisions and actions during travel. Both, the conditional and high automation, assume that the human driver does not have to permanently monitor the system, but in necessary cases, he will be requested to take over the control with a certain time buffer.

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Thanks to these comprehensive project activities driver assistance systems have greatly advanced in recent years. The advanced driver assistance systems (ADAS) like adaptive cruise control (ACC) and lane departure warning (LDW) are commonplace today. In the ACC, the desired speed and the distance to be maintained to the vehicle ahead are set by the driver. LDW warns the driver in case the car moves to close to the edge of the lane. Lane keeping assist systems (LKA) are actively steering the vehicle to keep it in the lane.  Many of the smart systems and components enabling AD are shown in figure below according to their degree of market maturity and level of automation. Conditional automated driving (level 3), combining ACC and LKA with environment perception (shown as “X”), such that the driver interaction is obsolete, has not been launched to the market yet. However, there are already some vehicle manufacturers offering these features as level 2 automation with the required driver interaction, though.

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Timeline vis-à-vis degree of automation by different paths:

 

When talking about future technical progress in automated driving, evolutionary and revolutionary development paths can be distinguished as seen in the figure above: by stepwise improvements from advanced driver assistance systems can evolve into the AD system. Fundamental transformational developments characterize the revolutionary scenario that is based on technology transfer coming from the field of robotics. According to the evolutionary scenario, development and introduction of AD will pass through steady increase of levels of autonomy of the vehicle system in more and more complex environments allowing for higher and higher velocities. On the other hand side, the protection of vulnerable road users and synergies of automated driving with modes other than passenger cars are to be considered when describing the revolutionary development path of automated driving. The revolutionary scenario should not be underestimated, maybe leading to fully autonomous driving applications sooner than originally conceived.

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The transition to fully autonomous cars happen in 3 stages vis-à-vis data sharing:

Stage 1 – Limited autonomy that doesn’t depend on data sharing.

Stage 2 – Semi-autonomy that utilizes shared data.

Stage 3 – Full autonomy with shared data, advanced sensors and (perhaps) high resolution mapping.

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Stage 1 is where we are today. Vehicles have various safety systems built with the basic technologies behind autonomous cars, like 3D camera systems and millimeter wave radar. Examples of ‘stage 1′ technologies:

•Lane keeping/lane warning systems, which help you stay in your line by controlling steering or warn you as soon as you leave (data that’s determined by a camera system)

•Cruise control radar systems that let you set your cruise control to follow the vehicle ahead, slowing or braking automatically to avoid a collision.

•Cross traffic/blind spot detection systems, which use radar and/or cameras and/or ultrasonic sensors to “see” around corners, etc.

•The so-called “autopilot” system from Tesla (which is basically just a combination of lane keeping and cruise control radar), Distronic Plus from Mercedes-Benz, Hyundai’s Auto Braking, etc. are all systems that combine sensors and programming to offer excellent safety and convenience features.

Stage 1 technologies are affordable and powerful, and all new vehicles will come with these features as standard equipment as they’re likely to become federally mandated safety features this decade in the U.S.

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Stage 2 is beginning, but we’re 5-10 years away from meaningful market penetration.

Essentially, stage 2 is V2X communications, which stands for “vehicle to x”. “X” can be other vehicles as well as infrastructure. V2X will allow vehicles to share their position and course information with all the surrounding vehicles, as well as data from their on-board sensing systems.  This, in turn, will allow vehicle software to build “models” of the world around them, filling in gaps with information shared by other vehicles as well as the roadway. V2X offers tremendous opportunities for improving vehicle safety, and it may be sufficient (when combined with stage 1 technologies) to offer nearly autonomous driving. In terms of market penetration, something like 50% of vehicles will need to have both sensing systems and V2X communication for the driving public to see a major shift. But even at low market penetration percentages, V2X and advanced sensing will save lives.

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Stage 3 is a combination of V2X, and advanced sensors, and (potentially) high resolution map data.

Google, Audi, M-B, Toyota, Honda, Ford, etc. etc. are all experimenting with stage 3 right now. All automakers (or almost all of them) are deploying high-priced LIDAR (laser radar) pods on their test vehicles, and then combining these high powered LIDAR systems with radar, 3D cameras, and (in most cases) high resolution map data. The vehicles are designed to be 100% autonomous without any sort of communication (e.g. no V2X), which would make them able to drive themselves down a rural road without any driver input or any shared data. These vehicles are also able to handle all weather conditions, something that 3D camera systems can’t manage (snow and heavy rain cripple the effectiveness of 3D camera systems). The technology for stage 3 vehicles exists today. The challenge isn’t technological – it’s cost. High resolution LIDAR pods are tens of thousands of dollars (some cost upwards of $80,000). Lower resolution pods are only $8,000 a piece, but they’re not really sufficiently precise enough for complete autonomy….Which brings us to map data. If you combine highly precise map data with good (but not great) LIDAR pods, radar, and 3D cameras, you get a highly autonomous car that’s almost affordable enough for mass production.

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My view on nomenclature and classification of autonomous cars:

As you can see, I divide cars in ordinary, semi-autonomous, self-driving and driverless cars depending on degree of autonomy and technological advancement.

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Technology of driverless cars:

 

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Autonomous Vehicle Equipment and Service Requirements:

• Automatic transmissions.

• Diverse sensors (optical, infrared, radar, ultrasonic and laser) capable of operating in diverse conditions (rain, snow, unpaved roads, tunnels, etc.) and cameras.

• Wireless networks. Short range systems for vehicle-to-vehicle communications and long-range systems to access to maps, software upgrades, road condition reports, and emergency messages.

• Navigation, including GPS systems and special maps.

• Automated controls (steering, braking, signals, etc.)

• Servers, software and power supplies with high reliability standards.

• Additional testing, maintenance and repair costs for critical components such as sensors and controls.

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Components of driverless car:

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Google has developed a small fleet of autonomous vehicles that rely on video cameras, radar sensors, laser range finders, and maps collected by the company and are always manned with a driver trained to take over by disengaging cruise control when necessary.

Radar sensors dotted around the car monitor the position of vehicles nearby.

Video cameras detect traffic lights, read road signs and keep track of other vehicles, while also looking out for pedestrians and other obstacles.

Lidar sensors help to detect the edges of roads and identify lane markings by bouncing pulses of light off the car’s surroundings.

Ultrasonic sensors in the wheels can detect the position of curbs and other vehicles when parking.

Finally, a central computer analyses all of the data from the various sensors to manipulate the steering, acceleration and braking.

This is just the start. As the technology gets cheaper, the driverless car future will increasingly become a reality.

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Features driverless car:

Today’s intelligent automobile can perform many driver-assistance tasks, such as avoiding and preventing accidents and reducing the severity of accidents. To perform these tasks, the vehicles have passive safety systems, such as air bags and seat belts; active safety systems, such as electronic stability control, adaptive suspension, and yaw and roll control; and driver-assistance systems, including adaptive cruise control, blind-spot detection, lane-departure warning, drowsy-driver alert, and parking assistance. These systems require many of the same sensors that the autonomous car requires: ultrasonic sensors, radar, LIDAR systems, and vision-imaging cameras.

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There are three things required to turn a regular car into an automated one. The first is a GPS system pretty much like the ones found in many vehicles today. The second is a system to recognize dynamic conditions on the roads. And the third is a way to turn the information from the other two systems into action on your ride. What the autonomous system is supposed to achieve, in its full maturity, is the best of a computer, which is able to process large reams of data, and the ability of a human being to be adaptive in a new or known environment. While having a GPS may seem like a no-brainer, it’s actually a vital part of a self-driving car’s over-arching technology. This system, which is essentially no different than Google Maps’ driving directions, defines the “mission” of the autonomous vehicle by setting the starting and ending point of the drive. It looks at all the roads, chooses the best path, and is often better than people at doing it. Human beings are not equipped to process tremendous amounts of prior data like maps. But GPS alone is not enough to make a smart car. Its maps never (or rarely) change, and the reality of the road includes dynamics like detours, traffic, and other obstacles. Autonomous driving requires a second level of intelligence with the ability to fill in additional details in the map. This system uses an array of technology such as radar and cameras to detect the ever-changing variables that surround it. If you think of the map as having a static view of the world, the sensor system is providing a dynamic fill-in to that map, then these two together provide what is called a ‘world model’ for that autonomous vehicle. Among the sensors feeding information are cameras, radar, and lasers. Cameras let the car’s computers see what’s around it. Radar, however, allows the vehicle to see up to 200 meters away in the dark, rain, snow, or other vision-impairing circumstances (Interestingly, “adaptive” cruise control systems in newer vehicles already use radar technology.) And the lasers, which look like a spinning siren light, continuously scan the world around your car and provide the vehicle with a continuous, three-dimensional omni-directional view of its surroundings. These sensors are providing you with raw information of the world. You need very sophisticated algorithms to process all that information, just like a human would. Of course these sensors are necessary because autonomous cars are adapting to a human-driven world. There is hope that, in the future, all cars would be able to talk to each other in a connected vehicle environment. Your car would know precisely where other vehicles are, where they’re going, and where they will turn, so the computers can navigate smoothly. But we’re not there yet, though its framework is in its experimental stage. And lastly, the autonomous vehicle needs to be equipped to take the GPS and sensor information and turn it into actions, like steering, accelerating, or hitting the brakes. This is typically done by what’s called the “CAN bus” (which stands for controller area network). This in-vehicle electronic network has been in cars for decades, which means that autonomous vehicles of the future aren’t much different, mechanically, than the dumb-mobiles we’re driving today.

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A self-driving car is capable of sensing its environment and navigating without human input. To accomplish this task, each vehicle is usually outfitted with a GPS unit, an inertial navigation system, and a range of sensors including laser rangefinders, radar, and video.  The vehicle uses positional information from the GPS and inertial navigation system to localize itself and sensor data to refine its position estimate as well as to build a three-dimensional image of its environment. Data from each sensor is filtered to remove noise and often fused with other data sources to augment the original image. How the vehicle subsequently uses this data to make navigation decisions is determined by its control system. The majority of self-driving vehicle control systems implements a deliberative architecture, meaning that they are capable of making intelligent decisions by 1) maintaining an internal map of their world and 2) using that map to find an optimal path to their destination that avoids obstacles (e.g. road structures, pedestrians and other vehicles) from a set of possible paths. Once the vehicle determines the best path to take, the decision is dissected into commands, which are fed to the vehicle’s actuators. These actuators control the vehicle’s steering, braking and throttle. This process of localization, mapping, obstacle avoidance and path planning is repeated multiple times each second on powerful on-board processors until the vehicle reaches its destination.

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On board computers of autonomous car:

 

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What Google car sees:

Figure above shows a Google car’s internal map at an intersection. Google’s Self-Driving car gathers almost 1 GB of data per second. A car with no steering wheel, no brakes and no accelerator would be essentially useless without advanced positioning systems to track its course and plot an appropriate route to its destination. For this challenge, Google uses its own map system, as well as GPS satellites, inertial measurement units, and a wheel encoder to determine actual speed. The system works alongside the on-board cameras to process real-world information as well as GPS data, and driving speed to accurately determine the precise position of each vehicle, down to a few centimeters all while making smart corrections for things like traffic, road construction, and accidents. GPS is accurate and precise enough to be the basis of an autonomous system, as Audi has shown with its TTS Pikes Peak. The car used only GPS, wheel speed sensors and an accelerometer to complete the Pikes Peak race course in 27 mins, while a (good) human driver could do it in 15 mins.

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GPS and DGPS:

The Global Positioning System (GPS) is a space-based satellite navigation system that provides location and time information anywhere on or near the earth. The accuracy of a GPS receiver is about +/- 10 meters, not practical for locating an object the size of an automobile, which is about 3 meters long.  Differential Global Positioning System (DGPS) is an enhancement to GPS that improves location accuracy from +/- 10 meters to about 10 cm. The DGPS correction signal loses approximately 1 meter of accuracy for every 150 km. Shadowing from buildings, underpasses, and foliage causes temporary losses of signal. An aerial on the rear of the car receives information about the precise location of the car, thanks to GPS satellites. The car’s GPS inertial navigation unit works with the sensors to help the car localise itself. But GPS estimates may be off by several metres due to signal disturbances and other interferences from the atmosphere. To minimise the degree of uncertainty, the GPS data is compared with sensor map data previously collected from the same location. As the vehicle moves, the vehicle’s internal map is updated with new positional information displayed by the sensors.

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Inertial Navigation Systems (INS) have been used for years for vehicle navigation before the advent of GPS. INS uses inertial cues from sensors like accelerometers, speedometers and gyros to determine the vehicle position. With a good initial position (available from GPS), a well-designed INS can keep very accurate position for a short time with loss of GPS coverage due to terrain or other environmental factors.

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LIDAR:

Laser Illuminating Detection and Ranging – or LIDAR – is used to build a 3D map and allow the car to “see” potential hazards by bouncing a laser beam off of surfaces surrounding the car in order to accurately determine the distance and the profile of that object. The Google Car uses a Velodyne 64-beam laser in order to give the on-board processor a 360-degree view by mounting the LIDAR unit to the top of the car (for unobstructed viewing) and allowing it to rotate on a custom-built base. At the moment, before a self-driven car is tested, a regular car is driven along the route and maps out the route and its road conditions including poles, road markers, road signs and more. This map is fed into the car’s software helping the car identify what is a regular part of the road. As the car moves, its Velodyne laser range finder kicks in and generates a detailed 3D map of the environment at that moment. The car compares this map with the pre-existing map to figure out the non-standard aspects in the road, rightly identifying them as pedestrians and/or other motorists, thus avoiding them.

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LIDAR is an optical remote sensing technology that can measure the distance to, or other properties of, targets by illuminating the target with laser light and analysing the backscattered light. Google’s self-driving car uses Velodyne LIDAR to electronically “see” the environment. HDL-64E and HDL-32E modules use an array of either 64 or 32 lasers. On Google’s car, the module is set inside a rotating drum. Its lasers complement Google’s own mapping software and GPS data, which help orient the car on the road. The LIDAR provides additional positional data, but also identifies other cars, bicycles, pedestrians, and road hazards. The car sends out a pulse of light in a certain direction, and an on-board sensor records the reflected pulse’s time-of-flight. By sending out laser beams in all directions, collecting the reflected energy, and performing some nifty high-speed computer processing, the vehicle can create a real-time, virtual map of the obstacles in its path. Because laser light is higher in energy and shorter in wavelength than radio waves, it reflects better from non-metallic objects , such as humans and wooden power poles, and provides mapping advantages over radar. By coupling novel roof-mounted LIDAR systems with vision cameras, advanced computer processing, and GPS to position the vehicle in global coordinates, it has become possible to create a self-driving machine. Google combines the LIDAR system with vision cameras and algorithmic vision-processing systems to construct and react to a 3-D view of the world through which it is driving.

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The LIDAR system bolted to the top of Google’s self-driving car is crucially important for several reasons. First, it’s highly accurate up to a range of 100 meters. There are a few detection technologies on the car that work at greater distances, but not with the kind of accuracy you get from a laser. It simply bounces a beam off surfaces and measures the reflection to determine distance. The device used by Google — a Velodyne 64-beam laser — can also rotate 360-degrees and take up to 1.3 million readings per second, making it the most versatile sensor on the car. Mounting it on top of the car ensures its view isn’t obstructed. Google mounts regular cameras around the exterior of the car in pairs with a small separation between them. The overlapping fields of view create a parallax not unlike your own eyes that allow the system to track an object’s distance in real time. As long as it has been spotted by more than one camera, the car knows where it is. These stereo cameras have a 50-degree field of view, but they’re only accurate up to about 30 meters. Google’s LIDAR system is great for generating an accurate map of the car’s surroundings, but it’s not ideal for monitoring the speed of other cars in real time. That’s why the front and back bumper of the driverless car includes radar. This is one of the few technologies Google employs in its driverless car that you can already get in mainstream vehicles. Conventional vehicles use radar to warn you of an impending impact or even apply the brakes to prevent one, but the Google car uses radar to adjust the throttle and brakes continuously. It’s essentially adaptive cruise control that always takes into account the movement of cars around you.

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Lidar view:

The image above shows 3-D data captured by the Lidar instrument atop a Google self-driving car, where color indicates height from the ground. Inset is the view from the car’s front-facing camera.

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LIDAR is a light-based radar. The sensor sends out short pulses of invisible laser light, and times how long it takes to see the reflection. From this you learn both the brightness of the target, and how far away it is, with good accuracy.

LIDAR’s advantages are strong:

•The result is a 3-D map of the world around you. It is trivial to isolate something from the things behind it (or in front of it.)

•LIDAR uses emitted light, so it works independent of the ambient light. Night or day, clouds or sun, shadows or sunlight, it pretty much sees the same in all conditions.

•It is robust against interference, and much higher resolution than radar.

However, there are disadvantages:

•Today it’s very expensive. High resolution LIDARS are made in small quantities and cost more than a car. Just a few years ago, a lidar sensor might have cost $70,000. Today however that price has plunged by a factor of 10 to less than $8,000.

•The resolution is pretty modest. The best units get an image only 64 pixels high, though much more horizontally, at about a 10hz rate.

•Range is limited. Typical LIDARs see well to about 70 metres, and get more limited returns from larger objects like cars to around 100m. 1.5 micron LIDARS, which are even more expensive, can see further.

•LIDARs have moving parts so they can scan the world. Flash LIDARs avoid moving parts but are currently even more expensive.

•Refresh rates tend to be slower. In addition, since LIDARs normally scan a scene, the scene is distorted by the movement of the scanning car and the movement of the objects being scanned, because one end is scanned at a different time than the other end, and everything’s moved.

•LIDARs can face trouble in heavy rain, snow and fog, though it is similar to other light based sensors including cameras. LIDARs can also sometimes see invisible things like car exhaust.

•LIDARs are better mounted outside. They need every photon so you don’t want to send them through a windshield with any tinting.

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Radar:

While LIDAR is great for accurately mapping surroundings, it’s one fatal flaw is in its ability to accurately monitor speed of surrounding vehicles in real time. This is where the four bumper-mounted radar units pick up the slack. With two sensors in the front bumper, and two in the rear, the radar units allow the car to avoid impact by sending a signal to the on-board processor to apply the brakes, or move out of the way when applicable. This technology works in conjunction with other features on the car such as inertial measurement units, gyroscopes, and a wheel encoder in order to send accurate signals to the processing unit (the brain) of the vehicle in order to better make decisions on how to avoid potential accidents. The radar system is probably paired with sonar in at least some of Google’s test cars. While radar works up to 200 meters away, sonar is only good for 6 meters. They both have a narrow field of view, so the car knows things are about to get messy if another vehicle crosses the radar and sonar beams. This signal could be used to swerve, apply the brakes, or pre-tension the seatbelts.

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Radar has some fantastic advantages. First, it sees through fog just fine when all the optical sensors fail. Secondly, it sees other cars quite well, and each radar hit returns not just a distance, but how fast the obstacle is moving, thanks to Doppler. That’s even more than what LIDAR gives — a single radar capture shows all the moving obstacles and their speeds.  Radar is able to do things like bounce off the road under a car or truck in front of you and tell you what the invisible vehicle in front of the truck is doing — that’s a neat trick. Radar today offers much less resolution. There are experimental high-resolution radars but they need lots of radio spectrum (bandwidth) — more than is allocated by the regulators for use. Radar has a hard time telling you if a target is in your lane or not, or if it’s on an overpass or on the road before you. Non-moving objects also return radar signals, but this is a problem. The ground, signs, fenceposts — they are all returning radar signals saying they are fixed objects. So while a stalled car also gives a radar return, you can’t tell it apart from a sign on the side of the road or a stalled car in the shoulder. Most automotive radars just ignore returns from fixed objects, which is one reason that for a long time, automatic cruise controls did not work in stop-and-go traffic.

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High-Powered Cameras:

The actual camera technology and setup on each driverless car varies, but one prototype uses cameras mounted to the exterior with slight separation in order to give an overlapping view of the car’s surroundings. This technology is not unlike the human eye which provides overlapping images to the brain before determining things like depth of field, peripheral movement, and dimensionality of objects. Each camera has a 50-degree field of view and is accurate to about 30 meters. The cameras themselves are quite useful, but much like everything else in the car they are redundant technology that would allow the car to work even if they were to malfunction. Camera systems follow the human model. One or more cameras view the scene, and software tries to do what humans do — intuit a 3D world from the 2D image.

•Cameras are really inexpensive. The hardware can cost just tens of dollars. You can have lots of them.

•Because the visible light cameras use reflected light, they can see an arbitrary distance in the daytime if they have a narrow field of view and can be aimed. At night they must use emitted light — like your headlights.

•They see colour. LIDARs just see a gray-scale in the infrared spectrum.

•Unless they are aimable they do not have moving parts, but if they are aimable they can gain very high resolution for more distant objects. Even in the wide field, cheap cameras with very high resolution are available — where a LIDAR might see 64 lines, a camera could see 3,000.

•Because of this high resolution, and colour, they are in theory able to understand things about the scene that can’t be learned from lower-resolution LIDAR.

•They can see traffic lights, brake lights, turn signals and other emitted light. They are superior for reading signs.

But cameras have a few downsides, the first being a deal-breaker:

•Computer vision is just not anywhere nearly good enough today to detect all important features with the reliability necessary for safe driving.

•They must deal with lighting variation. Objects are routinely subject to moving shadows and can be lit from any direction — or not lit at all.

•They need illumination at night, and headlights might not be enough.

•Computer vision takes a great deal of CPU or custom chips to get even as far as it does today.

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There is a special type of camera, known as long-wave infrared or “thermal” which uses infra-red light. Thermal images are monochrome but work equally well day or night — in fact they are better at night. They can be very good at spotting living creatures, though not when the ground is at human body temperature. Unfortunately thermal cameras are very expensive, and decent resolution is extremely expensive. They also must be mounted externally as LWIR (long wavelength infra-red) does not go through glass. At present, nobody reports using these cameras.

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Sonar:

Again, each prototype car built by Google is slightly different, but some of those tested have featured advanced sonar technology. The limitations of sonar are its narrow field of view and its relatively short effective range (about 6 meters). However, the inclusion provides yet another redundant system that allows the car to effectively cross-reference data from other systems in real time to apply the brakes, pre-tension seat belts for impact, or swerve to avoid obstacles.

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Lidar, radar, camera and sonar are integrated with car computer:

 

The whole point of having multiple systems on these cars is that they have redundancy. There are certain things that LIDAR is good at, there are certain things that cameras are good at, and there are certain things that radar is good at. If we were to take two of those systems out of the equation, you’re left with a vehicle that, while it might be able to navigate and handle basic obstacle avoidance, would leave it inherently less safe. Autonomous vehicles have a hard enough time as it is trying to predict the movements of obstacles. A deer, for example, is by its very nature unpredictable. Having headlights on gives passengers and “drivers” an opportunity to act when the vehicle is unable to accurately predict what is going to happen. Additionally, LIDAR itself isn’t perfect. It doesn’t work very well in heavy rain or snow. It needs a clear point-to-point path to objects around it to properly identify their distance, size, and shape.

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What will the most important sensors in a driverless car be?

The sensors drive what is known as the “perception” system, and that’s central to driving the car. The perception system’s job is to spot all important things on or near the road like other vehicles, pedestrians, debris and in some cases road features like signs and lane markings. (Also driven by the sensors is the localization system, whose job it is to figure out very accurately where the car is on the road.)  The perception system has to detect all the obstacles, and attempt to identify them. It needs to measure their speed and direction and predict where they are going. It’s a very challenging problem.  Two key ways the perception system can go wrong are called false negatives (blindness) and false positives (ghost objects.) A false negative is not detecting an obstacle. That can be catastrophic if it happens for long enough so that you might be unable to safely avoid hitting the obstacle. A good system will almost never get a false negative. It may occasionally take a little bit of extra time to fully understand an obstacle, and one may even blip out for brief flashes, but a persistent failure can mean a crash. Another error is a false positive. There the system sees an obstacle that isn’t really there. This will cause the vehicle to jab on the brakes or swerve. This is annoying to the occupants, possibly even injurious if they don’t have seat-belts. And it can also cause accidents if the vehicle is being followed too closely or swerves dangerously or brakes too hard. Usually these jabs end up safe, but if they are too frequent, users will give up on the system.  Related to the above is a misclassification. This can mean mistaking a cyclist for a pedestrian, or mistaking two motorcycles for a car. Even without identification, you know not to hit the obstacle, but you might incorrectly predict where it is going or how best to react to it. A different class of error is complete failure. A sensor or its software components may shut down or malfunction in an obvious way. Surprisingly, this can be tolerated more frequently than blindness, because the system will know the sensor has failed, and will not accept its data. It will either rely on redundant sensors, or quickly move to pull off the road using other sensors if that’s not enough. This can’t be too frequent or people will stop trusting the system, though.

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The human driving record is both horrible and fairly good. On average, there is an accident of any sort every 250,000 miles. Fatalities occur every 100 million miles on average, every 180 million miles on the highway. That puts the highway rate at around 3 million hours of driving between fatal accidents, though only about 6,000 hours between minor accidents. Many drivers never cause an accident in their lives, others cause several.  Not every perception blindness will cause an accident, of course. In fact, humans look away from the road very frequently for short stretches and only rarely crash from it. (Though 80% of the crashes are linked to not looking.) As such it’s hard to establish truly good metrics for reliability here. Digital systems see the world “frame by frame” though they also analyze how things change over time. If the perception system fails to see something in one frame but sees it the next, it’s almost never going to be a problem — this is akin to a human “blinking.” If a system is blind to something for an extended time, the risk of that causing a safety incident goes up. How long you have to perceive things depends on speed. If an obstacle appears ahead of you on the road at the limits of your perception range, you must be able to stop. While swerving may be a fall-back plan, you cannot always swerve, so your system must be able to stop. This means reliable detection well before the stopping distance for your speed and road condition. If the roads are wet or icy that can be fairly far. LIDAR’s big attraction is that, at least for objects of decent size like pedestrians, cars, cyclists and large animals, it’s always going to get laser returns saying something is there. The system may not be able to figure out what it is, but it will know it is there, and get more and more sure the closer you get to it. If something of size is blocking the road in front of you, you must stop, no matter what it is — though there are some exceptions like birds and blowing debris. Within a certain range of distances and sizes, LIDAR is very close to 100%, and that’s important.

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Localization:

Both cameras and LIDAR can be used for localization. LIDAR again has the advantage of being independent of external lighting and making use of full 3-D, but the challenges of doing localization with a camera are less than doing full object perception with one. The localization process starts by using tools like GPS, inertial motion detection and wheel encoders to figure out roughly where you are, and then looking at the scene and comparing it to known maps and images of the area to figure out exactly where you are. The GPS and other tools are not nearly good enough to drive (and GPS fails in many areas) but advanced localization is well up to the job.

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Sensor Fusion:

When you have more than one sensor, you want to combine all the data so you can figure out that the car you see on the radar is the same as the one you’re seeing in LIDAR or on camera. This improves the quality of your data, but can also hurt it. The fusion is not 100% reliable. What do you do if your radar suggests there is a car in front of you but the camera says not, or vice versa? You must decide which to believe. If you believe the wrong one, you might make an error. If you believe any obstacle report, you can reduce your blindness (which is very important) but you now add together the ghost obstacles of both sensors. Sometimes you get the best of both worlds and sometimes the worst.  In spite of this, because sensors all have different limitations, good sensor fusion remains a high goal of most robotics teams. Sensor fusion can be done without complicating matters if each sensor is better at a particular problem or a particular region of examination. Then you trust that sensor in its region of best operation.  (It should be noted that good fusion is done considering the raw data from all sensors; you don’t just decide that the radar data has an object and the vision data does not. Nonetheless many items will appear much more clearly in one set of sensor data than another.)

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Computer vision:

Camera processing can be divided into two rough categories known as “machine vision” and “computer vision.” Machine vision usually refers to simpler, localized analysis of digital images. This includes things like finding features and edges, detecting motion and motion parallax, and using parallax on stereo (binocular) images to estimate distance. These techniques are reasonably well established and many are well understood. Some machine vision problems are harder, but on track to solution, like detecting and reading signs.  Computer vision refers to a harder set of problems, more akin to the abilities of humans. This means things like recognizing objects. A human can be shown a picture of a human in almost any setting, any lighting, and quickly identify that it is a human, and even how far away they are. They can even discern their direction of attention and activity. A key technology at the heart of autonomous driving is computer vision. That doesn’t just mean having a lot of cameras on the car, it means having high performance and energy efficient processors that can analyze the video coming from these cameras. Sophisticated algorithms need to process the incoming information in real-time, reported to be as much as 1 gigabyte (GB) per second.

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Synopsis of environmental inputs to driverless car:

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Artificial Intelligence:

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Driverless vehicles rely on highly sophisticated artificial intelligence to integrate and analyze internal vehicle operational data and roadway sensor data and then to determine which automated controls to activate. This machine ability to control all vehicle operations distinguishes driverless vehicles from other automated technologies that either assist or warn human drivers. Driverless vehicle artificial intelligence integrates internal vehicle operational and external roadway environment inputs as described above. It is likely that driverless vehicle artificial intelligence will be functionally distributed across multiple parts of a vehicle’s decision and control systems, rather than being located in a single central processing unit. It also will be self-learning in the sense that the algorithms used in operating a vehicle modify themselves over time in response to previous operations, new information, and feedback. Self-learning algorithms are characterized by their dynamic adaptability. Rather than robotically carrying out static programming directions, driverless vehicles analyze data, model it, and make data-driven predictions and decisions, such as actuating vehicle controls.  Actuated controls simultaneously provide feedback data to various parts of the system. So far, sufficient computational power to manage driverless vehicle data integration, analysis, and activation appears to be available and at necessary analytic speed. However, capacities for rapid data fusion and control architecture are not unlimited. In particular, the computational demands of advanced security systems needed to protect driverless vehicles from external threats may drain resources and slow analytic functioning in driverless vehicles. A driverless vehicle’s artificial intelligence is tasked with performing vehicle management and guidance functions otherwise performed by a human driver. That artificial intelligence has to be at least as accurate and reliable as human intelligence engaged in the same types of operations. At present, the legal system does not specifically regulate any of the parameters in which driverless vehicle artificial intelligence will be permitted to operate. Because artificial intelligence decisions have consequences in terms of safety, economic, and environmental impacts, this aspect of driverless cars is likely to be subject to extensive legal regulation that is not yet in existence.

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The software processes all of the data in real-time as well as modelling behavioral dynamics of other drivers, pedestrians, and objects around you. While some data is hard-coded into the car, such as stopping at red lights, other responses are learned based on previous driving experiences. Every mile driven on each car is logged, and this data is processed in an attempt to find solutions to every applicable situation. The learning algorithm processes the data of not just the car you’re riding in, but that of others in order to find an appropriate response to each possible problem. Behavioral dynamics are also mapped and this data is used to help recognize situations before they happen, much like a human driver. For example, the cars are smart enough to recognize – and adapt to – situations such as:

•A slow-moving vehicle in the right line suggests a higher probability that the car following it will attempt to pass.

•A pot hole or foreign item in the street shows a higher probability of a driver swerving to avoid it.

•Congestion in the left lane means that drivers are more likely to attempt to enter the right lane.

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All the data gathered by various sensors is collated and interpreted together by the car’s CPU or in built software system to create a safe driving experience. The software has been programmed to rightly interpret common road behaviour and motorist signs. For example, if a cyclist gestures that he intends to make a manoeuvre, the driverless car interprets it correctly and slows down to allow the motorist to turn. Predetermined shape and motion descriptors are programmed into the system to help the car make intelligent decisions. For instance, if the car detects a 2 wheel object and determines the speed of the object as 10mph rather than 50 mph, the car instantly interprets that this vehicle is a bicycle and not a motorbike and behaves accordingly. Several such programs fed into the car’s central processing unit will work simultaneously, helping the car make safe and intelligent decisions on busy roads. Google engineers have programmed some real life behaviour in these cars. While the vehicle does slow down to allow other motorists to go ahead, especially in 4 way intersections, the car has also been programmed to advance ahead if it detects that the other vehicle is not moving.

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“If a ball rolls across the road then reduce the speed and watch for children that might come running after the ball.”

Programmers who develop a self-driving car do not work with some exceptionally detailed “theory of driving” which they then translate into computer algorithms. Such cars heavily rely on machine learning and pattern recognition – approaches from the field of artificial intelligence. A core problem, for example, is interpreting sensor- and image data: What kind of an object is in front of the vehicle? Is it another vehicle, a pedestrian, a bicycle, an animal? Instead of developing many rules for recognizing these objects, a sophisticated learning algorithm is fed with many images containing objects. Each image is annotated with the kind of object that it contains. Now the learning algorithm begins its work. It examines the images and guesses the kind of object in each image. Initially most of its guesses will be wrong. Therefore the algorithm modifies internal parameters or parts of its structure somewhat and tries again. This process continues, discarding changes that reduce the algorithm’s accuracy, keeping changes that increase the accuracy, until it correctly classifies all images. Afterward, when entirely new images are presented to the algorithm it will classify them with high accuracy. The algorithm has learned! Thus a driverless car may not explicitly recognize the ball but rather consider it as an unidentified object which is unexpectedly moving across the road. It does not need to increase its vigilance (which is already at 100%) to search for children which might follow the ball but it will treat this as a disturbance of the road situation by an unidentified moving object which implies an additional risk and therefore – depending on the complexity and clarity of the road situation – reduce its velocity or stop altogether. The precise form of the object is not important – if a lone skateboard were to suddenly slide across the street the car would react in the same manner. The learning approach can also be used for actions and evaluations. Instead of supplying the vehicle with a fixed evaluation scheme from which the right action for each situation can be deduced, the programmers feed the software with many traffic situations and specify the correct action for each situation. The program then searches by itself for the best configuration of internal parameters and internal decision logic which allow it to act correctly in all of these situations. Like with us humans, it then becomes difficult to answer the question why the car exhibits a specific behavior in a new situation: no “explicit rules” have been specified; the decision results from the many traffic situations to which the algorithm had been exposed beforehand. Therefore self-driving vehicles are not programmed in the classical sense; they need to learn. It is not possible to reduce human driving decisions to a few (not even very many) If-Then rules. The development of autonomous vehicles is not only a challenging software development problem. It requires an extensive learning strategy where vehicles are exposed to a huge number of traffic situations. Google, as a consequence, has driven almost two million kilometers on public roads with test drivers and has assembled an enormous fund of traffic situations from which its vehicles can learn. Another characteristic of self-driving cars is their use of probabilistic reasoning. For example, a car does not assume that it knows its exact position. Instead it maintains a distribution of positions at which it currently might be with certain probabilities and – somewhat simplifying – takes the position with the highest probability as its current position. Another unusual aspect of some algorithms used in self-driving cars is the use of randomly generated numbers in the decision-making process. As with human decision making, this means that a self-driving vehicle may behave differently when faced with exactly the same situation multiple times! To summarize, we should avoid conceptualizing self-driving vehicles as machines which are controlled by a detailed, exactly specified and in principle comprehensible software program. Instead we should conceptualize their behavior as being the result of a long and varied program of learning. The capability of such cars can be analyzed through simulation and testing but not just by examining its source code.

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In a future when cars no longer need humans to drive, choices about who might live or die in a crash are already being made — by the so-called “moral codes” that are preprogramed into a car’s neurology. Like humans, autonomous cars make countless tiny decisions while navigating the complexities of street traffic. But instead of a brain, driverless cars rely on a preprogramed set of parameters to decide whether to brake, turn or accelerate. “Suppose we have some [trouble-making] teenagers, and they see an autonomous vehicle, they drive right at it. They know the autonomous vehicle will swerve off the road and go off a cliff,” said Keith Abney, an associate professor of philosophy at California Polytechnic State University. “But should it?” While fully driverless cars are still in the research stage, several automakers such as Bosche, Tesla, Nissan Mercedes-Benz, Uber and Audi are already testing partially or completely self-driving cars on the roads. The cars of the future will face dilemmas that require split-second responses. Since they will only be able to react according to preset codes, some say experts from cognitive and behavioral sciences should be helping programmers who are outsourcing potentially murky decisions to rigid algorithms. To minimize the potential for harm, “what you want to do is think through these situations beforehand,” Abney said. “You shouldn’t be overoptimistic.” But some coders say that while these hypothetical situations are interesting, they are misleading because autonomous cars do not make judgments based on value, they make them based on protocol. While moral decisions will come into play when programmers decide how to use which algorithms, an assistant professor in computer science at Carnegie Melon University said the car itself does not have a moral agency. “The actual split-second decisions, those are not about morality. They’re following prescribed behavior,” Kolter said. Like neurotransmitters, algorithms enable cars to make calculations. The cars envision a 360-degree digital map of the environment through lasers, cameras or radars, to figure out their placement within the setting and also categorize which objects might move. Through algorithms, driverless cars use these inputs, in addition to the rate of motion and proximity to other objects, to figure out the easiest, safest trajectory. So instead of preprograming reactions to specific, dire situations, Google’s driverless car prototype, for example, is preset to recognize unfamiliar objects or situations, and most often reacts by stopping or slowing down. The life-or-death hypotheticals “are not accurate portrayals of what system needs to think about,” Kolter said. Regardless, crashes will happen and someone will have to be held accountable. Google’s autonomous car while it was on a test drive in Mountain View, California, sideswiped a public bus as it tried to merge into the bus’s lane. No one was injured in the incident.

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Deep Learning:

Much of the excitement today in computer vision is around convolutional neural networks, in particular those created with a tool named “Deep Learning” which seems to mimic many of the capabilities of biological brains. Many think this is where the breakthrough may come. Deep learning works with a large training set — and to a limited extent even can do things even without special training — to help it understand the world and even what to do. People have built robots that after being guided through terrain and training Deep Learning on the guided paths are able to learn how to move in similar environments. This is exciting work, but the extreme accuracy needed for robocars is still some distance away. Also of concern is that when Deep Learning works, we don’t strictly know why it works, just that it does. You can add training to it to fix its mistakes, but you can’t even be sure why that fixed things. The same flaw can be attributed to human brains to a degree, but humans can tell you why they acted.  There are different ways to look at this from a legal standpoint. Machine learning in general may hurt you, because you can’t understand how it works, or it may help you that you simply applied best practices with a good safety record, and no specific mistake was made that can be ruled negligent.

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Cars update themselves:

Tesla cars can update software using a simple wireless connection. And not just audio and navigation systems but Tesla regularly upload software improvements to the electrical motors and other components that can immediately improve driving performance.

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Google self-driving car:

Google self-driving car is any in a range of autonomous cars, developed by Google X as part of its project to develop technology for mainly electric cars. The software installed in Google’s cars is named Google Chauffeur. Lettering on the side of each car identifies it as a “self-driving car”.  In May 2014, Google presented a new concept for their driverless car that had neither a steering wheel nor pedals, and unveiled a fully functioning prototype in December of that year. Google plans to make these cars available to the public in 2020. The project team has equipped a number of different types of cars with the self-driving equipment, including the Toyota Prius, Audi TT, and Lexus RX450h, Google has also developed their own custom vehicle, which is assembled by Roush Enterprises and uses equipment from Bosch, ZF Lenksysteme, LG, and Continental.  Google’s robotic cars have about $150,000 in equipment including a $70,000 LIDAR system.  The range finder mounted on the top is a Velodyne 64-beam laser. This laser allows the vehicle to generate a detailed 3D map of its environment. The car then takes these generated maps and combines them with high-resolution maps of the world, producing different types of data models that allow it to drive itself.  As of June 2014, the system works with a very high definition inch-precision map of the area the vehicle is expected to use, including how high the traffic lights are; in addition to on-board systems, some computation is performed on remote computer farms.

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Here’s how Google’s cars work:

Once a driver activates the autonomous mode, the vehicle’s drive-by-wire system transfers control of the brake, gas, and steering to an on-board computer. The vehicle’s roof-mounted lidar (or light detection and ranging) unit probes 360 degrees with 64 laser beams, taking more than a million measurements per second. This data forms a high-resolution map (accurate to about 11 cm) of the car’s surroundings. Prebuilt navigation maps indicate static infrastructure, such as telephone poles, crosswalks, and traffic lights, which enables software to quickly identify moving objects, like pedestrians and cyclists. These targets are clustered together and tracked so that algorithms can process the traffic situation and plot a path safely through it. The artificial intelligence component of Google’s Level 4 autonomous cars can be considered the driver, whether or not the cars are occupied by humans, the U.S. National Highway Transportation Safety Administration said. Level 4 full self-driving automation vehicles perform all safety-critical driving functions and monitor roadway conditions for an entire trip. Google’s L4 vehicle design will do away with the steering wheel and the brake and gas pedals. Robot drivers react faster than humans, have 360-degree perception and do not get distracted, sleepy or intoxicated, the engineers argue.

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How can Google self-driving car reduce the damage and protect pedestrians?

What if cars became driverless? Vehicles not controlled by you but by computers and are in charged with getting you safely from point A to Point B. This is what Google self-driving car is all about, but what happens if there will be an unexpected accident? Google has a solution to that and it has something to do with adhesive. Passengers of the Google self-driving car has no control over it and cannot hit the brakes if there’s a pedestrian right in front of the Google self-driving car, so what does it do to reduce the damage during unavoidable accidents? Google oddly, yet cleverly, thought of a way to minimize the impact by patenting a sticky layer on the front of the Google self-driving car. Ideally, the adhesive coating on the front portion of the vehicle may be activated on contact and will be able to adhere to the pedestrian nearly instantaneously. The whole adhesive idea is that the sticky layer will carry the pedestrian safely until the Google self-driving car will go into a complete stop.

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But Google’s driverless cars still need a human driver:

The company released a report in accordance with regulations from the California Department of Motor Vehicles for autonomous vehicles. In the report, Google noted that from Sept. 24, 2014 to Nov. 30, 2015, test drivers in Google’s fleet of self-driving cars had to take control from the autonomous system 341 times. Of those disengagements, 272 were because of detected technology failures. More critically, Google reported 69 incidents where “safe operation of the vehicle” required the driver to take over. Of those, Google later determined that 13 of the incidents would have resulted in the car’s running into something if the test driver had not taken control of the vehicle. The company noted in the report that two of the incidents would have resulted in the car’s hitting traffic cones and three of the incidents were caused by other drivers. “These events are rare and our engineers carefully study these simulated contacts and refine the software to ensure the self-driving car performs safely,” the report noted. “The rate of these simulated contact disengagements is declining even as autonomous miles driven increase. Because the simulated contact events are so few in number, they do not lend themselves well to trend analysis, but, we are generally driving more autonomous miles between these events.”

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Vehicular communication systems: car to car talk:

Individual vehicles may benefit from information obtained from other vehicles in the vicinity, especially information relating to traffic congestion and safety hazards. Vehicular communication systems use vehicles and roadside units as the communicating nodes in a peer-to-peer network, providing each other with information. As a cooperative approach, vehicular communication systems can allow all cooperating vehicles to be more effective. According to a 2010 study by the National Highway Traffic Safety Administration, vehicular communication systems could help avoid up to 79 percent of all traffic accidents. In 2012, computer scientists at the University of Texas in Austin began developing smart intersections designed for autonomous cars. The intersections will have no traffic lights and no stop signs, instead using computer programs that will communicate directly with each car on the road.  Among connected cars, an unconnected one is the weakest link and will be increasingly banned from busy high-speed roads, predicted a Helsinki think tank in January 2016. Estimated 250 million cars will be able to share traffic data with each other by 2020, according to technology research firm Gartner.

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The majority of self-driving car technologies under development focus on the car as the self-contained primary technology and not on developing external infrastructure. While the vehicle might gather information from the cars surrounding it in a “connected” manner, the technology to self-drive is under development to come almost entirely from within (or on) the car. While the focus appears to be on self-contained vehicles, it is likely that the complete self-driving vehicle will include some vehicle-to-vehicle communication (V2V) and some vehicle-to-infrastructure communication (V2I). Examples of V2V might include vehicles that set speed or traveling distance based on information from surrounding vehicles, and examples of V21 might include interaction with traffic lights to manage road congestion. In February 2014, NHTSA stated that it would focus on the development of V2V communication to allow for the deployment of safety technologies that help drivers monitor other cars to prevent crashes. “Connected vehicles” would communicate with each other and their surroundings to identify the optimum route, helping to spread demand for scarce road space. Vehicles could also communicate with roadside infrastructure such as traffic lights and use this information to minimise fuel consumption and emissions. The discussion around enhanced roads lags behind that of enhanced vehicles due to cost and scalability is worth noting. From a cost perspective, the ability to attach or incorporate an apparatus into an existing vehicle that can be utilized wherever the vehicle travels, beats the necessary dual technologies that would be needed within the car and on the road for a system than relies on enhanced roads. Additionally, while road and infrastructure development in the U.S. would depend on federal, state, and sometimes local cooperation and involvement in construction, vehicle enhancements can be developed independently by manufacturers and subjected to more limited regulation. Focusing on the vehicle, a number of technological enhancements combine to make the self-driving car possible.

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At the heart of V2V communications is a basic application known as the Here I Am data message. This message can be derived using non-vehicle-based technologies such as GPS to identify location and speed of a vehicle, or vehicle-based sensor data wherein the location and speed data is derived from the vehicle’s computer and is combined with other data such as latitude, longitude, or angle to produce a richer, more detailed situational awareness of the position of other vehicles.

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V2V is car to car talk:

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Later this year, GM will begin selling cars that can “talk” to each other. Vehicle-to-vehicle communication will be featured on the 2017 model Cadillac CTS, said Allie Medack, GM’s chief of staff for global public policy. The cars will have on-board computers that send signals to other cars with similar capabilities, and share data such as location, speed, steering and braking. If the vehicles sense danger, they can warn other cars, which can then alert drivers by flashing lights on the dashboard, vibrating seats or even automatically braking. It’s a major example of what high-tech experts call the “Internet of things” — a sub-universe of data created and used by inanimate objects that exists outside the phone apps and search engines normally used by humans.

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Ideal driverless car would have convergence of sensor based and connected vehicle solutions:

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Platoon:

Grouping vehicles into platoons is a method of increasing the capacity of roads. An automated highway system is a proposed technology for doing this.  Platoons decrease the distances between cars or trucks using electronic, and possibly mechanical, coupling. This capability would allow many cars or trucks to accelerate or brake simultaneously. This system also allows for a closer headway between vehicles by eliminating reacting distance needed for human reaction. Platoon capability might require buying new vehicles, or it may be something that can be retrofitted. Drivers would probably need a special license endorsement on account of the new skills required and the added responsibility when driving in the lead. Smart cars with artificial intelligence could automatically join and leave platoons. The automated highway system is a proposal for one such system, where cars organise themselves into platoons of eight to twenty-five. Trucks can save 10.5 percent of their fuel by tailgating, which reduces air drag and resistance. New technology makes it safe for trucks to “platoon” without crashing into each other.

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German engineering:

Much of the hype about autonomous driving has, unsurprisingly, focused on Google’s self-driving project. The cars are impressive, and the company has no doubt insinuated the possibility of driverless vehicles into the imaginations of many. But for all its expertise in developing search technology and software, Google has zero experience building cars. To understand how autonomous driving is more likely to emerge, it is more instructive to see what some of the world’s most advanced automakers are working on. And few places in the world can rival the automotive expertise of Germany, where BMW, Audi, Mercedes-­Benz, and Volkswagen are all busy trying to change autonomous driving from a research effort into a viable option on their newest models. Concealed inside the BMW’s front and rear bumpers, two laser scanners and three radar sensors sweep the road before and behind for anything within about 200 meters. Embedded at the top of the windshield and rear window are cameras that track the road markings and detect road signs. Near each side mirror are wide-angle laser scanners, each with almost 180 degrees of vision, that watch the road left and right. Four ultrasonic sensors above the wheels monitor the area close to the car. Finally, a differential Global Positioning System receiver, which combines signals from ground-based stations with those from satellites, knows where the car is, to within a few centimeters of the closest lane marking. Several computers inside the car’s trunk perform split-second measurements and calculations, processing data pouring in from the sensors. Software assigns a value to each lane of the road based on the car’s speed and the behavior of nearby vehicles. Using a probabilistic technique that helps cancel out inaccuracies in sensor readings, this software decides whether to switch to another lane, to attempt to pass the car ahead, or to get out of the way of a vehicle approaching from behind. Commands are relayed to a separate computer that controls acceleration, braking, and steering. Yet another computer system monitors the behavior of everything involved with autonomous driving for signs of malfunction. Impressive though BMW’s autonomous highway driving is, it is still years away from market. To see the most advanced autonomy now available, visit another German automotive giant, Daimler, which owns Mercedes-Benz. At the company’s research and development facility, where experimental new models cruise around covered in black material to hide new designs and features from photographers, you may ride in probably the most autonomous road car on the market today: the 2014 ­Mercedes S-Class. The car can lock onto a vehicle in front and follow it along the road at a safe distance. To follow at a constant distance, the car’s computers take over not only braking and accelerating, as with conventional adaptive cruise control, but steering too. Using a stereo camera, radar, and an infrared camera, the S-Class can also spot objects on the road ahead and take control of the brakes to prevent an accident.

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Obstacles and challenges faced by driverless car:

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Any vehicle that appears on the market has to fulfil standards that concern safety, security, functionality, efficiency and robustness. Additional features, comfort, design, quality and lightweight will finally define vehicle’s market competitiveness and its price. Some of these go beyond the scope this article.  Also, for the smooth development of automated driving it is not sufficient to just push the technological progress but it is also necessary to identify all existing and imaginable risks. There are a number of challenges that have to be monitored and conquered in order to achieve given objectives. The major obstacles to autonomous vehicle deployment fall into four categories: cost, technology, consumer acceptance and policy. Since 2012, the industry has been racing forward and making remarkable and unexpected progress on the first three areas. As far as policy is concerned, it is subjected to the highly irrational elements of our legal system, politics and society. Bad policy could bring autonomous vehicles to a screeching halt, or be a drag on development. Good policy could propel the industry forward and save millions of lives (over 30,000 Americans die a year in automotive crashes). A survey by IEEE, a technical professional organization dedicated to advancing technology for humanity, of more than 200 experts in the field of autonomous vehicles found that of six possible roadblocks to the mass adoption of driverless vehicles, the three biggest obstacles are legal liability, policymakers and consumer acceptance. Cost, infrastructure and technology were seen as the least of the problems.

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Automated driving (AD) has to be smoothly incorporated in mixed traffic consisting of auto-mated and non-automated vehicles. For this, AD will face three major challenges. Firstly, it is not trivial to build the communication between automated vehicles and those vehicles that possess no modern technologies. Secondly, considering vulnerable road users into every-day traffic can be challenging, since there are sudden situations in angles that cannot be easily captured and processed by vehicle’s equipment. The third major challenge for AD in multi-modal transport is its regional dependence. If we take bikers in the Netherlands as an example, it is clear that automated driving has to even parry to regional requirements. The solution to this problem could be than easily translated beyond European markets, towards possibly more complicated traffic demands, as in other regions of the world. This will put additional requirements to the performances of sensors, for instance.

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Admittedly, Google has done a good job of developing its driverless cars. And most of the auto manufacturers already have similar development programs under way. Even Apple is said to be doing development in this area. No one wants to get left behind in case a large market develops, but it is not a sure thing. Recently released statistics show the Google car to be very safe. The record shows only 13 accidents in the past 6 years in nearly 1.7 million driving miles mostly in the Silicon Valley area. And most of these accidents were faults of the other vehicle driver. No injuries or deaths, of course. No doubt Google’s algorithms and systems are super-conservative to ensure good results and publicity.  One of the key challenges for the driverless future is to address the underlying logistics and legalities. Insurance, specifically, is an open question: When an accident involves a self-driving car, who is liable? Social acceptance is another important component: Are drivers ready to take their hands off of the wheel? Digital security is a third. Computer viruses are all too familiar, but the question is what to do if somebody “hacks” a self-driving car and changes the gas pedal into the brake, or even worse, makes the intersection go haywire.

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Challenges faced by driverless cars:

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Potential obstacles:

In spite of the various benefits to increased vehicle automation, some foreseeable challenges persist:

•Liability placed on manufacturer of device and/or software driving the vehicle.

•Time needed to turn an existing fleet of vehicles from nonautonomous to autonomous.

•Resistance by individuals to forfeit control of their cars.

•Implementation of legal framework and establishment of government regulations for self-driving cars.

•Inexperienced drivers if complex situations require manual driving.

•Loss of driving-related jobs.  Resistance from professional drivers and unions who perceive job losses.

•Loss of privacy. Sharing of information through V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols.

•Self-driving cars could potentially be loaded with explosives and used as bombs.

•Ethical problems in situations where an autonomous car’s software is forced during an unavoidable crash to choose between multiple harmful courses of action.

•Current police and other pedestrian gestures and non-verbal cues are not adapted to autonomous driving.

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Technical obstacles:

•Software reliability.

•A car’s computer could potentially be compromised, as could a communication system between cars by disrupting camera sensors, GPS jammers/spoofing.

•Susceptibility of the car’s navigation system to different types of weather.

•Autonomous cars may require very high-quality specialised maps to operate properly. Where these maps may be out of date, they would need to be able to fall back to reasonable behaviors.

•Competition for the radio spectrum desired for the car’s communication.

•Field programmability for the systems will require careful evaluation of product development and the component supply chain.

•Current road infrastructure may need changes for autonomous cars to function optimally

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Technological challenge:

Before addressing any other problems the autonomous car faces, it’s important to note that technology is still the biggest barrier separating driverless cars from the consumer market. One of these problems Google faces is the adaptability of the mapping system. The maps used by these cars aren’t like those you see in your GPS unit or on Google Maps. Each map is highly detailed down to the height of the curbs, and the dimensions of the lane the car is currently traveling in. The problem with this level of detail is the enormity of mapping the entire country – or the world. Currently, Google has mapped approximately 2,000 miles of road for the driverless car to operate on. To give you an idea of scale, there are more than 170,000 miles of road in California alone, and over 4-million miles of public road in the United States. The reason the cars have performed so well in their initial 700,000 mile test is largely due to the fact that the cars get to “cheat” in the way in which they respond to their environment. That is to say, each car isn’t making decisions in real-time on how to respond to external stimuli, and Google hasn’t tested the car’s ability to respond to situations outside of these mapped environments. Of course, this is a problem that could – at some point – correct itself to an extent, as each Google Car on the road isn’t just driving, it’s also helping to create 3D maps for other autonomous cars by charting data. Another problem with maps is that once you make them, you have to keep them up to date, a challenge Google says it hasn’t yet started working on. Considering all the traffic signals, stop signs, lane markings, and crosswalks that get added or removed every day throughout the country, keeping a gigantic database of maps current is vastly difficult. Safety is at stake here; if the car came across a traffic signal not on its map, it could potentially run a red light, simply because it wouldn’t know to look for the signal, however, that an unmapped traffic signal would be “very unlikely,” because during the “time and construction” needed to build a traffic signal, there would be adequate opportunity to add it to the map.

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The Google car will need a computer that can deal with anything the world throws at it:

The mapping system isn’t the only problem. The Google car doesn’t know much about parking: It can’t currently find a space in a supermarket lot or multilevel garage. It can’t consistently handle coned-off road construction sites, and its video cameras can sometimes be blinded by the sun when trying to detect the color of a traffic signal. Because it can’t tell the difference between a big rock and a crumbled-up piece of newspaper, it will try to drive around both if it encounters either sitting in the middle of the road. They also can’t detect uncovered manholes or potholes. The cars ‘see’ pedestrians as moving blocks of pixels and know to stop, but unlike a cautious human driver, they could not spot a traffic policeman at the side of the road, waving for traffic to stop – which could lead to trouble. Those seem like some fairly serious issues that need to be addressed before the technology could even be considered for public use. Can the car currently “see” another vehicle’s turn signals or brake lights? Can it tell the difference between the flashing lights on top of a tow truck and those on top of an ambulance? If it’s driving past a school playground, and a ball rolls out into the street, will it know to be on special alert?  Every unfinished piece of technology—every prototype, which is what the Google car is—has plenty of items to check off on its to-do list. But the biggest issue with the Google car is one that has tormented computer researchers for as long as computers have been around: how to endow the machines with the sort of everyday knowledge that humans acquire and use from childhood on. Because Google is promising the world a totally driverless car, it will need an in-vehicle computer that can deal not only with all the obvious tasks of driving but anything else the world throws at it, whether on a congested city street or a highway with an 85 mph speed limit. Computer scientists have various names for the ability to synthesize and respond to this barrage of unpredictable information: “generalized intelligence,” “situational awareness,” “everyday common sense.” It’s been the dream of artificial intelligence researchers since the advent of computers. And it remains just that. None of this reasoning will be inside computers anytime soon.

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Additional technology-related problems are:

•So far, the car has issues that would prevent it from driving in snow, ice or heavy rain.

•It’s unable to tell the color of traffic lights when sensors are blinded by sun or glare.

•Sensors detect objects as pixelated shapes, so hypothetically, the car would respond the same way – by swerving – to miss a child in the road, or a newspaper that was floating past.

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The relationship between human and robot driver could be surprisingly fraught. The problem is that it’s all too easy to lose focus, and difficult to get it back. The difficulty of re-engaging distracted drivers is an issue. In an effort to address this issue, carmakers are thinking about ways to prevent drivers from becoming too distracted, and ways to bring them back to the driving task as smoothly as possible. This may mean monitoring drivers’ attention and alerting them if they’re becoming too disengaged. The first generations [of autonomous cars] are going to require a driver to intervene at certain points. We may have this terrible irony that when the car is driving autonomously it is much safer, but because of the inability of humans to get back in the loop it may ultimately be less safe.

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Driving requires many complex social interactions — which are still tough for robots:

A far more difficult hurdle, meanwhile, is the fact that driving is an intensely social process that frequently involves intricate interactions with other drivers, cyclists, and pedestrians. In many of those situations, humans rely on generalized intelligence and common sense that robots still very much lack. Much of the testing that Google has been doing over the years has involved “training” the cars’ software to recognize various thorny situations that pop up on the roads. For example, the company says its cars can now recognize cyclists and interpret their hand signals — slowing down, say, if the cyclist intends to turn.  But there are thousands and thousands of other challenges that pop up, many of them quite subtle and unpredictable. Just imagine, for instance, that you’re a driver coming up on a crosswalk and there’s a pedestrian standing on the curb looking down at his smartphone. A human driver will use her judgment to figure out whether that person is standing in place or absent-mindedly about to cross the street while absorbed in his phone. A computer can’t (yet) make that call. Or think of all the different driving situations that involve eye contact and subtle communication, like navigating four-way intersections, or a cop waving cars around an accident scene. Fully self-driving cars will ultimately need to be adept at four key tasks: 1) understanding the environment around them; 2) understanding why the people they encounter on the road are behaving the way they are; 3) deciding how to respond (it’s tough to come up with a rule of thumb for four-way stop signs that works every single time); and 4) communicating with other people. There’s a long ways to go in all of these areas. And reliability is the biggest challenge of all. Humans aren’t perfect, but we’re amazingly good drivers when you think about it, with 100 million miles driven for every fatality. The reality is that a robot system has to perform at least at that level, and getting all these weird interactions right can make the difference between a fatality every 100 million miles and a fatality every 1 million miles. In the interim, to deal with the very toughest situations, companies (or regulators) might end up settling on a compromise: self-driving cars that hand the controls back over to humans when the computer is unsure what to do. Google’s cars are meant to be completely driverless, but more traditional car companies such as BMW or Audi are working on autonomous vehicles that can flip between computer and driver control, depending on the situation. The huge drawback to the latter approach, as plenty of analysts have noted, is that shared control could potentially make self-driving cars much more dangerous. Imagine, say, that the human inside the car has been drifting off but then suddenly has to snap to attention to prevent a crash. (This has been a growing problem in the airline industry as autopilot becomes more prevalent.) Plus, it’s a bit of a high-wire act to hand over controls on a highway when the car is going 60 mph.

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Cyber-security will likely be an issue — though a surmountable one:

How do you make sure these cars can’t be hacked? As vehicles get smarter and more connected, there are more ways to get into them and disrupt what they’re doing. This shouldn’t be impossible to fix. Software companies have been dealing with this issue for a long time.

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Data Security:

In order to ensure social acceptance of AD, any issues that concern data security and liability of produced systems and solutions, must be solved. The security of data has to be assured on a multitude of levels. Firstly, processing of a large amount of data followed by their storage and accessibility is essential if in future, steady communication between a car and its environment (other vehicles, road infrastructure, services and platforms) needs to be provided. Secondly, questions that concern data ownership, data evaluation and interpretation, or data misuse may slow down the implementation of AD significantly, if not solved properly in parallel to technology development.

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Google self-driving cars are timid:

It is cautious. It drives slowly and deliberately. In the early versions they tested on closed courses, the vehicles were programmed to be highly aggressive. Apparently during these aggression tests, which involved obstacle courses full of traffic cones and inflatable crash-test objects, there were a lot of screeching brakes and roaring engines and terrified interns.

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Is our infrastructure ready?

To function at their best, autonomous cars will communicate with each other (“you go ahead and change lanes first”) as well as with the infrastructure around them (“I am ready for a green light”). That means that we have an awful lot of work to do to prepare our roads and communities for these things, even after we’re done designing the cars themselves. These infrastructure questions are both high- and low-tech. What kind of lighting do we need on city streets if we’re trying to optimize for radar vision instead of human sight? Can a computer process a street sign that’s covered in graffiti? Will automakers want to make autonomous cars if only a few places in the country are ready for them? And if we need to invest in radically changing our roadways — networking streetlights, installing sensors — how will we pay for that? These questions about physical infrastructure don’t even scratch the surface of the legal and policy infrastructure that must come before any widespread adoption of driverless cars.

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Groups such as RUF (Denmark), BiWay (UK), ATN (New Zealand) and TriTrack (USA) are working on projects consisting of private cars that dock onto monorail tracks and are driven autonomously. As a method of automating cars without extensively modifying the cars as much as a robotic car, Automated highway systems (AHS) aims to construct lanes on highways that would be equipped with, for example, magnets to guide the vehicles. Highway computers would manage the traffic and direct the cars to avoid crashes.

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Why cities aren’t ready for the Driverless Car:

Urban planners have a lot of infrastructure work to do to make cities safe and practical for autonomous vehicles.

When it comes to adopting self-driving cars and trucks, the easiest part may well be building them. The far more difficult task will be maintaining our urban transportation infrastructures for autonomous vehicles to be functional, safe and practical.  Consider a modified  Audi NSU completed a 3,400-mile cross-country trip, driving itself 99% of the time. The 1% of roadway the car couldn’t navigate on its own: construction zones and other complicated traffic situations—hallmarks of urban traffic. What will cities have to do to get ready for the transition to the autonomous car? For starters, they will have to maintain everything from complex intersections to lane markings to the specifications expected by vehicle software designers. Without a city’s commitment to certain standards, self-driving autos might freeze in place on streets lacking clear lane markings. Similarly, unmanned vehicles might proceed at speed through an intersection where a stop sign has been removed by college students or knocked down the night before by an impaired human.

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Liability and Safety:

Level 3 and beyond automated driving offers high comfort and flexibility for the human driver allowing, for example, he or she to phone or text while the vehicle is moving. This introduces, however, concerns considering the liability of automated driving which implies the question of responsibility, particularly in accident cases. Thus, it of essential importance for insurance companies and all road users whether the responsibility lies primarily by the human who uses the car, the owner of the vehicle or the vehicle manufacturer or a vehicle supplier. For an acceptance of automated vehicles, the balance between these three potential responsibilities has to be established. Although, an alternative solution of such issue would be separating the victim in an accident from the matter of guilt, that is, indemnifying the victim independently from the question of who is guilty. The latter becomes secondary then, indicating that insurance system has to be thoroughly analyzed in order to respond to the problem of responsibility.

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Could driverless car fail?

While driverless cars have demonstrated remarkable proficiency in many aspects of driving, some contend that many driving scenarios will remain unsolved. For example, points to left hand turns into heavy traffic, adverse weather, changes to road surfaces, and eye and hand gestures as open technical questions for which solutions might be a very long way away. The technology is not robust enough. Even if theoretical solutions are found, it might be hard to reliably translate them to the real world. Robustness would depend on a myriad of electronic, mechanical and software components operating with little tolerance for error under adverse (freezing, wet, boiling) conditions, long duty cycles, less than perfect maintenance (car owners being human), fender benders, network and electrical outages, and so on. They will cost too much. The cost of sophisticated electronics, including lidar, radar, sensors, cameras, computing and networking devices, on top of the cost of additional development, maintenance, and liability, will price driverless cars out of reach of most consumers. They will not mix well with human drivers. Even if driverless cars can learn to interact with human-driven cars, human drivers will not be able to deal with driverless cars. The resulting confusion would lead to more accidents and congestion, rather than less. Regulatory and liability hurdles will long delay the technology. Legal and regulatory frameworks are all built around the assumption of human drivers. This encompasses expectations about who is liable for what and when, what components are required in cars, and so on. Sorting through the maze of necessary changes will take decades, and will offer ample points for entrenched interests to delay the approval process. Product liability might be a showstopper. They will be hacked. Beyond the unintentional vagaries of the real world, driverless cars will suffer from attacks by hackers, hooligans, hijackers, thieves and terrorists. Risk range from invasion of privacy to the spectre of driverless cars being used as precision bomb delivery vehicle. They will cause economic devastation. By some estimates, 10% of all jobs are driving related. Driverless vehicles could put millions of Americans out of work, including taxi, Uber, bus and truck drivers. And, the list of jobs at risk does not stop there. Add auto suppliers, new car dealers, collision and repair shops, tow truck operators, insurance agents, adjustors, call-center operators, ambulance drivers, emergency medical personnel, traffic police, and untold other jobs related to how cars and trucks are designed, manufactured, sold and operated today. These are serious issues and mandate that we approach the deployment of driverless cars with caution and deliberation. Some issues embody technical challenges that developers must adequately address. Policy makers and regulators will have much to say, too, about acceptable standards and levels of verification. Other issues, such as cost and user acceptance, will be decided in the market place. Driverless technology will enable tremendous business and service-level innovation. Customers, however, will have the last word on whether the products are good enough. The hardest issues will be those that require making the societal trade-offs between wonderful benefits and tremendous cost. As with every major technological transition, driverless cars will result in winners and losers. There can be no neutral decisions. Whatever decisions are made (or not made) will result in gain to some and loss to others.

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Ethics and driverless car:

Every time a car heads out onto the road, drivers are forced to make moral and ethical decisions that impact not only their safety, but also the safety of others. Does the driver go faster than the speed limit to stay with the flow of traffic? Will the driver take his/her eyes off the road for a split second to adjust the radio? Might the driver choose to speed up as he approaches a yellow light at an intersection, in order to avoid stopping short when the light turns red? All of these decisions have both a practical and moral component to them, which is why the issue of allowing driverless cars—which use a combination of sensors and pre-programmed logic to assess and react to various situations—to share the road with other vehicles, pedestrians, and cyclists, has created considerable consternation among technologists and ethicists. For example, in the event of an unavoidable crash, does the car’s programming simply choose the outcome that likely will result in the greatest potential for safety of the driver and its occupants, or does it choose an option where the least amount of harm is done to any of those involved in an accident, such as having the car hit a telephone pole with the potential to cause the driver a relatively minor injury, instead of striking a (relatively) defenseless pedestrian, bicyclist, or motorcycle rider, if the driver is less likely to be injured? The answer is not yet clear, though the moral decisions are unlikely to reside with users, given their natural propensity to protect themselves against even minor injuries, often at the expense of others.  Given the near-infinite number of potential situations that can result in an accident, it would seem resolving these issues before driverless cars hit the road en masse would be the only ethical way to proceed. Not so, say technologists, noting unresolved ethical issues have always been in play with automobiles.

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Ethical questions are one of the most important issues, being often brought in the context of AD. According to Goodall, even though AD vehicles will be safer because most of the crashes are due to human errors, it cannot be excluded that such vehicles will be involved in accidents. This creates high demands on future technologies since for vehicles on the road there are hundreds of varieties for predicting an accident. Thus, it will be expected from the vehicle itself to select a path with the lowest damage or likelihood of collision. Here come the ethics into the front row: who decides who will be killed in an accident? Whether thereby a child is killed, an old lady with a shopping caddy or a human driver suicides in order to minimize a number of victims, are just some of scenarios that represent enormous challenges influencing the acceptance of AD. Therefore, further research will be needed in order to minimize that problem. Ethics have a significant role when examining data security and even more significantly, data privacy. As expected, automated vehicles will be connected with an entire traffic infra-structure, receiving and sending an enormous amount of data. What kind of data and for which institutions will those be collected, owned, and shared, what is their purpose, how long can the data be stored and finally, how the regulatory framework complies with all these issues, are just some of ethical concerns needed to be taken seriously into account and included in early technological development steps. This problematic doesn’t only apply to the human driver who sits in the vehicle but also to pedestrians filmed with cameras which are being installed in vehicles and are a part of an infrastructure.

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The Trolley Problem:

Philosophers have been thinking about ethics for thousands of years, and we can apply that experience to robot cars. One classical dilemma, proposed by philosophers Philippa Foot and Judith Jarvis Thomson, is called the Trolley Problem: Imagine a runaway trolley (train) is about to run over and kill five people standing on the tracks. Watching the scene from the outside, you stand next to a switch that can shunt the train to a sidetrack, on which only one person stands. Should you throw the switch, killing the one person on the sidetrack (who otherwise would live if you did nothing), in order to save five others in harm’s way? A simple analysis would look only at the numbers: Of course it’s better that five persons should live than only one person, everything else being equal. But a more thoughtful response would consider other factors too, including whether there’s a moral distinction between killing and letting die: It seems worse to do something that causes someone to die (the one person on the sidetrack) than to allow someone to die (the five persons on the main track) as a result of events you did not initiate or had no responsibility for. To hammer home the point that numbers alone don’t tell the whole story, consider a common variation of the problem: Imagine that you’re again watching a runaway train about to run over five people. But you could push or drop a very large gentleman onto the tracks, whose body would derail the train in the ensuing collision, thus saving the five people farther down the track. Would you still kill one person to save five? If your conscience starts to bother you here, it may be that you recognize a moral distinction between intending someone’s death and merely foreseeing it. In the first scenario, you don’t intend for the lone person on the sidetrack to die; in fact, you hope that he escapes in time. But in the second scenario, you do intend for the large gentleman to die; you need him to be struck by the train in order for your plan to work. And intending death seems worse than just foreseeing it. This dilemma isn’t just a theoretical problem. Driverless trains today operate in many cities worldwide, including London, Paris, Tokyo, San Francisco, Chicago, New York City, and dozens more. As situational awareness improves with more advanced sensors, networking, and other technologies, a robot train might someday need to make such a decision. Human drivers may be forgiven for making an instinctive but nonetheless bad split-second decision, such as swerving into incoming traffic rather than the other way into a field. But programmers and designers of automated cars don’t have that luxury, since they do have the time to get it right and therefore bear more responsibility for bad outcomes. Autonomous cars may face similar no-win scenarios too, and we would hope their operating programs would choose the lesser evil. But it would be an unreasonable act of faith to think that programming issues will sort themselves out without a deliberate discussion about ethics, such as which choices are better or worse than others. Is it better to save an adult or child? What about saving two (or three or ten) adults versus one child? We don’t like thinking about these uncomfortable and difficult choices, but programmers may have to do exactly that. Again, ethics by numbers alone seems naïve and incomplete; rights, duties, conflicting values, and other factors often come into play.

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If you complain here that driverless cars would probably never be in the Trolley scenario—that the odds of having to make such a decision are minuscule and not worth discussing—then you’re missing the point. Programmers still will need to instruct an automated car on how to act for the entire range of foreseeable scenarios, as well as lay down guiding principles for unforeseen scenarios. So programmers will need to confront this decision, even if we human drivers never have to in the real world. And it matters to the issue of responsibility and ethics whether an act was premeditated (as in the case of programming a driverless car) or reflexively without any deliberation (as may be the case with human drivers in sudden crashes).  Anyway, there are many examples of car accidents every day that involve difficult choices, and robot cars will encounter at least those. For instance, if an animal darts in front of our moving car, we need to decide: whether it would be prudent to brake; if so, how hard to brake; whether to continue straight or swerve to the left of right; and so on. These decisions are influenced by environmental conditions (e.g., slippery road), obstacles on and off the road (e.g., other cars to the left and trees to the right), size of an obstacle (e.g., hitting a cow diminishes your survivability, compared to hitting a raccoon), second-order effects (e.g., crash with the car behind us, if we brake too hard), lives at risk in and outside the car (e.g., a baby passenger might mean the robot car should give greater weight to protecting its occupants), and so on.

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Can Self-driving cars make the right ethical judgements?

As an autonomous car drives down a street, a frail old man suddenly steps into its path from the right. Simultaneously, a child steps into its path from the left. It is too late to brake. If the car swerves to the right, the old man dies, the child lives. If it swerves to the left, the old man lives, the child dies. If it continues straight ahead both will die. What is the ethically correct decision for the car? Variations of this kind of ethical dilemmas – often referred to as the ‘trolley problem’ currently receive much attention. At first glance this seems to be a really difficult question. However, no good solutions to these dilemmas exist or can exist. Humans are not able to make a ‘right’ choice when faced with such situations either. This dilemma is a good starter for night-long discussions. None of the alternatives one comes up with is ‘ethically right’. If a human driver is in the same situation, he will necessarily make a choice but any action he chooses, is a bad action. How can we require a machine to make an ethical choice that no human is capable of making? In summary, much of the current discussion about the ethical dilemmas of life and death decisions related to self-driving cars is misplaced because it is concerned with finding right decisions where no right decisions are possible, and one must realize that self-driving cars can travel as long as they can avoid making decisions that are wrong.

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The idea that humans will act ethically and wisely while driving is an absurd and false assumption.  In 2013 over 10,000 people were killed in alcohol-impaired driving crashes, which accounts for 31% of vehicle related deaths in the U.S. So from the start we have a third of all driving deaths resulting from humans who are probably often using poor judgement, and unethical and unwise decision making. What’s more, even if self-driving cars were unethical monsters it would be a huge improvement. The CDC estimates that recognition errors were a critical reason for 41% of motor vehicle crashes. This includes driver’s inattention, internal and external distractions, and inadequate surveillance, all problems that driverless cars would avoid.

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Road testing and transport system:

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Road testing:

As of March 2016, Google had test driven their fleet of vehicles, in autonomous mode, a total of 1,498,214 mi (2,411,142 km). However, it is impossible to judge the maturity of a self-driving car by observing public demonstrations. Difficult situations don’t occur that frequently and therefore these demonstrations can only confirm that a prototype has reached quite a basic level of capability. The enormous difference in maturity between, for example, Google’s prototypes – the current leader in this technology with nearly two million kilometers of testing in autonomous mode and more than 10.000 km of testing being added every week — and the prototypes of all other developers of autonomous car technology cannot be appreciated by observing public demonstrations.

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Driverless car and transport system:

In Europe, cities in Belgium, France, Italy and the UK are planning to operate transport systems for driverless cars, and Germany, the Netherlands, and Spain have allowed testing robotic cars in traffic. In 2015, the UK Government launched public trials of the LUTZ Pathfinder driverless pod in Milton Keynes. Since Summer 2015 the French government allowed PSA Peugeot-Citroen to make trials in real conditions in the Paris area. The experiments will be extended to other French cities like Bordeaux and Strasbourg by 2016. The alliance between the French companies THALES and Valeo (provider of the first self-parking car system that equips Audi and Mercedes premi) is also testing its own driverless car system.

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Public transport:

The currently most overlooked aspect of self-driving vehicles is their effect on medium and long-distance travel in areas with sufficient population densities. Whereas today many people choose their own vehicle for distances between 100km and 500km, self-driving taxis and self-driving buses make it much easier to provide excellent, extremely cost efficient long distance mobility services.

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Traffic lights:

You’ll still need road-crossings for pedestrians and cyclists, but in a world where every vehicle is controlled by computers, algorithms should be able to feed vehicles through junctions faster. No more sitting at the lights waiting while literally nothing wants to cross your path. The need for traffic lights gradually fades away, in the same way that we no longer have inns where you can pick up fresh horses. Motorway junction design, roundabouts, urban parking spaces: all of these things could and will be profoundly changed.

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Don’t stop at the stoplight: Intersection management for driverless cars:

Driverless cars will fundamentally change mobility in more ways than we can imagine today. Researchers from the University of Texas at Austin have taken a hard look at how driverless cars could best negotiate intersections: The classic stoplight would be highly inefficient in a world comprised of only driverless cars. Therefore they have developed algorithms for managing the flow of cars at busy intersections. Cars would signal their arrival at an intersection to an intersection manager and request to pass the intersection. The intersection manager then looks for conflicts with other cars and allocates a time slot for passing the intersection at a specified speed. This approach is over a 100 times more efficient than the classic stoplight and could greatly reduce congestion, driving times, and petrol consumption in city traffic.

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Commercialization of driverless car:

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Are there driverless cars on the road right now?

There are driverless cars currently on the road. Google has some, and Chinese tech giant Baidu is testing others. Audi, Ford, and General Motors are testing out their technology, too, as are others. Tesla has a semi-autonomous system called Autopilot, which allows its Model S and Model X (and eventually the Model 3) to drive themselves on highways, and it can even park itself, drive in and out of a garage on its own, or meet you in front of your house with its summon feature. But Autopilot can’t drive itself all the time, drive on every road, or pick you up at the airport just yet. Tesla will have fully autonomous cars by 2018, which would match up pretty closely with the launch of its Model 3. But Tesla, and all other tech companies and carmakers, are at the mercy of the U.S. government’s ability to establish laws and guidelines. Autonomous car testing is limited to just a few states in the U.S. right now, and fully autonomous vehicles can’t be purchased by anyone yet. Additionally, U.S. laws require a licensed driver to be behind the wheel right now and the cars still have to have things like steering wheels (but Google’s trying to change that).

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The first combinations of advanced driver assistance features, now available in some 2016 vehicle models, offer semi-autonomous driving under specific circumstances. Cars will soon have the ability to cruise on freeways and safely navigate traffic jams with minimal driver input. As a result of increasing volumes and technology improvements, as well as cost reductions, it is now feasible to install the multiple sensors necessary for such capability. The industry consensus is that more comprehensive self-driving features will be brought to market by 2020. Such features will enable more complex automated driving, but still require some supervision by a competent driver. However, the obstacles to autonomous functionality are not all technological. While more testing is still needed to develop robustness, the biggest practical hurdles to clear before the rollout of self-driving vehicles to the public are related to liability, regulation, and legislation. In the long term, though, autonomous vehicle technology has the potential to institute major change in personal mobility, particularly in large cities. According to Navigant Research, 85 million autonomous-capable vehicles are expected to be sold annually around the world by 2035.

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Cars will drive themselves in three phases:

Truly autonomous cars won’t exist for at least 10 years, but earlier models starting next year will do some of the driving for you. That’s according to Carlos Ghosn, Chairman and CEO of auto makers Nissan and Renault, who told a Mobile World Congress audience that self-driving cars will come in three phases. Nissan is working with U.S. space agency NASA on autonomous car technology. Phase One will help you out when you probably least enjoy driving: In traffic jams. Starting next year, Nissan and Renault will make cars that can drive themselves in stop-and-go highway traffic, Ghosn said. The technology’s ready for this, so all that’s left is for governments to allow it, he said. The next phase, coming in 2018, will see cars that can drive themselves on a highway at normal speeds and can even change lanes. City driving won’t come until the third phase, in 2020. It’s a much bigger challenge because there are so many different objects around that cars have to see and respond to appropriately, Ghosn said. There are some tough decisions, too. For example, a self-driving car in the city would have to decide what to do if it’s stopped at a red light and another car comes up too fast behind it: Go through the intersection or get hit from behind? A car with no driver is an even steeper technical challenge and also raises the issue of cyber-security. Regulators will be much more reluctant to allow driverless cars than vehicles that do the work but have a human behind the wheel, Ghosn said. Like any technology, self-driving features will start out expensive and gradually get cheaper. Each phase of self-driving technology will first come available in high-end vehicles and then work its way down to mass-market models, Ghosn said.

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If fully autonomous cars become commercially available they have the potential to be a disruptive innovation with major implications for society. The likelihood of widespread adoption is still unclear, but if they are used on a wide scale policy makers face a number of unresolved questions about their effects.  One fundamental question is about their effect on travel behaviour. Some people believe that they will increase car ownership and car use because it will become easier to use them and they will ultimately be more useful. This may in turn encourage urban sprawl and ultimately total private vehicle use. Others argue that it will be easier to share cars and that this will thus discourage outright ownership and decrease total usage, and make cars more efficient forms of transportation in relation to the present situation.

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Mercedes-Benz:

The Mercedes-Benz F 015 Luxury in Motion:

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Mercedes-Benz has been testing driverless vehicles on public roads in California since September 2014; in October, it started testing at Concord Naval Weapons Station, northeast of San Francisco. It’s also been testing in Germany. In fact, back in August 2013 the Mercedes-Benz S 500 Intelligent Drive drove fully autonomously 100 kilometers — a little more than 62 miles — between the German cities of Mannheim and Pforzheim. But the most recent accomplishment for Mercedes-Benz was revealing the F015 Luxury in Motion driverless car in January at the 2015 International Consumer Electronics Show, or CES, in Las Vegas. “The car is growing beyond its role as a mere means of transport and will ultimately become a mobile living space,” Dieter Zetsche, CEO of Daimler AG, the manufacturer of Mercedes-Benz cars said. The F015 concept car offers a variable-seating system with four rotating lounge chairs that allow a face-to-face seat configuration. It also continuously exchanges information between the vehicle, passenger and outside world. There are no plans to bring the F015 to market. Rather, the purpose of the vehicle is to show ideas and future technologies that could find their way into the company’s series models, a company spokesman said.

The Car as Conference Room:

Once cars become fully autonomous, they won’t need to take the form they have for more than a century. One concept design is the Mercedes-Benz F 015, which transforms the vehicle into a “digital living space.” Inside, seats swivel to face one another, and a series of displays permit passengers to entertain themselves or work. In other words, cars could double as conference rooms—and employers may begin to demand that people use their commutes productively. The F 015 design is sleek and beautiful—it looks like a silver bullet—but style may become passé in future cars.

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Audi:

Audi got its start in the autonomous-vehicle industry with the DARPA Grand Challenges in 2004, when 15 autonomous vehicles, including an Audi, attempted to navigate a desert track outside in Barstow, California. None of those cars finished, says Bradley Stertz, corporate communications manager for Audi. In fact, the farthest any of them got was 7.3 miles, he notes. Contrast that with January 2015, when Audi used its piloted driving system in an A7 Sportback to autonomously drive more than 560 miles on a highway from Palo Alto, California, to Las Vegas. Audi’s concept car accelerates and brakes independently and initiates lane changes and passing manoeuvres automatically. When the system reaches its limits and the driver needs to take control — in city environments, for example — multiple warning systems are activated simultaneously to notify the driver. Stertz says a fully automated vehicle with no driver is still 20 or 30 years away. “To have the car understand every single possibility is a massive challenge,” he says. However, certain autonomous features will be available in stages. Audi’s traffic-jam technology, where the car takes over in traffic moving less than 37 mph, could be available in two or three years, Stertz says. Along with traffic management and increasing transportation availability, Stertz says Audi, like other companies, sees accident prevention as a major benefit of self-driving vehicles.

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China:

In the race for driverless car technology, Chinese companies are taking big strides competing with the likes of Google and Tesla.  With the Beijing Motor Show under way, the days when the country’s domestic car firms was brushed off as mere copycats are well and truly over. And a lot of this year’s buzz is around driverless cars in particular.  In past years, innovation might have come from Silicon valley, but Chinese companies are pushing ahead. There is a lot more going on in China than many in the West have realised.  China-based Internet search giant Baidu is working to bring self-driving public transportation to China by 2018 and the company is taking huge strides to make it happen.  Baidu’s autonomous vehicle has already driven in Beijing, merged into highway traffic all on its own, and successfully passed other vehicles. Let’s put that in perspective for a moment, because you may not know exactly how hard that is to do in Beijing. New York City welcomes about 2.7 million vehicles on its roads every day, and it’s one of the most congested cities in America. Now consider that Beijing has two times more vehicles than NYC. But it’s not the technical wizardry that might propel China’s self-driving cars ahead of the U.S. — it’s the lack of red tape.

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India:

Tata group is working on India’s first indigenous driverless car and ultra-hi-tech cars for future. Senior company officials say that the pro-type is planned to be rolled out for testing in two years. Several companies of Tata Sons are contributing to the research and development (R&D) of the ambitious projects, with Tata Elxsi leading it, said sources close to the project. According to information available, the proposed driverless car is expected to have 12 cameras for roaming guidance. It may be equipped with 5 to 6 laser sensors. For future hi-tech cars, Tata Elxsi is conducting research on communications systems, particularly communication between two cars and communications between car and smart infrastructure. This communication system will also be exploited to alert car before any collision with any object. Cars could be controlled through mobile phones, tables and other wearable electronic devices. In addition, these cars will be developed in a way that preventive maintenance can be done from a remote location. Technology of cloud computing will be utilized at various stages to control car and hi-tech future cars.

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Autonomous car forecasts:

This paragraph lists the most recent predictions about when driverless cars will be available on the market:

1. Ford autonomous vehicle on the market by 2020

2. Baidu’s Chief Scientist expects large number of self-driving cars on the road by 2019 in china

3. First autonomous Toyota to be available in 2020

4. First fully autonomous Tesla to be available by 2018, approved by 2021

5. Driverless cars will be in use all over the world by 2025

6. Uber fleet to be driverless by 2030

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Will it take decades until most of the vehicles on the road are capable of autonomous driving?

It took automotive innovations such as anti-lock braking, airbags, seat-belts several decades from being introduced in cars of the premium segment until they trickled down to all models and until most of the cars on the roads were equipped with these innovations. Many people assume that autonomous driving will also take this long. But there are good reasons why the diffusion process for self-driving cars (though not for driver assistance systems) will adopt a different pattern:

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The slow diffusion of classical automotive innovations results from the limited additional benefit of these technologies. It is difficult for a buyer to quantify the benefits of anti-lock braking or airbags in dollar terms and to determine whether they exceed their often initially hefty price. Severe accidents – in which these technologies would make a difference – are rare and therefore it may be a rational decision for many, not to purchase the new technology. The higher the buyer’s wealth and the higher the buyer values his life (both of which are correlated), the higher the likelihood that someone will adopt such an innovation. This is why these safety-related innovations are so strongly tied to the premium segment in the beginning. As the technology is adopted more and more, their costs gradually sink which means that the cost-benefit calculation turns positive for more and more prospective buyers.

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But increased safety is not the only key benefit of autonomous vehicles: Self-driving cars unleash the driver from the steering wheel and thereby increase available time – a precious and scarce resource. The benefits are most obvious in logistics, where the costs for a truck driver represent about a third of the total transport costs. Fully autonomous technology dramatically increases the return on investment and will therefore lead to rapid adoption. Consumers also value their discretionary time. If an average driver spends about 1 hour per day behind the wheel this translates to a 15 full days of additional time gained each year! Someone who values their own time at only $5 ($10) per hour, values the time-gaining benefit of this technology at $1,825 ($3,650) per year. If the buyer intends to keep the vehicle for 5 years, then he would be willing to pay $9,125 ($18,250) for this feature. Given these benefits, the group of early adopters would be large and not at all limited to the buyers of premium cars. The more time people spend in their cars, the higher the incentive to purchase a fully autonomous vehicle. In contrast to the increase in safety associated with many classical automobile innovations, which can only be expressed as a reduction in the probability of an accident and associated damages, the benefit of additional discretionary time is a solid fact which the buyer can be certain to experience every time he uses the car.

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Another solid benefit of self-driving vehicles is lower insurance premiums. Because self-driving cars will be much safer, the premiums will shrink. However it may take a few years for this effect to kick in. Insurance companies first need to be able to quantify the risk associated with fully autonomous vehicles. Young drivers – who pay the highest premiums today – will benefit most from the lower premiums. Understanding the direction that the technology is taking and being concerned about the risks of manual driving, many parents will decide not to spend the money for driving lessons and guide their children toward fully autonomous driving instead.

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Fully autonomous vehicles can be used as self-driving taxis. In sufficiently populous areas, fleets of autonomous cars will emerge that provide mobility as a service. These cars can provide mobility at much lower cost than privately owned vehicles because their utilization rate will be much higher than the dismal utilization rate of approximately 5 percent for privately owned vehicles. Because of higher utilization rates, these fleets are much less sensitive towards the cost of autonomous technology and can adopt the technology much earlier than privately owned vehicles. In cities many privately owned cars will be displaced by such fleets. Several simulation studies have shown that each self-driving taxi may replace 6 to 10 privately owned vehicles. Because such fleets exhibit network effects and tend to become monopolies, there will be an intense fight for market leadership. This will accelerate the diffusion of self-driving taxis and will rapidly increase the number of miles travelled in autonomous cars.

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Besides individuals and fleet operators, governments play a key role to ensure that this technology will be adopted with unusual speed: Self-driving cars are a critical, enabling technology which will impact almost every sector of industry. New business models become possible; in addition, transportation costs for people and goods will shrink considerably. Countries which are slow to adopt this technology may lose their competitive positions; early adopters may significantly improve their competitive advantage. Therefore many governments will work hard to ensure that they are well positioned with respect to this technology. Such patterns are already visible in the United States, where the competition between states for leadership in autonomous car technology has been in full swing since 2012 when Nevada became the first state to pass an autonomous vehicle law. After intense lobbying California followed suit; many other states have since worked on autonomous vehicle legislation – not all successfully. Michigan is an example for a state that is very concerned about losing its position as heart of the US auto industry to California and therefore has also passed a law for autonomous vehicles alongside with additional measures aimed to increase Michigan’s competitive position in this emerging technology. In Europe, the United Kingdom has recognized the technology’s potential and is investing hundreds of millions of pounds to grow an autonomous mobility industry. The UK has not signed the Vienna Convention on Road Vehicles and therefore can more easily introduce autonomous cars on their roads. The first such projects – most notably in Milton Keynes were 40 autonomous pods will ferry passengers from the train station to the city center, are being implemented. In Asia, Singapore looks very actively to improve local mobility through driverless car technology. It is only a question of time until China recognizes the potential of this technology for reducing congestion and pollution in their cities via autonomous vehicle fleets as well as reducing the size of required investments in their road infrastructure – much of which is still being built – and makes this technology a top national priority.

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MaaS:

Mobility-as-a-Service (MaaS) describes a shift away from personally owned modes of transportation and towards mobility solutions that are consumed as a service. This is enabled by combining transportation services from public and private transportation providers through a unified gateway that creates and manages the trip, which users can pay for with a single account. Users can pay per trip or a monthly fee for a limited distance. The key concept behind MaaS is to offer the travellers with goods mobility solutions based on the travel needs. MaaS is not limited to individual mobility; the approach can be applied to movement of goods, as well – particularly in urban areas. This shift is fuelled by a myriad of innovative new mobility service providers such as ride-sharing and e-hailing services, bike-sharing programs, and car-sharing services as well as on-demand “pop-up” bus services. On the other hand, the trend is motivated by the anticipation of self-driving cars, which put in question the economic benefit of owning a personal car over using on-demand car services, which are widely expected to become significantly more affordable when cars can drive autonomously.

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Driverless Cars and Car Sharing Services:

Today, cars are used less than 5% of the time. Shared vehicles would vastly increase this: Google suggests to as much as 75% of the time. This means that less vehicles will be needed, with some experts estimating as much as a 90% reduction. This would greatly reduce the cost of mobility, and eliminate much of the need for parking spaces, freeing up valuable space for other, more attractive uses. In the US, for example, it is reckoned that there are nearly four parking spaces per vehicle. The savings would not end there. With human drivers being initially segregated on highways, and eventually banned from public roads, traffic management would become massively more efficient. Driverless cars would travel much closer together in ‘platoons’ in synchronised fashion, interacting seamlessly via vehicle-to-vehicle communications and road sensors. The result is that road space would be used far more intensively and congestion reduced and potentially eliminated. One study suggested that road capacity would be at least doubled. Faster journeys would therefore free up huge amounts of time. Drivers in England spend an average of 4½ hours a week driving. Imagine if this were cut in half, for extra work, leisure or sleep. This would come on top of the time that former drivers would gain as passengers in transit, using their vehicles as a mobile lounge or office. Driverless cars could significantly reduce the cost of car-sharing services like Uber and Lyft by eliminating the largest operational cost—the human driver. As Travis Kalanick has said: The reason Uber could be expensive is because you’re not just paying for the car — you’re paying for the other dude in the car. When there’s no other dude in the car, the cost of taking an Uber anywhere becomes cheaper than owning a vehicle. Several studies, including one by Larry Burns and William Jordon and another by Rutt Bridges, estimate that driverless taxis could operate at as little as 20% of the cost of individual car ownership. Emilio Frazzoli, Rick Zhang and their colleagues at MIT and Stanford have shown that driverless cars could further reduce cost of car sharing services by enabling intelligent coordination to minimize congestion, keep the driverless fleet in balance and better serve anticipated demand.  These advantages leave ample room for driverless-car-enabled business models that give significant savings to passengers (over individual ownership and driving) while enabling hefty profits to service providers. Thus, both Uber and Lyft openly talk about a day when they shift from being intermediaries for individually-owned-and-human-driven cars to providing mobility services with company-owned driverless cars. Aggressive adoption by mobility services would jumpstart and accelerates the spread of driverless cars. Such services would be able to pay far more for driverless cars than what consumers might otherwise be willing. Some industry insiders estimate that mobility service business models could sustain driverless cars costing as much as $250,000-300,000. Consumer adoption would be minimal at such price points but mobility services could buy millions of them. The study by Burns and Jordon, for example, estimated that 25,000 driverless cars would be needed to serve a small city like Ann Arbor, MI. For reference, according to the U.S. Census Bureau, there are 228 cities in the U.S. larger than Ann Arbor. If a proportion of automated cars are in nearly constant use and can come when called, the need for individual ownership and parking reduces significantly. It will also free up the time required for driving and parking the vehicle, hence enhancing convenience, relaxed travel, shorter travel time and improved safety. Eran Ben-Joseph, a professor at M.I.T., points out that “in some U.S. cities, parking lots cover more than a third of the land area, becoming the single most salient landscape feature of our built environment.”  Other estimates say there are as many as 2 billion parking spaces in the US, an area about the size of Connecticut and Vermont combined. This real estate could be put to more productive use, if there was no need to park 250 million vehicles.

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The Virtuous Cycle between Driverless Cars, Electric Vehicles and Car-Sharing Services:

At first glance, the three are distinct business categories with unique technical, strategic and market challenges. A virtuous cycle, however, links them.

Each has the potential to reinforce the others and, together, massively disrupt the technology and business of personal mobility. Ultimately, such reinforcement might mean the difference between lacklustre adoption and breakout success. Electric vehicles are generally cheaper to build, maintain and operate, so mobility services would have a natural preference for them. If such services could coordinate across an entire fleet, they could sidestep typical consumer concerns about electric cars’ battery range and charging station availability. That is because most taxi rides are well within current electric vehicle ranges. Cars could be dispatched according to customer destination, battery life, recharging needs and so on. Since most trips involve only one or two passengers, mobility services could further reduce car and energy cost by assembling fleets of mostly smaller cars. The potentially virtuous interaction between mobility services and electric vehicles does not exist for driver-owned car-sharing cars, however. Individual drivers are incentivised to optimize their own range and flexibility, and therefore avoid electric vehicles.

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Industries that will be transformed by Driverless Cars:

1. Fast food.

Believe it or not, 70 percent of sales at McDonald’s come from drive-thru customers. When people enter their destination into a driverless car and press “go,” they’ll be less likely to change course mid-route to grab fast food. Why? When it’s just as convenient to go anywhere for food as it is to go to McDonald’s or Burger King, people will likely choose fast food less. On top of this change, fast food locations near gas stations are also likely to attract fewer customers, as driverless cars will probably refuel when they’re not transporting passengers.

2. Media and entertainment.

Freeing up people from operating motor vehicles will present consumers with new blocks of time to read the news or enjoy entertainment. This will create opportunities for broadcasters to send video content to screens inside driverless cars and for advertisers to serve location-specific ads about products and services passengers will be near on their trip.

3. Hotels.

Hotels that derive a significant amount of business from single-night customers during road trips are set to lose a lot of business. Why? It’s likely that many travellers will simply decide to sleep in their cars rather pay for an overnight stay. To be sure, it may take 20 years or more for this to become commonplace, but the roadside motel seems like a less viable business proposition as driverless cars take over.

4. Real estate.

When commuting substantial distances to work in a car becomes less of an inconvenience, property values will likely shift. Instead of the highest values concentrated in urban areas, home values will likely spread out more evenly across cities and into suburban areas. Parking garages and other spaces built around human drivers may also be converted to serve other purposes, as autonomous driving technology gradually reshapes city planning.

5. Airlines.

Though most people prefer flying to driving due to the quicker travel time, shorter flights will likely see a drop in customers. The convenience and lower cost of sitting in a driverless car will begin to appeal more to people who don’t want to go through the hassle of waiting in line at the airport, going through security, and paying for ground transportation once they’ve arrived at their destination.

It’s unclear which of these industries will be hit the hardest–or soonest–by driverless cars, but if your business is directly tied to providing a service that might become less convenient as drivers adapt to autonomous vehicles, it’s probably time to start thinking about how to adapt your model.

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Transforming cities and make cities more sociable places:

Driverless cars will radically alter the geography of cities. With streets largely cleared of parked cars, journeys might be less confined to the traditional hub-and-spoke highway networks, allowing more flexible decisions on both home and work locations. In principle, the reduced pain and cost of commuting is likely to prompt some people to commute longer distances. However, since this might threaten the economic benefits of driverless cars, some form of road charging may help to discourage this. This would be easy to implement, since shared vehicle journey charges would likely be at least partly distance-based. Taxing usage would also provide governments with an income stream to replace the revenues lost on parking fees and speeding fines. Another convenience of driverless cars is that they may also offer passenger-less services such as deliveries. This could complement the drone delivery services already envisaged by Amazon. Just imagine if the driverless car that you ordered to take you to a party could pick up your shopping and gifts – or even your kids – on its way? Sharing rather than owning vehicles will also allow users more choice. Commuting to work on your own (as most people do)? Then order a single person vehicle, a ‘personal pod’. Taking the kids to the beach? Then order a large people carrier with cinema equipment to keep them entertained en route. Driverless cars promise to make cities more sociable places. Reduced travel times would free up more time for leisure. Enhanced mobility for non-drivers such young, elderly or disabled people would boost their social lives. Freed from driving, vehicle occupants would be free to interact with each other and, via mobile technology, with their friends elsewhere. Moreover, particularly since driverless cars are likely to be electric, or hydrogen, powered, they will offer enormous environmental benefits to cities. According to some estimates, autonomous vehicles could reduce air pollution by an average 90%. This in turn will add to the benefits to city dwellers’ health and longevity. Aside from reduced pollution, the sharp reduction in of the number of road accidents, over 90% of which are down to human error, will address one of the prime sources of injury and death. According to the World Health Organisation, 1.3 million people are killed on the roads every year. Road traffic injuries are currently estimated to be the ninth leading cause of death across all age groups globally, and are predicted to become the seventh leading cause of death by 2030. The reduction in accidents would cut the need for heavy safety protection, adding further to the tendency for driverless cars to be smaller and lighter. Combined with the dramatic efficiency gains in usage, energy and resource utilisation would plunge, reinforcing the environmental benefits.  So the economic, environmental and social impact of driverless cars promises to be transformational. In economic terms, they represent a massive productivity boost. Output will benefit from quicker journeys and the increased scope for working while travelling. The reduced cost of travelling, insurance, repair and the freeing up road and parking space will increase the spending power of both consumers and businesses. The reshaping of the urban infrastructure will also entail substantial new investment. The near-gridlocked cities in emerging economies such as India and China stand to make huge gains in both mobility and activity.

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Policies, legislation, laws and blame-game vis-à-vis driverless car:

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Policy:

The need of the hour is to bring private, public, and civil society stakeholders together to guide the policymaking process on the broad range of public interests, beyond simply safety. Any such conversation should be based on the following principles as envisaged by Americans:

1. The environmental impact of driverless cars should be positive, or at least be neutral. The effect of any increase in vehicle miles travelled (VMT) from changes in behavior should be offset by incentives for low-carbon fuel sources in these vehicles.

2. Autonomous technologies in cars should increase affordability and create value for people who otherwise are underserved by transit alternatives. Our goal must be to reduce the cost per mile driven, while increasing convenience, benefitting all.

3. Any policy should be a way to reduce how much we spend on transportation infrastructure. All levels of government in the United States spent $416 billion on public infrastructure in 2014. Shared autonomous vehicles can help us get more out of existing infrastructure by increasing our roads’ carrying capacity.

4. Driverless cars should make our communities safer. About 30,000 Americans die annually in accidents involving automobiles, the vast majority of these caused by driver error. Research suggests that autonomous vehicles will improve safety for the occupant of the car, as well as everyone on our roadways. The impact is even more profound abroad.

5. Driverless cars must reward us with two things that Americans value: their time and their property. Americans collectively spent 6.9 billion hours stuck in rush-hour traffic in 2014. Driverless cars and new forms of sharing could alleviate this congestion, and the resulting time suck. Using cars more efficiently will enable us to regain prime real estate, with parking lots currently covering as much a third of the land in urban areas.

As policymakers across America evaluate requests to enable autonomous technology or implement pilot programs, American people need to create a broad and engaged public conversation that accounts for its potential public benefits. That conversation should be based on the principles of improving emissions, affordability, lower public spending, greater safety, and consumer benefits. With a shared vision for the future of transport, our country can stay at the forefront of one of the greatest opportunities of the 21st century.

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Legislation:

In the United States, state vehicle codes generally do not envisage — but do not necessarily prohibit — highly automated vehicles. To clarify the legal status of and otherwise regulate such vehicles, several states have enacted or are considering specific laws. As of the end of 2013, four U.S. states, (Nevada, Florida, California, and Michigan), along with the District of Columbia, have successfully enacted laws addressing autonomous vehicles. The National Highway Transportation and Safety Administration told Google that the artificial intelligence system that controls its self-driving car can be considered a driver under federal law. The legal interpretation by federal regulators was made in response to a petition from Chris Urmson, the director of Google’s self-driving car project.

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Germany creates laws for driverless cars:

The German transport minister will be laying down legal guidelines for the use of driverless cars on the country’s autobahns. Alexander Dobrindt said driverless or robot cars would probably become a feature on German roads within a few years, but insisted that some rules needed to be in place first. He has created a committee including figures from research, industry and politics, to draw up a legal framework that would make it permissible and would like a draft of key points to be ready before the Frankfurt car fair in September. Current rules do not allow self-drive or robot cars on German roads, because a human being always has to be at the controls, according to the 1968 Vienna Convention on Road Traffic to which Germany is signed up, along with 72 other countries. Questions to be clarified include who would be responsible when the car’s computer fails causing an accident, how is a robot car is to be insured and how licences should be regulated?

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Laws regarding driverless cars:

Driverless cars will be required to obey all road traffic laws and the Highway Code and are expected to substantially reduce collisions, deaths and injuries. Driverless vehicles will bring many advantages over conventional vehicles. Enhanced safety, mobility, convenience, and environmental benefits are among these improvements. At the same time, driverless vehicles also will present challenges to the legal system. Although the current legal environment could probably accommodate driverless vehicles with relatively few alterations, changes in the legal system will be required with regard to such matters as insurance and regulatory requirements.  Existing legal rules regulate how motor vehicles are designed, manufactured, sold, repaired, and used. Laws also establish how liability should be imposed for injuries caused by motor vehicles; the sorts of misconduct that will be punished as criminal; as well as the nature of insurable risks with regard to driverless vehicles. The legal system also establishes appropriate uses of land for roads, highways, and other transportation infrastructure as well as how that infrastructure will be financed. This existing, historically determined, legal architecture will remain for a time. But the legal system will gradually adapt to how driverless vehicles operate. Driverless vehicle technologies appear to be transforming much more rapidly than the legal system, which tends to evolve slowly, to apply past precedents, and to modify those precedents only cautiously. The legal response to driverless vehicles has already begun with basic measures, such as laws that simply authorize the use of these vehicles in some states. More complex and far-reaching legal changes will evolve over time. As driverless vehicles grow more sophisticated and common, they will assuredly generate many novel issues of law. Initially, the legal rules devised for driverless vehicles likely will be shaped by analogies to conventional vehicles. Over time, however, policymakers will come to better appreciate, and begin to focus on, the unique capacities of, and challenges presented by, driverless vehicles and the system that supports them. There is also a substantial likelihood that driverless vehicles will produce some far-reaching changes in the law. Just as railroads provided the catalyst for new legal doctrines in the 19th century, the advent of driverless vehicles may produce substantial changes in the prevailing legal culture in the 21st century. Legal rules governing the artificial intelligence that operates driverless vehicles is an example of a novel area of law that will develop with regard to driverless vehicles. Once established within the law pertaining to driverless vehicles, these new rules may be extended to other settings and technology applications, until the rules become generally accepted legal principles. Overall, however, forecasts regarding the “likely” or optimal legal policy responses to driverless vehicles should be made only tentatively, and with deep appreciation of their inherent limits. About the only certainty associated with the legal environment for driverless vehicles is that these devices will challenge the ingenuity of federal, state, and local policymakers alike as they merge onto the nation’s roads.

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Operator Responsibility:

These legal questions are sure to be influenced by the level of automation under consideration. For instance, in those where the driver remains substantially in control of the vehicle, it is less likely that the new legal precedent will be created. Current precedent may even apply to models in which the driver receives warnings and is expected to take over if the SDV system needs to disengage, although it is questionable whether a human “fail safe” can be reasonably expected. Full automation, however, creates the potential for operators who are not physically able to drive. In those cases responsibility, negligence, and liability may be less clear.

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Data and Privacy:

A lot of data is generated in our 21st century interconnected, internet-enabled, media- and information-rich lives. A key business and policy consideration worldwide concerns “big data” or very large data sets generated by the content and information shared by the use of technology in a broad range of industries. SDVs will likely generate a great deal of data on operators’ travel habits, including information on GPS location, speed, traffic, weather conditions, and road conditions, as well as information about other road users around the operator. How to protect or use that data is an open question being debated.  As policymakers consider how to include privacy protections for the data requirements of SDVs, they will need to weigh the costs of protection on manufacturers and business owners against the benefits to operators or individuals. Policymakers might consider avenues such as setting limits on secondary uses of SDV data or setting time limits for the retention of that data. Until policymakers act, industry means of privacy protection and information will be used. A possible model could be the provision of privacy policies with opt-in mechanisms or information for consumers on how data will be gathered and used.

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Who takes blame for driverless car accident?

Who is responsible in the eyes of the law for accidents caused by driverless cars?

Is it the car’s owner, its manufacturer or the software maker?

Who would be taken to court if charges were brought?

And whose insurance company would have to pay for the damage?

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Most modern cars have some technology that operates without human intervention, from air bags and anti-lock brakes to cruise control, collision avoidance and even self-parking. But very few cars have full autonomy in the sense that they make their own decisions. A human driver is usually still in control – although this assumption is increasingly difficult to maintain as advanced driver assistance technologies, such as electronic stability controls, enable drivers to retain control of the vehicle when otherwise they might not. As things stand, the law still focuses specific car regulations on human drivers. The international Vienna Convention on Road Traffic gives responsibility for the car to the driver, saying “every driver shall at all times be able to control his vehicle”. Drivers also have to have the physical and mental ability to control the car and reasonable knowledge and skill to prevent the car harming others. Similarly, in UK law the person using the car is generally liable for its actions. But following an accident, legal liability can still depend on whether a collision is due to the negligence of the human driver or a defect in the car. And sometimes, it could be due to both. For example, it may be reasonable to expect a driver to take due care and look out for potential hazards before engaging a self-parking function. Driverless car technologies come with a warning that they are not insulated from software or design faults. But manufacturers can still be held liable for negligence if there is evidence that an accident was caused by a product defect.  However, while proving this for components such as windscreen wipers or locks isn’t too hard, it is more complicated to show software components are defective and, more importantly, that this has led to injury or harm. Establishing liability can also be difficult if there is evidence the driver has interfered with the software or overridden a driver assistance functionality. This is particularly problematic where advanced technologies enable driving to effectively be shared between the car and the driver. Product manufacturers also have specific defences, such as the limits of scientific knowledge preventing them from discovering the defect. When it comes to the driver’s responsibility, current law requires drivers to take the same amount of care no matter how technologically advanced the car is or their level of familiarity with that technology. Drivers are expected to demonstrate reasonable levels of competence and if they fail to monitor the car or create a foreseeable risk of damage or harm they are in breach of their duty of care. This implies that without a change in the law, self-driving cars won’t allow us to take our eyes off the roads or take a nap at the wheel. The current law means that if a self-driving car crashes then responsibility lies with the person that was negligent, whether that’s the driver for not taking due care or the manufacturer for producing a faulty product. It makes sense for the driver to still be held responsible when you consider that autonomous software has to follow a set of rational rules and still isn’t as good as humans at dealing with the unexpected. In the case of the Google crash, the car assumed that the bus driver was rational and would give way. A human would (or should) know that this won’t always be the case.

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Solution to clearly identifying the Primary Cause of a Self-Driving Car Accident:

One potential solution to clearly identifying the cause for a self-driving car’s future accident is to continuously and thoroughly record the vehicle’s operating performance data prior to the accident.  That way when an accident does occur all the operating data and control actions will be recorded-saved and available to help identify the primary cause.  If another vehicle was involved the sensor/camera would record its unsafe entry into the self-driving car’s path or the other vehicle colliding with the self-driving car before the control system could feasibly react.  If the accident was caused by the self-driving computer-control system malfunction then the operating alarm/error data would be recorded.  And, if the accident was caused by the self-driving car’s backup driver’s failure to take over manual operation as needed, this data would also be recorded. The required technology to record all the self-driving computer-electronic operating data already exists.  It would involve installing an on-board data recorder device or technology similar to a ‘black box’ to fully record all critical operating data prior to any and during all potential accidents.  The data could then be accessed following a given accident-incident to help determine its primary cause. The procedure would be pretty straight forward: following an accident the Police would pull the car’s black box, send it to their Station’s Incident Analyst and the Analyst would then determine the primary cause for the accident.  Proper-legal actions would then likely follow.  Hopefully the new self-driving technology has developed to the level that avoids chronic/repeat failures or malfunctions and the backup driver has been trained to take over manual operation when and as needed. Another advantage of recording all operating data for say up to a year is that any random or newly developing self-driving control system problem can be recorded and made available to shop Mechanics during routine annual vehicle maintenance/servicing.  The potential electronic control problem could then be identified and corrected to prevent a future incident.

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Driverless car insurance and repair:

Observers believe driverless cars will make automobile transportation a whole lot safer, and McKinsey predicts they could reduce US auto accidents by 90 per cent. While this might save insurers money on payouts in the near future, demand for insurance will ultimately decrease as risks of a car crash drop. In anticipation of this shift, some insurers are rolling out usage-based insurance policies (UBIs), which charge consumers based on how many miles they drive, and how safe their driving habits are. Basically, there are two kinds of liability systems. In some states liability is based on the no-fault concept, where insurers pay the injured party regardless of fault, and in others it is based on the tort system. Will the auto insurance system change to be more uniform with the arrival of self-driving vehicles and will the federal government play a larger role?  Some aspects of insurance will be impacted as autonomous cars become the norm. There will still be a need for liability coverage, but over time the coverage could change, as suggested by the 2014 RAND study on autonomous vehicles, as manufacturers and suppliers and possibly even municipalities are called upon to take responsibility for what went wrong. RAND says that product liability might incorporate the concept of cost benefit analysis to mitigate the cost to manufacturers of claims. Coverage for physical damage due to a crash and for losses not caused by crashes but by wind, floods and other natural elements and by theft (comprehensive coverage) is less likely to change but may become cheaper if the potentially higher costs to repair or replace damaged vehicles is more than offset by the lower accident frequency rate. The number of vehicle-related workers compensation claims, now responsible for a large but decreasing portion of claim costs according to the National Council on Compensation Insurance, should continue to drop as will the share of healthcare and disability insurance costs related to auto accidents. Automobile manufacturers and software companies, more specifically Google, have created driverless cars, which will be available to the public for use on roads at some time in the future. This new technology comes as both good and bad news for the insurance industry. From underwriting to insurance regulation, the effects of self-driving cars will be far reaching.  Here are just a few of the insurance implications that could come with owning a driverless car:

1. Underwriting:

Initially, many of the traditional underwriting criteria, such as the number and kind of accidents an applicant has had, the miles he or she expects to drive and where the car is garaged, will still apply, but the make, model and style of car may assume a greater importance. The implications of where a car is garaged and driven might be different if there are areas set aside, such as dedicated lanes, for automated driving. During the transition to wholly autonomous driving, insurers may try to rely more on telematics devices, known as “black boxes,” that monitor driver activity. Some drivers may object to them based on concerns about privacy. Usage-based insurance policies, which depend on data about the driver’s behavior submitted by an electronic device in the driver’s car, have attracted a smaller than expected percentage of the driving population, possibly because people do not want to be monitored. According to the National Association of Insurance Commissioners, use of telematics is forecast to grow to up to 20 percent within the next five years. With driverless cars, insurers will need to consider their effects on underwriting, pricing and risks to be insured. Presumably, driverless cars are safer than traditional ones. However, there’s not enough data to prove this theory. And even if accident frequency drops, this doesn’t mean that claim severity will drop as well. It could just be the opposite since these driverless cars will contain embedded technology, causing the cost of repair to be higher. There is also the issue of potential product defect that could lead to serious accidents. Insurance actuaries and product developers will therefore need to evaluate all the data and forecasts available before even considering to develop new rating models and products for AVs.

2. Technology:

AV technology could change liability schemes and risks assumed by insurers when insuring vehicles, component manufacturers and related automotive businesses. Technology also brings with it cyber risk issues. The technology used in driverless cars can collect data about the vehicle’s owner and its operations. This can lead to unauthorized access and use of this data. A hacker can even go as far as remotely controlling the car.

3. Regulation:

If and when AVs are approved for use on public roads, automobile insurance statutes and regulations will likely be revisited, and insurance companies will need to respond to them. While it’s going to be some time before you’ll see any driverless cars on your city streets, this new technology will definitely be an insurance game changer.

4. Liability:

As cars are become increasingly automated the onus might be on the manufacturer to prove it was not responsible for what happened in the event of a crash. The liability issue may evolve so that lawsuit concerns do not drive manufacturers and their suppliers out of business. RAND has suggested some kind of no-fault auto insurance system. Others foresee something akin to the National Childhood Vaccine Injury Act, a no-fault compensation program for vaccine recipients who suffer a serious adverse reaction when vaccinated. The legislation was passed in 1986 in response to the threat that life-saving vaccines might become scarce or even unavailable if manufacturers, overwhelmed by claims of injury, scaled back or terminated production.

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A special insurance policy designed for autonomous and partly automated vehicles:

One of the biggest mysteries we’ll need to solve for driverless cars is how to assign liability in an accident. Some experts think car manufacturers will start taking on more responsibility, but many insurance companies are still loath to touch the subject because of the various state laws that risk complicating the picture. But at least one insurer seems to sense an opportunity where others fear to tread. In what appears to be an unprecedented move, a British insurance company has begun offering a special policy designed for autonomous and partly automated vehicles. In theory, you could use this on your Google driverless car or your Tesla that’s equipped with autopilot. Unfortunately, it’s only available in Britain. But the policy protects against all of the usual things you would find in your typical car insurance — damage, fire, theft. And it also goes further, covering accidents caused by malfunctions in the car’s driverless systems even if the passenger has failed to use a manual override. It covers any havoc that hackers may wreak on a car’s operating system. It applies to cars even if they haven’t been updated to the latest software. And it even covers mishaps that may occur if your car loses satellite or other crucial connectivity. By setting out such a surprisingly comprehensive plan this early in the game, analysts say, the policy from U.K.-based insurer Adrian Flux sets a precedent that others may follow. Insurance is designed as a hedge against disaster; when disasters become rare, the cost of insurance will fall. “We expect premiums for fully autonomous cars to be considerably cheaper than regular cars, purely because of the expected reduction in accidents and claims,” said Matt Ware, a spokesman for Adrian Flux. Even though that means less revenue for insurers, the lower rate of accidents means the insurance company will get to save money overall because it won’t be forced to issue as many payouts.

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Car repairs and repair costs:

Fewer accidents will also mean fewer trips to the body shop. On top of that, mechanics’ traditional expertise will become less valuable as cars become more connected and software-dependent. This information could give drivers more transparency into the repairs they actually need, and allow them to calibrate preventive maintenance and avoid more expensive repairs down the line. For instance, the startup Zubie offers real-time diagnostics to owners of connected cars, allowing people to know what’s wrong with their engine before they bring their car in for inspection. While the number of accidents is expected to drop significantly as more crash avoidance features are incorporated into vehicles, the cost of replacing damaged parts is likely to increase because of the complexity of the components. It is not yet clear whether the reduction in the frequency of crashes will lead to a reduction in the cost of crashes overall. Automobile ownership appears to be on the decline, and more people in urban areas are opting for public transportation and shared rides. Some people wonder whether when all vehicles are self-driving anyone will actually own a car. Cars may belong to a company, municipality or other group and may be parked away from the center of the community in a location from which they can be summoned by phone. A study by the University of Texas at Austin of how the advent of autonomous cars may change vehicle owners found that each shared autonomous vehicle (SAV) replaced about 11 conventional vehicles. The study assumed that only 5 percent of trips would be made by SAVs.

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Will drivers need any sort of training?

Driver’s education classes have been a staple of teenage life, as have the nerve-wracking driver’s tests that follow. Would drivers need new training before getting behind the wheel of a self-driving vehicle? If training became a requirement, would it be the responsibility of the the government, the manufacturer, the dealer, or some other entity? The answer to this question probably won’t be clear until the technology is further along and officials have an understanding of just how intuitive fully-automated vehicles will or won’t be. NHTSA, for its part, recommends states develop a special license or endorsement based on some sort of prerequisite like a test or certification from a manufacturer of autonomous vehicle systems. States will also have to decide whether people with disabilities that preclude them from driving traditional vehicles would be eligible for an autonomous vehicle instead.

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Professional drivers:

Driverless automobiles will reduce demand for truckers, taxi drivers, and other driving professionals. Instead, telematics technology — the use of telecommunications to facilitate communication and gather data from vehicles — will allow taxi and trucking companies to manage their self-driving fleets so that they provide services and run their routes with optimal efficiency. Humans will still be needed to manage these systems. Already, driverless trucks are being used to move iron ore at mines in Australia, and the Canadian energy company Suncor Energy is working to automate its own trucks in a process its CFO estimates will take 800 human drivers off its drilling site.

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Ideal car:

In my view an ideal car should have following characteristics:

1. By its design it is less accident prone and saves life.

2. It saves time in driving & parking, and helps reduce traffic congestion.

3. It is cheap and affordable.

4. It saves fuel, reduces emission and reduces pollution.

5. It can park itself.

6. No problem in night driving.

7. No problem in driving in rain, snow or bad weather.

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Is driverless car an ideal car?

Let us judge.

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Accident reduction and lifesaving:

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The figure below shows role of human errors in car accidents:

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In spite of stronger cars, extra seat belts, and air bags, 1.3 million people are killed on the world’s roads every year; 33,000 year in the US every year. Each year in the U.S. there are six million car crashes, resulting in nearly $160 billion in expenses. These accidents are the number one cause of death between the ages of four and 34, and 93% of them are due simply to human error. You either need to make a better driver, or take the driver and human error out of the equation all together. NHTSA’s 2008 Crash Causation survey found that close to 90% of crashes are caused by driver mistakes (NHTSA, 2008). These mistakes, which include distractions, excessive speed, disobedience of traffic rules or norms, and misjudgment of road conditions, are factors within control of the driver (WHO, 2014). Volvo refers to these driver mistakes the 4Ds: distraction, drowsiness, drunkenness, and driver error (Bilger, 2013). The leading perspective is that, because SDVs would not be vulnerable to these weaknesses, they could reduce or eliminate human error in the driving process and work towards preventing the 1.25 million deaths globally and 34,000 deaths in the U.S. from car accidents (WHO, 2014).

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The leading cause of most automobile accidents today is driver error. Alcohol, drugs, speeding, aggressive driving, over-compensation, inexperience, slow reaction time, inattentiveness, and ignoring road conditions are all contributing factors. Given some 40 percent of accidents can be traced to the abuse of drugs and or alcohol, self-driving cars would practically eliminate those accidents altogether. The Eno study found if ten percent of all cars were self-driving, as many as 211,000 accidents would be prevented annually. Some 1,100 lives would be preserved, and the economic costs of automobile accidents would be reduced by more than $20 billion. If 90 percent of cars were self-driving, the numbers are even more sobering. The Eno Center’s study found up to 4.2 million accidents would be prevented; 21,700 lives would be preserved and more than $400 billion in related costs would be eliminated. This is all great news for pretty much everybody—except of course your local body shops, emergency services companies, and morticians. Those businesses would see a considerable drop-off in revenues.

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According to the National Highway Traffic Safety Administration, 40% of fatal crashes are the result of alcohol or drug use, driver distraction and/or fatigue. Since computers don’t drink or do drugs and they don’t become distracted or tired, their use in vehicles would dramatically reduce fatalities.  Through vehicle-to-vehicle and vehicle-to-infrastructure communication, autonomous cars and trucks could reduce freeway and artery congestion by more than 75%. For one, traffic congestion would also be dramatically reduced through fewer accidents. The Federal Highway Administration estimates that up to 25% of traffic jams are caused by accidents.  Additionally, autonomous vehicles are expected to be able to detect other vehicles around them – even communicating with other cars and trucks about their routes. Therefore, self-driving vehicles could anticipate a lead vehicle’s braking and acceleration decisions, allowing for smoother travel and leading to the avoidance of the traffic pileups that occur today. Driverless cars will prevent 95% of all traffic collisions – but experts say the technology won’t be perfected until 2050.

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A recent study by the Rand Corp. calculated that establishing to a statistical near-certainty that driverless cars would reduce vehicular fatalities by even 20% would require 5 billion miles of road testing — a record that would take a fleet of 100 test vehicles operating at an average of 25 miles per hour, 24 hours a day and 365 days a year, 225 years to complete. That’s because Americans drive 3 trillion miles a year and fatalities are relatively rare — the 32,800 deaths annually on U.S. roads amount to only 1.09 per 100 million miles. It’s unclear whether the public will demand such precise proof of safety before accepting driverless cars on the road, says Nidhi Kalra, the co-author of the Rand report. “Before now, new vehicle technologies have just been allowed to go on the road because the driver is still ultimately in control,” Kalra says. “The public may not be comfortable following the same path for autonomous vehicles.”

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Crashes of Google self-driving car:

On February 14, 2016 a Google self-driving car attempted to avoid sandbags blocking its path. During the manoeuvre it struck a bus. Google addressed the crash, saying “In this case, we clearly bear some responsibility, because if our car hadn’t moved there wouldn’t have been a collision.”  Some incomplete video footage of the crash is available.  Google characterized the crash as a misunderstanding and a learning experience. The company also stated “This type of misunderstanding happens between human drivers on the road every day.”  As of July 2015, Google’s 23 self-driving cars have been involved in 14 minor collisions on public roads, but Google maintains that in all cases the vehicle itself was not at fault because the cars were either being manually driven or the driver of another vehicle was at fault. In June 2015, Google founder Sergey Brin confirmed that there had been 12 collisions as of that date, eight of which involved being rear-ended at a stop sign or traffic light, two in which the vehicle was side-swiped by another driver, one of which involved another driver rolling through a stop sign, and one where a Google employee was manually driving the car. In July 2015, three Google employees suffered minor injuries when the self-driving car they were riding in was rear-ended by a car whose driver failed to brake at a traffic light. This was the first time that a self-driving car collision resulted in injuries.  Additionally, Google maintains monthly reports that include any traffic incidents that their self-driving cars have been involved in.  Google is required by the Californian DMV to report the number of incidents during testing where the human driver took control. Some of these incidents are not reported by Google when simulations indicate the car should have coped on its own. There is some controversy concerning this distinction between driver-initiated disengagements that Google reports and those that it does not report. Turns out, though, their accident rates are twice as high as for regular cars, according to a study (vide infra) by the University of Michigan’s Transportation Research Institute in Ann Arbor, Michigan. Driverless vehicles have never been at fault, the study found: They’re usually hit from behind in slow-speed crashes by inattentive or aggressive humans unaccustomed to machine motorists that always follow the rules and proceed with caution. It’s a dilemma that needs to be addressed. It’s similar to the thorny ethical issues driverless car creators are wrestling with over how to program them to make life-or-death decisions in an accident. For example, should an autonomous vehicle sacrifice its occupant by swerving off a cliff to avoid killing a school bus full of children?  Computers are learning, the programmers are learning and the people are learning to get used to these things.

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Google’s announced that the self-driving cars have now logged some 300,000 miles and “there hasn’t been a single accident under computer control.” This technology is still at its very early stages and 300,000 miles is not all that big of a sample. According to a “cursory” analysis by Bryant Walker Smith of Stanford Law School, “Google’s cars would need to drive themselves (by themselves) more than 725,000 representative miles without incident for us to say with 99 percent confidence that they crash less frequently than conventional cars. If we look only at fatal crashes, this minimum skyrockets to 300 million miles.” We’re still a long way away from there.

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Autonomous Vehicles crash more, but injuries are less serious, according to a 2015 study released from the University of Michigan’s Transportation Research Institute with the caveat of small sample size. The report pulled together all the publicly available data on the crashes that self-driving cars have been in and compared it with crash statistics involving conventional vehicles from the National Highway Traffic Safety Administration. After adjusting for under-reporting of accidents, researchers Brandon Schoettle and Michael Sivak found that:

1. Autonomous Vehicles (AVs) got into more crashes overall: 9.1 crashes per million miles driven, compared with 4.1 crashes per million miles for conventional vehicles.

2. AVs had a higher rate of injury per crash: 0.36 injuries per crash, compared with 0.25 for conventional vehicles.

3. AVs weren’t responsible for any of the crashes they were involved in.

4. Most AV crashes were low-speed, and the ones involving injury were minor compared with the injuries sustained during conventional vehicle crashes.

Compared with conventional vehicles, AVs mostly got into low-speed rear-ending crashes, as opposed to the more violent, higher-speed, head-on and T-bone crashes that cause worse injuries and fatalities. The AVs also didn’t hit pedestrians or bicyclists in any of the incidents, something that happens regularly with human-driven vehicles.

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Better time utilization:

If you take the average commute time in America, which is about 50 minutes, you multiply that by the 120 million workers they have, that turns out to be about 6 billion minutes wasted in commuting every day. Now, that’s a big number, so let’s put it in perspective. You take that six billion minutes, and you divide it by the average life expectancy of a person, that turns out to be 162 lifetimes spent every day, wasted, just getting from A to B.  The average driver in England spends 235 hours driving every year. That is the equivalent of six working weeks. Despite the increasing sophistication of modern vehicles, and greater application of driver assistance technologies, the driver must still concentrate on driving 100% of the time. Highly and fully automated vehicles will change this. For the first time since the invention of motor vehicles, the ‘driver’ will be able to choose whether they want to be in control, or to hand the task of driving over to the vehicle itself. This represents a major opportunity – allowing drivers to safely use the journey time however they wish, from reading a book, to surfing the web, watching a film or just chatting face to face with other passengers.

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Commutes would become quicker and less stressful:

For many Americans, commuting is the most miserable thing they do every day. Research shows that long commutes increase the risk of obesity, divorce, stress, sleeplessness, and neck pain.  Self-driving cars could ease the burden somewhat. Commutes would be quicker, as cars driven by robots could travel at steadier speeds and avoid traffic jams. What’s more, commuters could kick back and read a book or nap rather than stressing out behind the wheel.

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Traffic Congestion:

How do you reduce traffic congestion driving regular cars?

One of the leading causes of traffic jams is selfish behavior among drivers. It has been shown when drivers space out and allow each other to move freely between lanes on the highway, traffic continues to flow smoothly, regardless of the number of cars on the road. In fact, we have the capability of pretty much eliminating traffic jams right now. All we’d have to do is allow three to four car lengths of space between our car and the car in front of us, even in slow-moving traffic. The way we drive now, when traffic gets heavy, if someone needs to change lanes to exit the freeway, or if someone needs to enter the freeway, everybody has to stop to let it happen because we drive packed so tightly together. And, there’s no other way to say it, we do this out of selfishness. Every time we have ever proffered this theory to a group of drivers, their first response is if we drove that way, everybody would get in front of us. This, of course is exactly the idea, if we allowed cars to get in front of us and freely change lanes, traffic would continue to flow. When you don’t allow cars to get in front of you traffic has to stop, your frustration level increases, and you become determined to let even fewer cars get in front of you—thus exacerbating the problem.

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How do you reduce traffic congestion using driverless cars?

Take an intersection controlled by a traffic light , when the traffic light turns from red to green, it takes 1 second to 3 seconds for human drivers to perceive the change of the signal and react accordingly by switching pedals. For a driverless car, it only takes 0.3 seconds to do so. Driverless cars use sensors and cameras to detect the traffic lights.  They can communicate with the local controller that operates the traffic light. For advanced driverless cars, called connected vehicles, they can talk to each other exchanging information of location, speed and other parameters. Imagine if all the cars at the signalized intersection were driverless cars: They would all start at the same time without the human response delays. So the road can serve a larger amount of traffic in the same amount of time. In addition, driverless cars are equipped with collision avoidance technology, so if a regular motorist violated the red light, the driverless car would not enter the intersection and it would avoid the collision. If all vehicles are driverless cars, capacity of the road will be doubled. This means that every city intersection would have twice as many lanes at no cost to the city.  If driverless cars become dominant in the market and if they are designed to drive very closely to each other, then they will reduce traffic congestion, possibly by a lot.

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Automating driving would bring with it the benefit of decreased traffic. As connected vehicles and driverless vehicles communicate with each other and their surroundings, they are able to identify the optimum route, which helps spread demand for scarce road space. Separate vehicles move together as a unit, reducing unnecessary accelerating and braking which are often the cause of traffic congestion. And as automated vehicles decrease the number of accidents, traffic will be increasingly lighter since accidents are one of the biggest causes for congestion. Less traffic will also improve people’s health, as traffic jams have been shown to cause a rise in blood pressure, depression and anxiety, as well as a decrease in cardiovascular fitness and quality sleep.

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Some of the possible methods/reasons to reduce traffic congestion are listed below:

1. The headway (distance) between the cars can be significantly reduced, hence allowing high density along with high speed traveling down the highway and communicating with one another at regularly spaced intervals. More cars could be on the highway simultaneously because they would need to occupy less space on the highway. The results of a Columbia University study showed highway capacity, measured in vehicles per hour per lane, could be increased to nearly 12,000, given a scenario in which 100 percent of the cars on the highway were self-driving and communicating with one another at 75 mph. This compares to about 3,000 human-operated vehicles per hour per lane. This would happen because the safe vehicle distance could shrink to about 16 feet for self-driving cars going 75 mph, compared to the over 115 feet necessary for safe stopping by human-operated cars at the same speed. With human drivers, it is not possible because humans are required to maintain a certain distance based on their speed and reaction time. You won’t feel comfortable to drive at 100 km/h with only 1 meter distance between vehicles.

2. The speed limits can be increased (to design speed without safety margin) as the driver-less vehicles’ reaction time would be very little as compare to humans.

3. Vehicle-to-Vehicle (V2V), Infrastructure-to-Vehicle and Vehicle-to-Infrastructure communication technology along with driver-less technology can reduce delays at signalized intersections both by reducing the “waves” and also the imperfect signal cycle timing (extra red or extra green or yellow time).

4. Furthermore, driver-less vehicles along with V2V can “work together” and aim for System Optimal routes which will decrease the total travel time of the system as a whole. But, humans would still want their driver-less vehicles to select routes which will minimize their individual travel time.

5. One of the best ways to reduce traffic congestion is to provide other transportation alternatives and move people out of personal vehicles. If driver-less cars allow for people to more comfortably share cars, or if they allow for lanes to be reserved for public transit alternatives, traffic congestion could be reduced for reasons other than driver-less car efficiency themselves.

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Driverless cars react faster and therefore require shorter safety distances. This increases the number of cars that can drive on a given road. A group of researchers from Columbia University have calculated the potential capacity increases and have shown that autonomous cars could greatly increase highway capacity. If cars are able communicate with each other and negotiate their speed and safety distance, highway capacity could increase by up to a factor of 4! This could translate into great savings for infrastructure expenditures. Annual spending for highway infrastructure alone in the United States amounts to approximately 150 billion U$! Great savings could also be realized in developing countries with fast-growing road networks.

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How driverless cars may increase traffic congestion?

Research shows that at least for some time driverless cars could actually make things worse on the roads. A new simulation-based study of driverless cars questions how well these two big secondary benefits—less traffic and more comfort—can coexist. Trains are conducive to productivity in large part because they aren’t as jerky as cars. But if driverless cars mimic the acceleration and deceleration of trains, speeding up and slowing down more smoothly for the rider’s sake, they might sacrifice much of their ability to relieve traffic in the process. “Acceleration has big impacts on congestion at intersections because it describes how quickly a vehicle begins to move,” Scott Le Vine of Imperial College London, who led the research. “Think about being stuck behind an 18-wheeler when the light turns green. It accelerates very slowly, which means that you’re delayed much more than if you were behind a car that accelerated quickly.” For their study, Le Vine and colleagues simulated traffic at a basic four-way urban intersection where 25 percent of the vehicles were driverless and the rest were standard. In some scenarios, the driverless cars accelerated and decelerated the way that light rail trains do—more comfortable than, say, riding in a taxi, but still a little jerky at times. In other scenarios, the cars started and stopped with the premium smoothness of high-speed rail. Within these broad scenarios the researchers also tested alternatives that reduced speeds but improved smoothness, such as longer yellow lights or following distances. All told they modelled 16 scenarios against a baseline with all human-driven cars. The researchers then ran each simulation for an hour, repeated it 100 times, and calculated the average impact that scenario had in terms of traffic delay and road capacity. In every single test scenario, driverless cars designed to create a comfortable, rail-style ride made congestion worse than it would have been in a baseline scenario with people behind every wheel.

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Increasing the capacity of the road has been shown in most cases to be an ineffective means of addressing congestion. Typically, when the capacity of a road is increased through additional traffic lanes, congestion is eased only briefly. This is because easing congestion reduces travel time, which results in increased demand for travel and so demand goes up until the road is congested again. Also, all things being equal, driverless vehicles will increase the demand for travel. They will open up the road to people who previously couldn’t operate a vehicle: children, individuals with visual impairments, and many seniors and others with health issues that currently restrict their ability to operate a vehicle.  Any increase in efficiency or capacity will be exceeded by the increase in demand. A study by MacKenzie and other researchers published in the journal Transportation Research: Part A estimates that the vehicles can cut the cost of travel by as much as 80 percent. That in turn drives up miles travelled by 60 percent. “You are talking about a technology that promises to make travel safer, cheaper, more convenient. And when you do that, you’d better expect people are going to do more of it,” MacKenzie said.

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Improved mobility:

Improved Mobility for Children, the Elderly, and the Disabled:

If you’re blind, can’t walk, or you’re older and your reflexes and your memory have you concerned about driving, self-driving cars can ease the burden of transportation for you considerably. Suddenly blind people can get to work just as easily as sighted people, enabling them to be more productive in the work force. Children of aging parents are freed from the worry of taking off work to make sure parents get to doctor’s appointments because the car can be programmed to do it on its own. Programming the car to pick up people, drive them to their destination and then park by themselves, will change the lives of the elderly and disabled by providing them with critical mobility. Further, parents won’t have to worry about getting their kids to school in the morning, picking them up in the afternoon, driving them to soccer practice, or to the movies on the weekend—the car will be capable of doing those things all on its own. And don’t worry; with parental safeguards in place, the car can be programmed to only go to specific destinations.

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Driverless cars could make car-sharing more popular.

By some estimates, most privately owned cars are currently parked 90 percent of the time. That’s a huge waste — we spend a lot of money for cars that sit in the parking lot most of the time. In theory, self-driving cars could be used more productively — say, through car-sharing services. That might mean fewer cars overall.

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Cost and Economy of driverless cars:

Cost of car:

New technology costs big bucks. Google’s driverless test cars have about $150,000 in equipment including a $70,000 lidar (laser radar) system which makes it too expensive for consumers. But reasonably priced “lidar” systems are coming relatively soon. Lidar is the beam technology that amounts to the car’s eyes on the road. On-board driving assist technologies, like smart cruise control and 360-degree cameras, are becoming more widely available and cheaper, so these will likely lead the way toward driverless cars in the near future.

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Self-Driving cars will save you Money:

Early adopters are going to face a high price tag for fully autonomous vehicles, which will command a $10,000 premium at least the first few years, according a recent Boston Consulting Group analysis. That’s not pocket change, but it’s an amount that could be recouped by consumers in just a few years thanks to the reduction in ownership costs associated with the cars. Here are four ways that owning a self-driving car will save you money.

1. Your fuel efficiency will go up. Driverless cars will communicate with each other, eliminating the needs for slowing down and stopping at every intersection. On highways, driverless cars will “platoon,” driving more quickly and more closely together to eliminate wind resistance.

2. Insurance premiums will go down. Insurance start up MetroMile estimates that self-driving cars could save consumers an average $1,000 per year, given their near perfect safety record. If self-driving cars really do live up to the hype and eliminate collision-related crashes, then drivers will only have to pay for insurance for things like break-ins and acts of god.

3. You won’t get any more tickets. Say goodbye to speeding tickets, texting fines, or parking violations. With a driverless car, you’ll never have to worry about having to pay for subpar driving or worrying about traffic laws, since the car itself will be making such decisions for you. The reduction in tickets is expected to be so great that local governments are concerned about the potential drop in their revenue. “The hundreds of millions of dollars generated from poor driving-related behaviors provide significant funding for transportation, infrastructure… and many other public services,” according to a recent report by the Brookings Institute.

4. You may not need more than one car per family. The majority of families own multiple cars, but they don’t use them at the same time. A driverless car with a “return to home” mode would eliminate much of the need for the second car, since the first car could simply drive itself home to take the next family member where he or she needs to go. That’s the finding of a report by Brandon Schoettle and Michael Sivak at the University of Michigan Transportation Research Institute. They found the average household’s car ownership rate would likely fall from its current 2.1 to 1.2, following the widespread adoption of self-driving cars. In the 15 percent of incidences when family needs overlap, the wheel-less family member could always call a driverless Uber.

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It will be many decades before driverless cars are the norm. Driverless cars are likely to cost up to $7,000 more than normal vehicles when they reach mass acceptance but face a big challenge because programmers need to factor in the regular minor rule-breaking of humans driving traditional vehicles on the same roads, a United States expert says. Professor Rajkumar from Carnegie Mellon University in Pittsburgh says it will be decades before there is full automation and he expects when the self-driving car technology reaches the mass market and is available in family sedans and sports utility vehicles, the extra technology will cost between $4,000 to $7,000 per vehicle. Professor Rajkumar, an expert in computer engineering and robotics, is one of the main speakers at an International Driverless Cars Conference, and told media there will be major challenges for programmers of the new vehicles because of the human factor in manually-driven cars where people aren’t religiously obeying road rules.

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Driverless cars could bring billions of dollars in benefits. A recent study conducted by Columbia University said that replacing New York’s fleet of 13,000 yellow cabs with 9,000 driverless cars could cut costs per mile by nearly 88% and wait times down from 5 minutes to just 36 seconds during rush hour. But that’s just for cabs in New York. What about for the US as a whole?  Using data from Morgan Stanley, researchers found that autonomous cars could save the US $1.3 Trillion Dollars a year.  Daniel Fagnant and Kara Kockelman of the University of Texas tried to tally up many of these myriad benefits in a paper for the Eno Center for Transportation. Their rough estimate? If 10 percent of the vehicles on the road were self-driving cars, the country could save more than $37 billion a year due to fewer deaths, less fuel, more free time, etc. If we reached a point where self-driving vehicles constituted 90 percent of the cars on the road, the benefits would rise to some $447.1 billion a year. Widespread embrace of self-driving vehicles could eliminate 90% of all auto accidents in the U.S., prevent up to $190 billion in damages and health-costs annually and save thousands of lives, according to a new report by consulting firm McKinsey & Co. The study, compiled after interviews with dozens of industry officials, predicts mass adoption of auto-piloted vehicles beginning in about 15 years and initial implementations early next decade.

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Energy/fuel:

Lower Fuel Consumption:

With car-to-car communications systems enabled, self-driving cars would know what the cars in front of them were going to do before they did it. If one car needed to brake, all of the cars following it could do so simultaneously and in the process ease the ripple effect traveling through a line of cars. When the situation was cleared, all of the cars could then accelerate smoothly and gently back up to speed. It has been proven hard braking and so-called jackrabbit starts waste fuel. Self-driving cars could be programmed to eliminate this style of driving altogether. Further, with all cars linked together traveling the same speed, there would be not speeding up and slowing down to accommodate drivers of different skill-levels and attention spans. It has been shown the more your car operates at a continuous engine speed, the less fuel it consumes. Additionally, with cars traveling together in packs, all at the same identical speed, drafting would come into play—improving aerodynamics considerably. This would result in a decrease in the amount of fuel required. The Rocky Mountain Institute estimates that the reduction in wind drag alone from vehicles traveling closely together could reduce fuel use 20 percent to 30 percent.  “Platooning” would contribute to greater fuel economy due to reduced air resistance as well as reduced congestion.  What’s more, cars would no longer need to be so big and hulking — since their users don’t need to fear crashing as much. That all boosts fuel efficiency.  Driverless cars will waste less fuel on things like looking for parking.  One MIT study found that in congested urban areas, drivers waste an absurd amount of gasoline circling the blocks looking for parking. Intelligent self-driving cars could presumably improve on this — particularly if they could communicate with each other. One more thing, eliminating traffic congestion is also estimated to have the potential to save some 2.9 billion gallons of fuel annually.

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Wadud and colleagues at the University of Washington and Oak Ridge National Laboratory published a study that looked at the energy efficiency pros and cons of automated and driverless vehicles. The study identified these energy-saving benefits of self-driving cars, depending on how many people use them:

•More efficient computer-directed driving styles (0 percent to 20 percent reduction in energy use)

•Improved traffic flow and reduced jams because of coordination between vehicles (0 percent to 4 percent reduction)

•”Platooning” of automated vehicles driving very close together to create aerodynamic savings (4 percent to 25 percent reduction)

•Reduced crash risks mean that cars can be lighter (5 percent to 23 percent reduction)

•Fewer high-performance, gas-guzzling, hot rods (5 percent to 23 percent reduction).

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Self-driving cars could also encourage a completely new group of people to own vehicles – for example, the elderly, the disabled and possibly those too young to drive themselves. This would increase the welfare of that demographic by giving them greater mobility. Yet travel demand, energy use and carbon emissions would all rise: their estimate for the US is an increase between 2% and 10%. But the study also predicts that energy use will go up 5 percent to 60 percent as people switch to highly automated cars in situations where they would have previously taken alternatives like trains or planes. Wadud says his team made predictions based on economic and behavioral models of individuals’ energy use. But he was careful to say that the future is not easy to see.

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As you can see below, the features of driverless cars may have a range of impacts on energy consumption – both positive, and negative.

Figure above depicts changes in energy consumption, due to various mechanisms facilitated by automation.

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The facts and figures of the situation:

For some perspective, if only 10 percent of the cars on the road were self-driving, 102 million gallons (or 386 million liters) of gasoline would be spared. This represents a significant improvement over the current status quo and is a big step toward embracing a non-destructive energy sources.

If that figure were to rise to 20 percent, an impressive 724 million gallons (or 2.7 billion liters) of fuel would be saved. This creates a causal link and would put the global economy on track toward a better future. One of the features that many are looking forward to is the ability for these driverless cars to drop passengers off and then park themselves. According to the Rocky Mountain Institute, it’s their driving efficiency that may prove to be the tipping point as 20 to 30 percent of fuel usage could be eliminated without the need to find parking.

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Transition to Electric cars:

Self-driving cars could make the transition to electric vehicles easier. As Google consultant Brad Templeton explains here, self-driving cars will allow car manufacturers to radically change their designs. And that could have big implications for electric vehicles. For one, if self-driving cars can be lighter and more efficient, they’ll be able to go much farther on a single battery charge. That could allow electric cars to catch on more quickly — again, possibly saving on fuel use.

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The outlook for plug-in electric vehicles powered by clean energy looks promising but they aren’t likely to ameliorate major urban issues like traffic congestion and sprawl. Car maker Nissan says Japan now has more charging stations than petrol stations. “The Japanese automaker, whose fully battery-powered Leaf can travel up to 172km (107 miles) on a single charge, said there were more than 40,000 places nationwide where electric car owners could recharge their vehicles, compared with fewer than 35,000 petrol stations.” It’s an exaggeration but it signals that electric vehicles are making ground on conventional petrol and diesel powered vehicles.

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Reduced emissions and pollution:

The transportation sector is the second largest source of greenhouse gas emissions in the United States, accounting for 27 percent of the harmful gases emitted into the atmosphere, according to the U.S. Environmental Protection Agency. On the road, cars in the U.S. guzzle about 2 billion barrels of oil each year. Researchers at Lawrence Berkeley National Laboratory believe they have a good estimate, though. A recent study claims that replacing most private cars with a fleet of self-driving electric taxis could cut greenhouse-gas emissions by 90 percent. Using electric powertrains would also basically eliminate oil consumption in cars, researchers argue. They claim this would not only be the greenest solution, but also the cheapest. Even assuming a cost of $150,000 per self-driving electric car, they say, autonomous electric taxis could pay for themselves within five years. That’s because in addition to cutting the cost of fuel and eliminating the cost of a driver, cars could run 24 hours a day, seven days a week — increasing the number of fares. Presumably, that economy would also translate into low usage fees for passengers. The relatively limited ranges of most current electric cars reportedly wouldn’t be an issue either. Because the majority of cars would be operated as part of a coordinated fleet in this scenario, a car needing to recharge could simply be replaced in the field by another. That assumes the taxis will primarily operate in urban areas, where the distance of a single trip is unlikely to exceed a car’s range. And with the first mass-priced 200-mile electric cars arriving within two or three years, the need for a taxi to recharge during the 8- to 12-hour shift of today’s typical taxi driver will be reduced or eliminated.

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The new study, published online in Nature Climate Change, found that self-driving vehicles could cut energy use and heat-trapping emissions the most when used as part of a shared transit system. In 2030, when more electricity will come from renewable sources such as solar and wind, an electric cab could emit 63 to 82 percent fewer greenhouse gases per mile than a privately-owned hybrid. Emissions’ savings are greater when an electric taxi is compared to today’s gasoline cars.  The researchers also looked at the cost-effectiveness of autonomous electric taxis. They expect privately owned electric cars will still be more expensive to buy and operate in 2030 than gasoline ones. But if driven 40,000 to 70,000 miles per year, typical for U.S. taxis, the alternative-fuel vehicle—whether electric battery or hydrogen fuel cell—would be cheaper. Why? Its lower cost per mile offsets the higher purchase price. The savings remain even when factoring in the extra cost of autonomous technology, possibly $150,000 per vehicle, because the car is more energy efficient and doesn’t need a driver. Cheaper happens to be greener. The study is “an exciting addition to the emerging field of analysis exploring the role of advanced connected and automated vehicles,” writes Austin Brown, a researcher with the National Renewable Energy Laboratory, in an accompanying commentary. Another study shows that the use of autonomous taxis could reduce greenhouse gas emissions by 87 to 94% per mile by the year 2030.

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There can be no doubt that self-driving taxis and buses will change the nature of urban mobility. Much more short-distance travel than today will occur in small, lightweight, extremely energy efficient self-driving taxis. Although this may lead to a certain increase in total miles travelled, the following effects combine to reduce greenhouse gas emissions:

1. Self-driving taxis will be mostly electric which reduces carbon emissions (approximately 25% less emissions compared to internal combustion engine)

2. Self-driving urban taxis will be smaller and much lighter than the average car which further reduces energy consumption per kilometer

3. Self-driving taxis reduce demand for private cars and therefore reduce the sizable greenhouse gas emissions during vehicle manufacturing which are typically more than 10% of total life-cycle emissions of a car. According to some estimates, a self-driving car-sharing vehicle or taxi can eliminate 7 to 10 private cars. What a potential for greenhouse gas reduction in auto manufacturing!

4. Self-driving taxis facilitate multi-modal travel (taking an autonomous taxi to the train or bus station, continuing with bus or train, using an autonomous taxi for local transport at the destination)

5. Self-driving taxis facilitate ride sharing especially during peak hours and on certain routes.

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Parking:

The huge impact driverless cars will have on parking:

According to a ParkAtMyHouse Survey, the average UK motorist spends a shocking 106 days of their life looking for a parking spot, and it takes 20 minutes to find a spot in London alone, thanks to restrictions like resident parking and yellow lines. Since parking space is limited in cities, private parking spots can sell for more than houses. As driverless technology continues to improve, so does parking. In the next five to ten years, parking as we know it will be completely redefined just as cars will be. Without the need for drivers, cars can be managed by robots in high efficiency spaces that aren’t a blight on the urban landscape, and don’t require customer stairs, elevators and wide alleyways to allow access to individual cars. Some predict that 15 years from now; autonomous vehicles will have erased the need for up to 90 percent of our current parking spaces. Last year, Audi launched an automated parking garage for self-driving cars near Boston, “where space for vehicles would be reduced by two square meters per car, with driving lanes becoming narrower, and staircases and elevators no longer needed.” Meanwhile, in the UK, Milton Keynes is already experimenting with autonomous vehicles, and planning to convert its current car park real estate to more efficient uses. Future car parks will also be able to offer refuelling, maintenance and other car services. Considering how much of our current city space is taken up with large car parks, all of these developments could be transformative, akin to when “horseless carriages” were replaced by cars, and mews were re-appropriated into prime residential and commercial spaces.

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Driverless cars use an intelligent system of sensors, cameras, radars and lidars (a sensor technology that uses light) to control their movements. These automated vehicles can react faster and drive more smoothly than humans – allowing them to travel closer together on smaller roads. More efficient driving patterns will free up some space, but what will really make the difference is combining automated vehicles with car sharing and new approaches to vehicle parking and maintenance. For example, when you’ve finished with a car, you wouldn’t leave it parked and idle for hours. Instead, the car would drive itself straight to its next customer – according to real-time consumer demand and travel hotspots. Also, cars that are not in use could be parked nose to tail or side by side to save space. They could even be stacked on top of each other in an automated parking lot that simply dispatches the vehicle at the front of the queue when it’s needed, autonomously driving straight to your front door. If a system like this could deliver the same convenience people currently get from owning their own car, but at a lower cost in terms of both ownership and usage, why wouldn’t you make use of it? Widespread adoption of autonomous vehicles delivered through car sharing solutions would free up an incredible amount of space in our cities. Although it’s hard to calculate precisely, conservative estimates put the amount of land in the US taken up by surface car parks at roughly the size of Puerto Rico. And Siemens estimates that as much as 40% of inner city traffic consists of vehicles looking for somewhere to park.

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Shared autonomous vehicles could increase urban space by 15 percent:

A recent UK study has looked at the transformative implications of self-driving vehicles on cities. The authors found that shared autonomous vehicles could increase available urban space by 15 to 20 percent, largely through the elimination of parking spaces. Today central London has about 6.8 million parking spaces and a parking coverage of around 16%! Many large cities have even larger coverage ratios for parking space of up to 30%. Freeing up this space would make our cities greener, increase quality of life and also create the potential for additional housing. Autonomous vehicles will also make the rural communities more attractive because shared travel to nearby cities becomes widely available, affordable and does not lead to loss of productive time. The authors also consider autonomous vehicle only development areas and highways that are limited to autonomous vehicles. This could reduce costs as lane markings and signage would no longer be needed, the lanes could be narrower and throughput per lane would be higher.

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Night driving:

From a technical viewpoint there shouldn’t be a lot of difference between day and night driving with autonomous cars.  Non-vision sensors will not know the difference, and vision based sensors can be made to see under extreme low light via image intensifiers, near infrared or thermal imaging. Ford just overcame a major hurdle for driverless cars. Ford said that it has successfully tested one of its self-driving Ford Fusions in complete darkness — no headlights and no street lighting whatsoever. It was so dark you actually needed night vision goggles to even see the cars. The purpose for doing the testing at night was to test a situation where there might be poor illumination. The test took place on a closed course that was previously mapped out so the car could localize itself in the dark. It travelled up to 60 miles per hour and successfully navigated curvy roads.  So why is it such a big deal? For one, it was successful in conditions no human could ever handle without the assistance of night vision technology. Considering driverless cars don’t have a shot at hitting the roads until they can drive better than humans, the feat shows Ford is well on its way to Level 4 autonomy where no driver supervision is necessary. But it also highlights the sophisticated nature of its new LiDAR technology. Ford is now using a LiDAR device from Velodyne called the Ultra Puck that’s much smaller than previous version that was used on its autonomous fleet. Velodyne Lidar operates at infrared frequency and hence works in daylight and night.

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Bad weather, snow and rain:

Snow, rain, fog and other types of weather make driving difficult for humans, and it’s no different for driverless cars, which stay in their lanes by using cameras that track lines on the pavement. But they can’t do that if the road has a coating of snow. Falling snow or rain can also make it difficult for laser sensors to identify obstacles. A large puddle caused by heavy rain may look like blacktop to an autonomous car’s sensors.  In reports that Google and others have filed with California authorities about their on-road tests of autonomous cars, weather was a prime cause of system failures after which human drivers had to take back control. Automakers are confident that technology can improve. Mercedes-Benz already offers a car with 23 sensors that detect guardrails, barriers, oncoming traffic and roadside trees to keep the vehicle in its lane even on roads with no white lines.

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Driverless cars “see” the world around them using data from cameras, radar and lidar, which bounces laser light off objects to assess shape and location. High-speed processors crunch the data to provide 360-degree detection of lanes, traffic, pedestrians, signs, stoplights and anything else in the vehicle’s path. That enables it to decide, in real time, where to go. Winter makes this harder. Snow can shroud cameras and cover the lane lines they must see to keep a driverless car on course. Lidar also is limited because the light pulses it emits reflect off flakes, potentially confusing a curtain of falling snow with something to avoid, causing the vehicle to hit the brakes. Radar, which senses objects by emitting electromagnetic waves, is better. It also has the longest track record: It’s been used since 1999 in adaptive cruise control to maintain a set distance from other vehicles. Many motorists dream of the day they can sit back and relax while their car drives itself. And while Google and other companies are working hard to make autonomous vehicles a reality, it could take years to create a car that can negotiate complex situations on the road – including wet weather conditions. Google’s self-driving cars can’t currently cope in heavy rain or snow – or find their way around 99 per cent of the US, an insider has admitted. According to MIT Technology Review, the current prototype cars are very reliant on maps to navigate and can’t react like a human driver, dodging potholes and other hazards. Google’s cars have driven themselves over 700,000 miles (1,126,540km) but they can’t cope in snowy conditions and cannot negotiate heavy rain. Chris Urmson, director of the Google car team, said this is because the detection technology is not yet strong enough to separate certain objects from weather conditions.

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Snowstorm:

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Pinpoint Location in snowstorm:

Eustice, who has worked with the Dearborn, Michigan, company on the problem since 2012, said they’ve also found a way to filter the “noise” created by falling snowflakes. The filtered data combined with information from the 3-D maps enable the car to pinpoint its location to within “tens of centimeters,” he said. “That’s high enough accuracy that we know exactly what lane we’re in,” and “helps the robot to understand the environment,” Eustice said, adding that’s still only half the problem: “Then you have to decide what to do now that we know where we are.” Lane lines can become meaningless in a snowstorm, as humans blaze their own trails in the ruts created by vehicles in front of them. “For us to barrel down the road in our lane and ignore the ruts would be unnatural to the other drivers,” Eustice said. So Ford has to figure out how to read the ruts and navigate just like a person, which is “really hard.”

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The current crop of self-driving cars relies on cameras and sensors to see the world around it, which includes lane dividers and the white line marking the edge of the road. When snow and slush cover those up, autonomous cars wander around in the street like drunken robots. It’s really difficult, especially when you have the snow smoke from the car in front,” said Marcus Rothoff, director of Volvo’s autonomous-driving program. “A bit of ice, you can manage. But when it starts building up, you just lose functionality.” After moving the sensors around to various spots on the front, Volvo engineers finally found a solution. Next year, when Swedish drivers take their hands off the wheel of leased XC90s in the world’s first public test of autonomous technology, the radar will be nestled behind the windshield, as it is on the current model, where wipers can clear the ice and snow. As automakers race to get robot cars on the road, they’re encountering an obstacle very familiar to humans: The Winter. Simple snow can render the most advanced computing power useless and leave vehicles dead on the highway. That’s why major players including Volvo Cars, owned by Zhejiang Geely Holding Group Co.; Google, a unit of Alphabet Inc.; and Ford Motor Co. are stepping up their efforts to prevent snow blindness. The struggle to cure snow blindness is among a number of engineering problems still to be resolved, including training cars not to drive too timidly, causing humans to crash into them, and ethical dilemmas such as whether to hit a school bus or go over a cliff when an accident is unavoidable. With about 70 percent of the U.S. population living in the snow belt, learning how to navigate in rough weather is crucial for driverless cars to gain mass appeal, realize their potential to reduce road deaths dramatically and overcome growing traffic congestion. “If your vision is obscured as a human in strong flurries, then vision sensors are going to encounter the exact same obstacles,” said Jeremy Carlson, an IHS Automotive senior analyst who specializes in autonomy.

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Use Artificial Intelligence in snowstorm:

The solution may be artificial intelligence, or AI, said Danny Shapiro, senior director of automotive at Nvidia Corp., a Santa Clara, California-based supplier of high-speed processors. Using processing power equal to 150 MacBook Pros, Nvidia’s latest computer brain can perform as many as 24 trillion “deep learning operations” per second. Deep learning creates “superhuman levels of situational awareness” by training a robot car how to behave, based on millions of miles of driving experience loaded into its software and continually updated, Nvidia said. So, in a snowstorm, the car will know it should follow the ruts rather than stay within the lane lines. The AI vehicle can make adaptions in real time. It’s very similar to how a human learns, by experience. Also like a human, though, a whiteout can leave a driverless car disoriented.

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Woman safety:

Rape and molestation of a woman passenger by driver and his associates in cars/buses is on the rise in India. Driverless taxies would solve this problem.

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Benefits and limitations of driverless car:

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Figure below shows driverless car benefits to consumers and society:

 

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Recent announcements that autonomous vehicles have safely driven hundreds of thousands of miles, and major manufactures aspire to soon sell such vehicles, and optimistic predictions of their benefits have raised hopes that this technology will soon be widely available and solve many transportation problems. However, there are good reasons to be cautious when predicting their future role. There is considerable uncertainty concerning autonomous vehicle benefits, costs and travel impacts. Advocates claim that they will provide large benefits that offset costs, and so advocate policies that encourage their implementation. However, autonomous vehicles will require additional equipment, services and maintenance that will probably increase user costs by hundreds or thousands of dollars per vehicle-year, and many of their benefits are unproven. Current automated vehicles can only self-drive under limited conditions: significant technical and economic obstacles must be overcome before typical households can rely on them for daily travel. Operating a vehicle on public roads is more complex than flying an airplane due to the frequency and proximity of interactions with often-unpredictable objects including other vehicles, pedestrians, animals, buildings, trash and potholes. If they follow previous vehicle technology deployment patterns, autonomous vehicles will initially be costly and imperfect. During the 2020s and perhaps the 2030s, autonomous vehicles are likely to be expensive novelties with limited abilities, such as restrictions on the weather and road conditions in which they may operate. It will probably be the 2040s or 2050s before middle-income families can afford to own self-driving vehicles that can safely operate in all conditions, and longer before they become affordable to lower-income households on the used-vehicle market. A significant portion of motorists may resist such vehicles, just as some motorists prefer manual transmissions, resulting in mixed traffic that creates new roadway management problems. Vehicle innovations tend to be implemented more slowly than other technological changes due to their high costs, slow fleet turnover and strict safety requirements. Automobiles typically cost fifty times and last ten times as long as mobile phones and personal computers, so consumers seldom purchase new vehicles just to obtain a new technology. Autonomous vehicles will probably have relatively costly equipment and service standards, similar to airplanes, which may discourage some users. Large increases in new vehicle purchase and scrappage rates would be required for most vehicles to be autonomous before 2050. Autonomous vehicles may allow shared vehicles to replace some personal vehicles. Their costs are likely to be between car-sharing ($0.60-1.00 per mile) and human-driven taxis ($2.00-3.00 per mile), depending on factors such as their cleaning costs, which will make them a cost effective alternative to owning lower (below 5,000 annual miles) vehicles. Many motorists are likely to prefer owning a personal vehicle for prestige and convenience sake. As a result, shared vehicles are likely to reduce vehicle ownership mostly in compact, multi-modal urban areas, and will have little effect in exurban and rural areas. Advocates may exaggerate net benefits by ignoring new costs and risks, offsetting behavior (the tendency of road users to take additional risks when they feel safer), rebound effects (increased vehicle travel caused by faster travel or reduced operating costs, which may increase external costs), and harms to people who do not to use the technology, such as reduced public transit service. Benefits are sometimes double-counted, for example, by summing increased safety, traffic speeds and facility savings, although there are trade-offs between them. Transportation professionals (planners, engineers and policy analysts) have important roles to play in autonomous vehicle development and deployment. We can help support their development and testing, and establish performance standards they must meet to legally operate on public roads. If such vehicles perform successfully and become common they may affect planning decisions such as the supply, design and operation of roadways, parking and public transit. To be prudent, such infrastructure changes should only occur after autonomous vehicle benefits, affordability and public acceptance are fully demonstrated. This may vary: autonomous vehicles may affect some roadways and communities more than others. A critical question is whether autonomous vehicles increase or reduce total vehicle travel and associated external costs. It could go either way. By increasing travel convenience and comfort, and allowing vehicle travel by non-drivers, they could increase total vehicle mileage, but they may also facilitate car-sharing, which allows households to reduce vehicle ownership and therefore total driving. They will probably increase total vehicle travel unless implemented with offsetting policies such as efficient road and parking pricing. Another critical issue is the degree potential benefits can be achieved when only a portion of vehicle travel is autonomous. Some benefits, such as improved mobility for affluent non-drivers, may occur when autonomous vehicles are uncommon and costly, but many potential benefits require that most or all vehicles on a road operate autonomously. For example, it seems unlikely that traffic densities can significantly increase, traffic lanes be narrowed, parking supply be significantly reduced, or traffic signals be eliminated until most vehicle on affected roads self-drive. A key public policy issue is the degree that this technology may harm people who do not use such vehicles, for example, if increased traffic volumes and speeds degrade walking and cycling conditions, conventional public transit service declines, or human-driven vehicles are restricted. Some strategies, such as platooning, may require special autonomous vehicle lanes to achieve benefits. These issues will probably generate considerable debate over their merit and fairness.  Autonomous vehicle implementation is just one of many trends likely to affect future transport demands and costs, and therefore planning decisions, and not necessarily the most important. Its ultimate impacts depend on how it interacts with other trends, such as shifts from personal to shared vehicles. It is probably not a “game changer” during most of our professional lives, and is certainly not a “paradigm shift” since it does not fundamentally change how we define transport problems; rather, it reinforces existing automobile-oriented transport planning.

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The potential advantages of self-driving cars are numerous. The broad range of sensors can monitor a vehicle’s movement and the movement of other vehicles, pedestrians, and potential obstacles much more thoroughly than the average driver, and can safely react to potential collusion hazards much quicker than average human reflexive capabilities.  These vehicle operating advantages could substantially reduce the current total 10 million U.S. motor vehicle accidents annually and reduce associated fatalities by possibly thousands per year. Other benefits include more efficient use of existing highway and road systems by reducing traffic congestion/delays and continuously operating vehicles with maximum fuel efficiencies.  Just think, if everyone’s vehicle normally operated safely during trips, including at all stops and intersections, continuously turning or merging safely onto all freeway/road systems, and continuously maintained speeds within all established speed-safety limits, we could possibly prevent most past-current accidents.

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Other Benefits:

Revolutionizing Car Design:

To an enormous extent, the design of cars is built around the needs of the driver. Start with the windshield: the front of the passenger compartment, close to the first two people in the car, is a large and vulnerable piece of glass. No matter how many technological advancements have been made in windshield construction, shattered windshields and people being launched through them remains a major cause of injury and death. And windshields are one of the hardest, yet most essential, parts of a car to keep clean, requiring among other things regular replacement of the wipers. But a driverless car does not need a windshield (it will have exterior cameras/sensors to watch the road, the way some sensors now watch the bumpers), nor does it need to situate seats facing forward near the front. The person in charge of getting the car to its destination can sit anywhere and check the directions and the road from a monitor. Once you move away from the need to have everything within arm’s reach of the driver’s console, other design changes follow. Air conditioning, GPS, stereo, and video systems can be controlled from anywhere within the car. And debates over things like stick shift versus automatic transmissions become pointless without the human driver. Then there’s safety. The current tug of war between heavier and therefore safer cars against lighter and more fuel-efficient cars could be revolutionized if we transition to truly driverless roads, which would likely see far fewer accidents and in particular far fewer high-speed accidents. Safety isn’t the only reason for big cars—there’s also a need for space for kids and cargo—but it’s a major consideration and one that will be less urgent as people acclimate to reduced risk of accident.

Enhanced Human Productivity:

Freed from the task of managing the progress of the vehicle, all of the excess capacity afforded your eyes, hands, and brain can be turned to doing what you usually do on an airplane, train, or a bus. Finish up a project, type a letter, monitor the progress of your kid’s schoolwork, return phone calls, take phone calls safely, text until your heart’s content, read a book, or simply relax and enjoy the ride. Currently, the time spent in our cars is largely given over to simply getting the car and us from place to place. Interestingly though, even doing nothing at all would serve to increase human productivity. Studies have shown taking short breaks increase overall productivity. Thus, doing nothing on your way to work could actually make you more productive when you get there. Self-driving cars would make commute time productive time, for whatever pursuits we desire. Of course, while the people producing books on tape and podcasts might take something of a hit, the producers of video and filmed entertainment would experience a boom, as practically every car would be fitted with a video entertainment system.

Elimination of Traffic Enforcement Personnel:

If every car is “plugged” into the grid and driving itself, then speeding,—along with stop sign and red light running will be eliminated. The cop on the side of the road measuring the speed of traffic for enforcement purposes? They’re gone. Cars won’t speed anymore, so why send a police officer out to write speeding tickets? Yes, there will probably still be accidents occasionally, but they’ll be fewer and farther between. This means you can have a minimal contingent of police officers assigned to traffic duties, freeing more of the force up to deal with crime.

Higher Speed Limits:

Since all cars are in communication with one another, and they’re all programmed to maintain a specific interval between one another, and they all know when to expect each other to stop and start, the need to accommodate human reflexes on the highway will be eliminated. Thus, cars can maintain higher average speeds, while sacrificing very little in the way of safety and/or fuel efficiency. This means higher speed limits can be enacted, as the concerns about collisions will be all but eliminated.

Lighter, more versatile Cars:

The vast majority of the weight in today’s cars is there because of the need to incorporate safety equipment. Steel door beams, crumple zones and the need to build cars from steel in general relate to preparedness for accidents. Self-driving cars will crash less often, accidents will be all but eliminated, and so the need to build cars to withstand horrific crashes will be reduced. This means cars can be lighter, which will make them more fuel-efficient. Also, since you don’t have to be concerned about the placement of the control mechanisms for a car, you can arrange the interior any way you want. All the seats can even face inwards, as there’s no need to be concerned about a driver’s visibility.

Reducing Car Theft:

This is a second-order effect, but so long as a car is controlled by a driver, it needs to be operated manually; the more it is run by a computer, the easier it becomes to password-protect the car’s navigation system. That may make it a lot harder to steal unoccupied cars but, like ATM passwords, could also encourage more car thieves to take up carjacking to ensure they can get the passwords. And of course, a car that can go nowhere without GPS cannot go far without police following it.

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Table below summarises expected autonomous vehicle benefits and costs.

Benefits Costs/Problems
Reduced driver stress. Reduce the stress of driving and allow motorists to rest and work while traveling. Increases costs. Requires additional vehicle equipment, services and maintenance, and possibly roadway infrastructure.
 Reduced driver costs. Reduce costs of paid drivers for taxis and commercial transport. Additional risks. May introduce new risks, such as system failures, be less safe under certain conditions, and encourage road users to take additional risks (offsetting behavior).
Mobility for non-drivers. Provide independent mobility for non-drivers, and therefore reduce the need for motorists to chauffeur non-drivers, and to subsidize public transit. Security and Privacy concerns. May be used for criminal and terrorist activities (such as bomb delivery), vulnerable to information abuse (hacking), and features such as GPS tracking and data sharing may raise privacy concerns.
Increased safety. May reduce many common accident risks and therefore crash costs and insurance premiums. May reduce high-risk driving, such as when impaired. Induced vehicle travel and increased external costs. By increasing travel convenience and affordability, autonomous vehicles may induce additional vehicle travel, increasing external costs of parking, crashes and pollution. Social equity concerns. May have unfair impacts, for example, by reducing other modes’ convenience and safety.
Increased road capacity, reduced costs. May allow platooning (vehicle groups traveling close together), narrower lanes, and reduced intersection stops, reducing congestion and roadway costs. Reduced employment and business activity. Jobs for drivers should decline, and there may be less demand for vehicle repairs due to reduced crash rates.
More efficient parking, reduced costs. Can drop off passengers and find a parking space, increasing motorist convenience and reducing total parking costs. Misplaced planning emphasis. Focusing on autonomous vehicle solutions may discourage communities from implementing conventional but cost-effective transport projects such as pedestrian and transit improvements, pricing reforms and other demand management strategies.
Increase fuel efficiency and reduce pollution. May increase fuel efficiency and reduce pollution emissions.
Supports shared vehicles. Could facilitate car-sharing (vehicle rental services that substitute for personal vehicle ownership), which can provide various savings.

Autonomous vehicles can provide various benefits and impose various costs. Some impacts, such as reduced driver stress and increased urban roadway capacity, can occur under level 2 or 3 implementation, which provides limited self-driving capability, but many benefits, such as significant crash reductions, road and parking cost savings and affordable mobility for non-drivers, require that level 4 vehicles become common and inexpensive.

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Potential Benefits:

Advocates predict that autonomous vehicles will provide significant user convenience, safety, congestion reductions, fuel savings, and pollution reduction benefits. Such claims many be overstated. For example, advocates argue that because driver error contributes to more than 90% of traffic accidents, self-driving cars will reduce crashes 90%. However, autonomous vehicles are likely to introduce new risks including system failures (“death by computer”), cyberterrorism, offsetting behavior (the tendency of road users to take additional risks when they feel safer; also called risk compensation) and rebound effects (increased vehicle travel resulting from faster or cheaper travel). For example, because they feel safer vehicle occupants may reduce seatbelt use, other road users may become less cautious, vehicles may operate faster and closer together, and human drivers may be tempted to join autonomous vehicle platoons – it may become a sport – which will introduce new risks and enforcement requirements. Detailed analysis by Sivak and Schoettle (2015a) concluded that autonomous vehicles may be no safer than an average driver and may increase total crashes when self- and human-driven vehicles are mixed. Estimated congestion and parking cost reductions, energy savings and emission reductions are also uncertain due to interactive effects. For example, the ability to work and rest while traveling may induce some motorists to choose larger vehicles that can serve as mobile offices and bedrooms (“commuter sex” may be a marketing strategy) and drive more annual miles. Self-driving taxis and self-parking cars will require empty backhauls. Although the additional vehicle travel provides user benefits (otherwise, users would not increase their mileage) it can increase external costs, including congestion, roadway and parking facility costs, accident risk imposed on other road users, and pollution emissions. Some strategies such as platooning may be limited to grade-separated roadways, so human-driven vehicles may increase congestion on surface streets. Autonomous vehicles may reduce public transit travel demand, leading to reduced service, and stimulate more sprawled development patterns which reduce transport options and increase total vehicle travel.

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Potential advantages of autonomous cars:

An increase in the use of autonomous cars would make possible such benefits as:

•Avoid traffic collisions (and resulting deaths and injuries) caused by human driver errors such as reaction time, tail gating, rubbernecking and other forms of distracted or aggressive driving.

•Increased roadway capacity and reduced traffic congestion due to reduced need for safety gaps and the ability to better manage traffic flow.

•Relief of vehicle occupants from driving and navigation chores.

•Higher speed limit for autonomous cars.

•Removal of constraints on occupants’ state – in an autonomous car, it would not matter if the occupants were under age, over age, unlicensed, blind, distracted, intoxicated, or otherwise impaired.

•Reduction of physical space required for vehicle parking, and vehicles will be able to drive where space is not scarce.

•Reduction in the need for traffic police and premium on vehicle insurance.

•Reduction of physical road signage – autonomous cars could receive necessary communication electronically (although physical signs may still be required for any human drivers).

•Smoother ride.

•Reduction in car theft, due to the vehicle’s increased awareness.

•Increased ergonomic flexibility in the cabin, due to the removal of the steering wheel and remaining driver interface, as well as no occupant needing to sit in a forward-facing position.

•Increased ease-of-use of large vehicles such as motorhomes.

•Increased time in daily leisure activities or work productivity with the replacement of commuting hours.

•The mobility of the young, the elderly, and the disabled will be increased.

•Traffic flow could be more efficient and congestion decreased.

•Vehicle occupants could spend travel time engaged in other activities, so the costs of travel time and congestion are reduced.

•Fuel efficiency can be increased and alternative energy sources facilitated.

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When used for car-sharing:

•Reduces total number of cars.

•Enables new business models such as mobility as a service which aim to be cheaper than car ownership by removing the cost of the driver.

•Elimination of redundant passengers – the robotic car could drive unoccupied to wherever it is required, such as to pick up passengers or to go in for maintenance.

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Disadvantages of autonomous cars:

Although the advantages to automation cars seem to be endless, they hold just as many disadvantages to advantages. Many examples could be stated below:

1. Potential loss of jobs: With the notation of automatic vehicles, public transit specifically taxi drivers will lose their jobs as many people may turn to these cars. This may be due to the fact that they more fuel efficient and cost less compared to a taxi ride to their wanted destination. Therefore, a decrease in demand for drivers will result.

2. Computers are still computers: Although the innovation of technology has advanced so drastically in the past decade, there will always be room for concern in the malfunction or breakdown of those computers that control the vehicles. This may result in expensive or fatal collision which may be even more tragic compared to those caused by human error.

3. Not fully mature: Although this is a huge leap into such advanced technology, the promise for public safety cannot be 100% assured. If we were to take a look at the scale at which it is currently developing on, based on Roger’s diffusion of innovation, we are still in the knowledge and persuasion stages of this operation, meaning we are still in the early stages of developing this innovation and many people may still be hesitant in the investing of this car.

4. Not affordable: Since this car is relatively new to the market, it would not be affordable for most people as prices would be extremely expensive and not affordable by the average person.

5. Laws and Regulations: If an automated car is in an accident, who is responsible? Is it the people in the car (if any), the car’s owner(s), the car’s manufacturer(s), or the car’s software creator(s)?

6. Increased Complexity and Risk: Computers can malfunction, be hacked, spontaneous crash and reboot, and automated cars are heavily reliant on GPS systems. All of these systems are increased risk for safety when using an autonomous vehicle.

7. Appeal: For some people, the appeal of driving their own car will be too great. The human need for control, power, and speed is a strong desire for some people.

8. Experimental: Finally, autonomous cars are still experimental and have not completely proven themselves yet.

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Potential Costs:

The incremental costs of making autonomous vehicles are uncertain. They require a variety of special sensors, computers and controls, which currently cost tens of thousands of dollar but are likely to become cheaper with mass production (KPMG 2012). However, because system failures could be fatal to both vehicle occupants and other road users, all critical components will need to meet high manufacturing, installation, repair, testing and maintenance standards, similar to aircraft components, and so will probably be relatively expensive. Autonomous vehicle operation may require special navigation and mapping service subscriptions (this explains the Google Corporation’s interest in this technology). Other, simpler technologies add hundreds of dollars to vehicle retail prices. For example, GPS and telecommunications systems, review cameras, and automatic transmissions typically cost $500 to $2,000. Navigation and security services such as OnStar and TomTom have $200 to $350 annual fees. Autonomous vehicles require these plus other equipment and services.

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Limitations of driverless cars:

1. Human assistance:

Most self-driving cars being tested still require a human in the driver’s seat to help with the tricky bits, like snow or confusing highway interchanges.

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2. Safety and regulatory concerns:

This is one of the big hurdles self-driving cars will have to surmount before catching on — since self-driving cars will likely have to meet more rigorous standards than regular cars. The first accident that’s caused by a computer malfunction will freak everyone out far beyond the thousands of car accidents caused by humans. That makes regulations trickier. Who is liable if a self-driving car crashes? The manufacturer?  The passenger?

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3. Hacking:

Fears about hackers taking control of driverless cars are one of the biggest roadblocks preventing autonomous vehicles taking to the roads.  A series of high-profile hacks of computerised systems in cars highlighted vulnerabilities with current technology, according to a new report.  Boston Consulting Group (BCG) and the World Economic Forum partnered to research driverless cars and identify the main obstacles in their path, with worries about hacking singled out as one of the major technical challenges.  “Such incidents could demolish public confidence in autonomous vehicles overnight and undo years of costly research and development,” said co-author of the report Jan Mohr, referring to attacks that affected manufacturers including BMW, GM, Jeep and Tesla.  Computer security experts were able to take over systems in a Tesla car. “In one well-publicised incident, researchers connected a laptop directly to the controller area network – the system which connects multiple vehicle functions – of a conventional car, [giving] them full control over nearly every system, as they demonstrated by disabling the brakes in a controlled environment.” In another demonstration, students at a Chinese hacking conference gained remote access to a brand new car and unlocked it, sounded the horn, flashed the lights and opened the sunroof while it was in motion.  The report warns that the car industry’s mind-set opens it up to cyber-attacks as designers still think of it as a “closed system”, when in reality the addition of networked systems such as satnavs, voice-controlled phones and web connections means they can be accessed from a distance. Having to make these systems cheap, small and robust enough to fit in a vehicle also makes it hard to build in high-level cryptography to keep attackers at bay.

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Potential Cyber-attacks on Automated Vehicles: 2014 study:

Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of their deployment by 2020 show that this is becoming a reality. The ITS industry has already been focusing much of its attention on the concepts of “connected vehicles” (United States) or “cooperative ITS” (Europe). These concepts are based on communication of data among vehicles (V2V) and/or between vehicles and the infrastructure (V2I/I2V) to provide the information needed to implement ITS applications. The separate threads of automated vehicles and cooperative ITS have not yet been thoroughly woven together, but this will be a necessary step in the near future because the cooperative exchange of data will provide vital inputs to improve the performance and safety of the automation systems. Thus, it is important to start thinking about the cybersecurity implications of cooperative automated vehicle systems. In this paper, authors investigate the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities. Authors analyze the threats on autonomous automated vehicles and cooperative automated vehicles. This analysis shows the need for considerably more redundancy than many have been expecting.

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Driverless car researchers develop plan to prevent hacking on the highway:

If an internet ne’er-do-well gains access to your computer or phone, they can cause an awful lot of havoc, but this is nothing new. You put the pieces back together, change your passwords, and go on with your life. If an attacker were to interfere with the computer powering your self-driving car, on the other hand, the consequences could be much more dire — you might not be going on with your life after that. Two experts on self-driving cars are weighing in on this increasingly likely scenario and their message is that companies are not prepared for the threat of cyberattacks on future robotic cars. Jonathan Petit of University College Cork and Steven Shladover of the University of California Berkeley have completed what they say is the first exhaustive analysis of potential hacking on self-driving cars. We don’t really know how self-driving cars will work because there are no consumer products just yet, so Petit and Shladover addressed a variety of systems and attempted to identify the most serious threats to safety and security. The report splits cyber-threats to robotic vehicles into three matched pairings — passive snooping versus active manipulation, jamming of a signal versus the substitution of a false signal, and attacks focusing on single cars versus those targeting a network of interconnected vehicles. If you go down the list and choose the worst case scenarios, you unsurprisingly come up with an active attack that involves using fake signals to affect a network of cars. Petit and Shladover point to global navigation satellite systems like GPS and GLONASS as prime targets for this kind of attack. More worryingly, the technology to do this already exists. GPS scrambling technology can be had for as little as $20 and could be used to knock a self-driving car off course. It’s likely other technologies like laser rangefinders and radar would be used to orient the vehicle, but a broken GPS connection would at least cause the car to pull over and make you late. More advanced GPS spoofing systems are even capable of passing incorrect location data to a car. This is particularly problematic — if the vehicle doesn’t know it has bad data, a crash could be unavoidable. If cars are connected in a mesh network to enable more efficient traffic management, that bad data could end up being passed to other vehicles and cause a chain reaction.

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4. Lethal weapon:

FBI warns driverless cars could be used as ‘lethal weapons’:

Google’s driverless car may remain a prototype, but the FBI believes the “game changing” vehicle could revolutionise high-speed car chases within a matter of years. The report also warned that autonomous cars may be used as “lethal weapons”. The FBI predicts that autonomous cars “will have a high impact on transforming what both law enforcement and its adversaries can operationally do with a car.” In a section called Multitasking, the report notes that “bad actors will be able to conduct tasks that require use of both hands or taking one’s eyes off the road which would be impossible today.” One nightmare scenario could be suspects shooting at pursuers from getaway cars that are driving themselves. Autonomy … will make mobility more efficient, but will also open up greater possibilities for dual-use applications and ways for a car to be more of a potential lethal weapon that it is today.” This presumably reflects fears that criminals might override safety features to ignore traffic lights and speed limits, or that terrorists might program explosive-packed cars to become self-driving bombs. It directly contradicts the message that many developers of self-driving vehicles are trying to communicate: that these cars – immune from road rage, tiredness and carelessness – can be even safer than human operators. The FBI also claims that tailing suspects will be much simpler with the next generation of robot cars. “Surveillance will be made more effective and easier, with less of a chance that a patrol car will lose sight of a target vehicle,” says the report. “In addition, algorithms can control the distance that the patrol car is behind the target to avoid detection or intentionally have a patrol car make opposite turns at intersections, yet successfully meet up at later points with the target.”

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ISIS working on driverless car bombs, says NATO security expert:

According to a NATO security expert, the Islamic State is working on autonomous cars that could deliver explosives. Dr. Jamie Shea, the Deputy Assistant Secretary General for the Emerging Security Challenges Division of NATO, said that the ISIS R&D department located in Raqqa, Syria, is using its technical expertise to create vehicles that would drive themselves to a target location. This leaves out the need for a suicide bomber to drive the car, and it would reduce the increasing decline in the numbers of ISIS fighters. We don’t expect ISIS will be approaching the problem the same way Google is, however. For starters, automaker and tech company attempts to safeguard passengers and pedestrians probably wouldn’t be taken into consideration in an ISIS-backed automobile. We’d imagine this to be more like a remote-control vehicle, a sort of drone on wheels. And given the fact that the vehicles would be one-time use if successful, the investment in machinery is likely to be much lower.

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5. Robotic Cars and their New Crime Paradigms:

Taken to the extreme, the moral rewiring of robotic cars could allow them to be used for ram raids or to run down crowds of pedestrians. The sensors inside the car may realistically be able to detect passengers using illegal drugs, transporting weapons, transporting kidnapped children or confessing to crimes.

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6. Driverless cars could encourage bored passengers to have sex behind the wheel, experts warn:

Barrie Kirk from the Canadian Automated Vehicles Centre of Excellence claims that removing the need to focus on the road will result in a lot of passengers having sex behind the wheel, as a means of whiling away tedious motorway journeys. He warned that this could actually be very dangerous, because although the vehicles are described as “driverless”, there will be a lot of instances when humans needs to jump in and take control.

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7. Driverless cars could save lives, but kill businesses:

Eventually, if fully driverless cars (L4 vehicles under the American government’s classification system) can be summoned with a smartphone just like Uber cars today, many people might forgo car ownership. Or families in developed nations might own one car instead of two. That could be a financial boon for families. In the United States, cars are usually the second-largest item in the household budget, even though studies show they sit idle 90% of the time. But automakers would suffer. If driverless cars catch on, US car sales could plunge 40% in the next 25 years, Barclays analyst Brian Johnson wrote in a report. General Motors and Ford Motor, he added, would have to cut their combined number of assembly plants in the US and Canada to 17 from the current 30. Some 25,000 auto workers would lose their jobs. People who drive taxis, Uber cars, transit buses or delivery trucks would be losers. The number of jobs lost in the U.S. alone could total 2.6 million, or nearly 2% of the work force, calculates economist Martin Zimmerman at the University of Michigan.  The driverless car will damage entire professions. Since it will slash the number of cars we need, it will decimate the number of cars we make, meaning millions of layoffs in this labor-intensive industry. In due course, the driverless vehicle will also put out of work millions of long-distance truckers, traffic cops, and car dealers.  With accidents nearing extinction, thousands of repair garages will go out of business, the number of car-insurance policies will plunge, as will their rates. Technology has already put out of work millions of manufacturers, postal workers, travel agents, journalists, realtors, printers, publishers, camera shops, and appliance stores to mention but some in the newly insecure class that is feeding our era’s political perplexity. The driverless car will accelerate this trend.

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8. Privacy issues: There are lots of possible issues here. Many of the benefits from self-driving cars come from the vehicles being able to communicate and share data with each other. Likewise, crash data will almost certainly be stored for use by manufacturers and to sort out liability (California’s laws require this). But how much data will be stored? And how will it be shared?

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9. Driverless cars may take the fun out of driving:

Lots of people enjoy driving because of the feeling of being in control, the steering, reacting to the sound of the engine and being alert and aware of your actions. In a driverless car, the car would do all the work for you, so you would just sit there. Whilst that idea will appeal to a lot of people, others may be disheartened.

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10. Motion sickness:

It has recently been suggested that driverless vehicles are likely to cause motion sickness. One main reason for motion sickness is that your inner ear is telling your brain something different to your eyes (for example, when you read a book in a car, your eyes think you’re stationary, but your ears know you’re moving). There are lots of bizarre cures for motion sickness, but the one you probably know best is being told to look at the road when you’re feeling queasy. That’s because focusing on the road is making your eyes focus on the fact that you’re moving. It is for this reason that drivers are less prone to motion sickness than passengers – but in a driverless car, we’re all passengers.

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11. Unpredictable Humans:

Computer algorithms can ensure that self-driving cars obey the rules of the road — making them turn, stop, slow down when a light turns yellow and resume when a light turns to green from red. But this technology can’t control the behavior of other drivers. Autonomous vehicles will have to deal with drivers who speed, pass even when there’s a double yellow line and drive the wrong way on a one-way street.  One solution is to equip cars with transponders that communicate their position, speed and direction to other vehicles. This is known as vehicle-to-vehicle or V2V communication, and it is similar to how airplanes avoid each other in the air. While promising, V2V is still early in development, and it will be effective only when large numbers of vehicles with this capability are on the road.

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12.  Automated cars can’t drive anywhere:

Many people believe that Google, for example, has built a car that can drive anywhere on its own. The car itself can only work in a very limited context right now. It has to be very good weather, it can’t handle parking garages because it can’t get a GPS signal. The reason why these cars can drive themselves is because they know where they are in the world.

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Public opinion and surveys about driverless car:

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Public opinion surveys:

In a 2011 online survey of 2,006 US and UK consumers by Accenture, 49% said they would be comfortable using a “driverless car”. A 2012 survey of 17,400 vehicle owners by J.D. Power and Associates found 37% initially said they would be interested in purchasing a fully autonomous car. However, that figure dropped to 20% if told the technology would cost $3,000 more. In a 2012 survey of about 1,000 German drivers by automotive researcher Puls, 22% of the respondents had a positive attitude towards these cars, 10% were undecided, 44% were skeptical and 24% were hostile. In a 2014 US telephone survey by Insurance.com, over three-quarters of licensed drivers said they would at least consider buying a self-driving car, rising to 86% if car insurance were cheaper. 31.7% said they would not continue to drive once an autonomous car was available instead.  In a February 2015 survey of top auto journalists, 46% predict that either Tesla or Daimler will be the first to the market with a fully autonomous vehicle, while (at 38%) Daimler is predicted to be the most functional, safe, and in-demand autonomous vehicle. In 2015 a questionnaire survey by Delft University of Technology explored the opinion of 5,000 people from 109 countries on automated driving. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. 22% of the respondents did not want to spend any money for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Finally, respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data.

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Cisco survey 2013:

Cisco announced the results of its study into consumer’s thoughts about connected and driverless cars. While a large part of the study focused on the role of technology in the car shopping experience (unsurprisingly, nobody likes car dealerships), the study also looked into drivers’ attitudes about driverless cars. Surprisingly, 57% of all of the respondents said that they would trust driverless cars to drive them around, but there are some clear differences between different markets. Acceptance for driverless cars seems to be especially strong in emerging markets. In Brazil, for example, 95% of respondents said they would trust a driverless car, in India 86% would do so and in China, 70% of drivers would be willing up to give control. In the U.S., however only 60% said they would trust these cars, and 57% of Russians (who may have good reason to think that they need to have full manual control over their cars) said they would consider these automated vehicles. Germans – who still love their manual transmissions – are far more skeptical (37% would trust them). Japan, a country that seems relatively at ease with robots, comes in dead last with 28%. All of these numbers, by the way, are lower when the researchers asked if drivers would let their kids ride in these vehicles. Still, this clearly shows that there is a market for driverless cars if they ever become available commercially. With regard to trusting technology, the study also found that 74% of drivers would be fine with their car tracking their driving habits if they could save on insurance and maintenance cost. About 65% of them would also share their height, weight, driving habits and entertainment preferences with the car manufacturers in return for a more custom driving experience.

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Perceptions of the dangers of driverless cars in the United Kingdom (UK) in November 2014:

This statistic shows the share of respondents who reacted positively to factors related to driverless cars in the United Kingdom in November 2014. Technological malfunctions and the threat of hacking or viruses were the most commonly perceived disadvantage among respondents.

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Shock 2016 survey reveals Britons would use driverless cars to get home from binge at the pub:

Britons would use driverless cars to get home from the pub. And others said they would welcome autonomous cars because they could have a nap on the motorway. Two drivers even thought they would be able to have sex at the wheel while the car drove itself. The alarming possibilities presented by driverless cars were exposed in two separate surveys. A survey for The Co-op Insurance found that 22 per cent of young drivers thought that if they had a driverless car they would be able to drink as much alcohol as they wanted and then get behind the wheel and let the vehicle take them home. And 24 per cent thought that while at the wheel of a self-driving car they would be able to have a snooze. Meanwhile a survey by Whatcar.com found that one in four drivers would grab the chance to have a nap on a motorway if they had a driverless car. Others said they would take the chance to chat to fellow passengers, surf the web or watch TV. One in three said motorways would be the best roads to have a car that drives itself despite the high speed of the traffic. Half of those polled said they would surrender control to the computer in a traffic jam while one in five thought an autonomous car would be useful in a city. But the Whatcar.com survey of 900 motorists also found great overall suspicion of the new technology. Half the drivers said they would feel unsafe or very unsafe behind the wheel of a self-driving car. The biggest concerns, each shared by one in three drivers, were that a driverless car could not avoid an accident or that it would take away the enjoyment of being on the road. The prospect of driverless cars allowing motorists to opt for dangerous activities like drinking and driving raises serious concerns. Steve Kerrigan of The Co-operative Insurance said: “This research has shown that young drivers are unprepared and uninformed about self-driving cars. “Many even mistakenly believe that you will be able to drink alcohol and sleep it off whilst you are driven home.” But an AA spokesman said: “If you are behind the wheel of a driverless car you are still responsible. “There cannot be any sleeping or getting drunk and letting the car take you home. “If that is what you are looking for you would be better off saving your money and getting a train or a taxi.

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New 2016 poll suggests no one wants driverless cars except the people who manufacture them:

Researchers Brandon Schoettle and Michael Sivak at the Michigan University’s Transportation Research Institute have been carrying out surveys on how the public feels about automating cars. The results? Not so good. 51 percent of respondents said they would not ride in a driverless car, 63 percent said they are unlikely to buy one in the next decade, and 43 percent said that driverless cars aren’t safe. Votes tended to be split along age and gender, with young people and men more likely to show interest in the burgeoning technology than older folks and women.  Respondents said their preference for car automation was, well, no automation at all. In second place was “partially self-driving vehicles”, while proper self-driving cars were the “least preferred choice”.  Researchers found that only 15.5 percent of respondents would be interested in an autonomous vehicle.

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Consumer Watchdog questions Google about driverless cars:

NHTSA should require humans with access to a steering wheel, brake, and accelerator in autonomous vehicles, says group. NHTSA has said that autonomous vehicle technology is an area of rapid change that requires you to remain flexible and adaptable. Please ensure that flexibility does not cause you to lose sight of the need to put safety first. Consumer Watchdog noted that Google is pressing NHTSA to create a fast-track approval process for its self-driving robot cars that would bypass usual rulemaking proceedings and Federal Motor Vehicle Safety Standards. Consumer Watchdog said NHTSA should reject Google’s proposal and instead ask the company ten tough questions as the agency develops its automated vehicle technology guidelines. They are:

1. We understand the self-driving car cannot currently handle many common occurrences on the road, including heavy rain or snow, hand signals from a traffic cop, or gestures to communicate from other drivers. Will Google publish a complete list of real-life situations the cars cannot yet understand, and how you intend to deal with them?

2. What does Google envision happening if the computer “driver” suddenly goes offline with a passenger in the car, if the car has no steering wheel or pedals and the passenger cannot steer or stop the vehicle?

3. Your programmers will literally make life and death decisions as they write the vehicles’ algorithms. Will Google agree to publish its software algorithms, including how the company’s “artificial car intelligence” will be programmed to decide what happens in the event of a potential collision? For instance, will your robot car prioritize the safety of the occupants of the vehicle or pedestrians it encounters?

4. Will Google publish all video from the car and technical data such as radar and lidar reports associated with accidents or other anomalous situations? If not, why not?

5. Will Google publish all data in its possession that discusses, or makes projections concerning, the safety of driverless vehicles?

6. Do you expect one of your robot cars to be involved in a fatal crash? If your robot car causes the crash, how would you be held accountable?

7. How will Google prove that self-driving cars are safer than today’s vehicles?

8. Will Google agree not to store, market, sell, or transfer the data gathered by the self-driving car, or utilize it for any purpose other than navigating the vehicle?

9. NHTSA’s performance standards are actually designed to promote new life-saving technology. Why is Google trying to circumvent them? Will Google provide all data in its possession concerning the length of time required to comply with the current NHTSA safety process?

10. Does Google have the technology to prevent malicious hackers from seizing control of a driverless vehicle or any of its systems?

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Predictions and future developments of automated driving:

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Predictions:

Autonomous vehicles are still a developing technology; a large number of companies and researchers have speculated about future developments and the possible effects of the cars. In 2014 Raj Rajkumar, director of autonomous driving research at Carnegie-Mellon University said that the artificial intelligence necessary for a driverless car would not be available “anytime soon” and that Detroit car makers believe the prospect of a fully self-driving car arriving anytime soon is ‘pure science fiction. Already in 2012, European Tier 1 suppliers predicted that the implementation of highly automated driving will be possible from 2020 and fully AD to start from 2025. The usage of partial automation should already be available from 2016 for “stop and go” situations on freeways at the speed of 30km/h. Similar predictions were made in the ITS roadmap of CLEPA that forecasts the implementation of highly automated driving between 2020 and 2025. The German VDA expects the implementation of the level 2 automation on a short term, and the level 3 on a mid-term. Even though the research progress is enormous and would respond to the predicted terms, there are significant legal boundaries that need to be amended. Also, considerable safety issues of AD are a challenge that can only be bridged by further development of environment monitoring, perception, and driver assistance enabled by smart components and systems. Intelligent Transportation Systems (ITS) are seen as an important enabler of AD in many of the roadmaps. Even though, as in the case of electrification of road transport, AD can be applied to all traffic participants like bikes, motorcycles, cars, trucks etc., the roadmap at hand concentrates only on the automation of passenger cars. This will simplify the analysis and enable constructive planning of tasks and timeframes, delivering the sphere of activities assignable to other systems. An extension to other vehicle classes and even a transfer of the concepts developed to other application domains, e.g. in manufacturing, or agriculture will be of great benefit.

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The Institute of Electrical and Electronics Engineers (IEEE) in 2014 released predictions that autonomous cars will account for up to 75 percent of vehicles on the road by the year 2040. The organization went even further, forecasting how infrastructure, society and attitudes could change when self-driving cars become the norm around the middle of the century. IEEE envisions an absence of traffic signs and lights since highly evolved, self-driving cars won’t need them, and it believes that full deployment could even eliminate the need for driver’s licenses. When asked to specify the year in which specific equipment will be removed from mass-produced cars, the majority of respondents believe rear-view mirrors, horns, and emergency brakes will be removed by 2030 and steering wheels and gas/brake pedals will follow by 2035. In addition, more than 75 percent of respondents also indicated that all 50 US states would pass legislation permitting use of driverless vehicles within this time period.

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Autonomous Vehicle Implementation Predictions: December 2015 report:

The analysis indicates that some benefits, such as independent mobility for affluent non-drivers, may begin in the 2020s or 2030s, but most impacts, including reduced traffic and parking congestion (and therefore road and parking facility supply requirements), independent mobility for low-income people (and therefore reduced need to subsidize transit), increased safety, energy conservation and pollution reductions, will only be significant when autonomous vehicles become common and affordable, probably in the 2040s to 2060s, and some benefits may require prohibiting human-driven vehicles on certain roadways, which could take longer.

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Roadmap to automated driving:

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Possible developments:

•By 2017, Mobileye expects to release autonomous capabilities for country roads.

•By 2018, Mobileye expects to release autonomous capabilities for city traffic as well as capabilities where driver is not required to be alert

•By 2018, Nissan anticipates to have a feature that can allow the vehicle manoeuver its way on multi-lane highways.

•By 2018, Baidu plans to release its fully autonomous system.

•By 2018, Elon Musk expects Tesla Motors to have developed mature serial production version of fully self-driving cars, where the driver can fall asleep. However, he expects they would be allowed only some years after that, due to regulatory issues.

•By 2020, Volvo envisages having cars in which passengers would be immune from injuries.  Volvo also claims vehicles will effectively be “crash free.”

•By 2020, Audi, BMW, Daimler, Ford, GM, Google, Kia, Mercedes-Benz, Nissan, Renault, Tesla, and Toyota all expect to sell vehicles that can drive themselves at least part of the time.

•By 2020, Google autonomous car project head’s goal to have all outstanding problems with the autonomous car be resolved.

•By 2024, Jaguar expects to release an autonomous car.

•By 2025, most new GM vehicles will have automated driving functions as well as vehicle-to-vehicle communication technology.

•By 2035, IHS Automotive report says will be the year most self-driving vehicles will be operated completely independently from a human occupant’s control.

•By 2035, Navigant Research forecasts that autonomous vehicles will gradually gain traction in the market over the coming two decades and by 2035, sales of autonomous vehicles will reach 95.4 million annually, representing 75% of all light-duty vehicle sales.

•By 2040, expert members of the Institute of Electrical and Electronics Engineers (IEEE) have estimated that up to 75% of all vehicles will be autonomous.

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Possible effects:

•Columbia University’s The Earth Institute forecasts the reduction of United States’ fleet of vehicles by a factor of 10.

•PricewaterhouseCoopers forecasts a reduction of traffic accidents by a factor of 10 and it concludes that the fleet of vehicles in the United States may collapse from 245 million to just 2.4 million.

•KPMG LLP and the Center for Automotive Research (CAR) foresee improvements in productivity and energy efficiency as well as new business models.

•Morgan Stanley estimates that autonomous cars could save the United States $1.3 trillion annually by lowering fuel consumption ($169 billion), reducing crash costs ($488 billion) and boosting productivity ($645 billion).

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Driverless trucks:

Media attention has created a real buzz about the possibilities of driverless vehicles and their potential. However, the logistics and regulations surrounding the issue means that widespread adoption is probably still decades away. As a result, trucks, not cars, are in pole position to form the world’s first network of truly driverless vehicles. Indeed Australian mining companies, under pressure to reduce costs and increase output without compromising safety, are already trialling Autonomous Hauling Systems (AHS) where trucks can load and dump ore and navigate haul roads without the presence of a driver. The trucks are fitted with high-precision GPS, obstacle detection systems, and a vehicle controller system, all of which is connected to a 4G LTE network, which permits real-time monitoring of operations to take place at a control centre thousands of kilometers away. One of the advantages of deploying LTE is the increased bandwidth available. It allows the use of multiple high-definition CCTV cameras even in areas susceptible to radio disturbance. The network also provides dependable quality of service management, high resiliency and cyber protection, and low latency of just 10ms, which is essential for a driverless vehicle requiring fast response times and real-time connections with the surrounding environment and operations centre. In addition, LTE contributes to reducing operations costs by utilizing an IP protocol which will enable mining companies to converge their communication systems and other applications onto single network architecture. This will provide greater operational efficiency. And with the solution scalable to meet new technologies and services as they become available, LTE can provide the foundation for adoption of M2M technologies and automation throughout all areas of operation, further helping mining companies to boost the efficiency of their operations. Machine to machine (M2M) is a broad label that can be used to describe any technology that enables networked devices to exchange information and perform actions without the manual assistance of humans. M2M and IoT are almost synonymous—the exception is IoT (the newer term) typically refers to wireless communications, whereas M2M can refer to any two machines—wired or wireless—communicating with one another. Indeed 4G LTE is the logical network on which to develop and deliver not only automated hauling systems but also entirely driverless freight trains for pit to port operations. While groups of driverless trucks for mining are arguably more straightforward applications than public road and city applications, they are no less innovative. Companies like Google and Apple may be generating the headlines, but heavy industry suppliers such as Caterpillar, Belaz, Komatsu and Liebherr are equally pioneering driverless technologies, and they are set to be the first to deliver meaningful results

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AVs accelerate robotics development for consumer applications:

The broad penetration of AVs will likely accelerate the development of robotics for consumer applications (including humanoid robots), since the two share many technologies. These include remote advanced sensing, hyperprecise positioning/GPS, image recognition, and advanced artificial intelligence. In addition to sharing technology, AVs and robots could benefit from using the same infrastructure, including recharging stations, service centers, and machine-to-machine communication networks. These commonalities might push multiple players to invest in both applications, as already shown by the significant investments in robotics made by selected automakers and high-tech players.

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Flying cars:

Flying cars have been part of our science-fiction dreams ever since Henry Ford pitched an early personal airplane back in 1926—Ford’s aircraft division actually tried to build a “Model T of the air.” Ninety years later, discarded prototypes litter junkyards and collectors’ garages, but no viable mass-market product has ever emerged. That might still change. The latest candidates include Skycar, a flying-car prototype, and the Ehang 184, an autonomous electric quadcopter introduced at the 2016 Consumer Electronics Show, in Las Vegas. In 2013, a company called Terrafugia announced plans for a self-flying car; it expects to have a prototype ready for testing by 2018. A commercial model will take at least another five years. When they do arrive, flying cars will likely cost at least several hundred thousand dollars. They may replace the Lamborghini or the Bentley as the status car of the superrich. But for most of us they’ll remain a dream, even if not a science-fiction one.

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Brain Powered Car Technology Unveiled:

Researchers in China have also followed the good path and have developed a mind-controlled car. Developed by researchers in the north-east port city of Tianjin, this is the country’s first car that uses nothing but brain power to drive. The research team from Nankai University, in the north-eastern Chinese port city of Tianjin, has spent two years bringing the mind-controlled vehicle to reality. By wearing brain signal-reading equipment a driver can control the car to go forward, backwards, come to a stop, and both lock and unlock the vehicle, all without moving their hands or feet. The equipment comprises of 16 sensors that capture EEG (electroencephalogram) signals from the driver’s brain. A computer program that selects the relevant signals and translates them, enabling control of the car. The tester’s EEG signals are picked up by this (brain signal-reading) equipment and transmitted wirelessly to the computer. The computer processes the signals to categorize and recognize people’s intention, then translates them into control command to the car. The core of the whole flow is to process the EEG signals, which is done on the computer. The researchers say their initial idea was inspired by helping disabled people who are physically unable to steer cars. There are two starting points of this project. The first one is to provide a driving method without using hands or feet for the disabled who are unable to move freely; and secondly, to provide healthy people with a new and more intellectualized driving mode. However, there lies the fear of potential road accidents caused by the driver being distracted because concentration was needed only when changing the vehicle’s moving status, i.e. changing lanes or turning. There are still a lot of wrongs to be undone and that’s one reason why you won’t see this technology make it to production just yet.

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Driverless Internet of things (IoT) connected cars:

Internet of things is a proposed development of the Internet in which everyday objects have network connectivity, allowing them to send and receive data. The auto industry is on the brink of a revolution, and the driving force is the suite of technologies known as the Internet of Things (IoT). With IoT applications—grounded in advances in everything from sensors to artificial intelligence to big-data analysis—all manner of objects, from wristwatches to road signs, can be not only connected but also “smart.” And both industry insiders and everyday drivers will soon see a fundamentally different world of mobility.  Automobile manufacturers such as BMW AG are now offering IoT-connected cars, and technology companies—including Google, Inc., Hortonworks, Inc., IBM and Uber—are helping companies gain valuable insights by offering solutions that analyze the real-time data streaming in from IoT-connected cars. Plus, Carnegie Mellon University and the University of Michigan have teamed up with automotive manufacturers, technology companies and local governments to create simulated cites designed expressly for testing these IoT-connected cars. Indeed, the IoT has become a powerful force for business transformation, and its disruptive impact is being felt not just in the automobile industry but across all industries. In fact, according to Gartner, Inc., 4.9 billion connected things will be in use in 2015, up 30 percent from 2014, and will reach 25 billion by 2020.

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A growing number of connected cars are on the road today. These cars have navigation systems, emergency call systems, internet access and wireless local networks. Some of these features are embedded from the factory, and others are brought into the car like a smart phone that connects you to your navigation systems, music and other content. In the near future, your behind-the-wheel experience and view will not change. However, the roll-out of V2X technology will provide secure communication between cars, roadside infrastructure and even people – this plays a huge part in the IoT. Each car will be more aware of its environment. Cars will know when the lights are about to change. Cars will “predict” if another car is heading into a collision. Securing and analyzing each of these connections as quickly as possible will be essential. Jump forward a few years and imagine how different your daily journey to work could be when fully connected cars (if they are still referred to as “cars”) autonomously navigate busy city streets and fast-moving highways. The reason you get in your car is still your end-destination. But now getting there is more fun, relaxing and flexible. You don’t have to focus on driving the car yourself. The car’s sensor fusion system, which uses radar, vision and V2X takes care of the driving for you.

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Traditional car window will become a work surface:

No more accidents, cheaper insurance premiums, running costs, zero emissions and significant reduction in commute times are all achieved. Your traditional car window becomes a viewing and work surface, flexible and fun to suit all occupants’ needs.

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Cars as mobile environmental monitoring station:

With the growing bank of onboard car sensors, the car could become a mobile environmental monitoring station. Providing real time secure information to local and national agencies allowing them to take action when required to improve the quality of the air we all breathe. The data the car is generating and collecting is now valuable and sharing that data securely through trusted partnerships will be paramount. We are working to establish what we believe are the necessary partnerships to provide secure solutions at every stage in the Automotive IoT.

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Even when the car is sitting in your driveway, it will be in communication with its manufacturer, using secure wireless technology. The information that is being shared is about the performance and maintenance of your car, with software updates being transferred over the air (OTA), to make your car more reliable, efficient and to fix any problems before they have an impact on your car’s drivability.

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Roadside infrastructure:

In the future, our town and city streets could fundamentally be transformed, as the need for traditional roadside infrastructure is first scaled back. The term road safety is then consigned to the history books as cars no longer cause accidents, by operating autonomously and being always connected to the IoT securely sharing information. Car ownership models are revolutionized. The car now becomes a mobile space that can be rented and used for what you want, when you want it. It is no longer just a mode of transport, but also a destination itself. Nobody knows for sure what the future will look like once Automotive IoT is fully established. However, there is consensus that significant change is coming.

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The moral of the story:

1. In spite of stronger cars, extra seat belts, and air bags, 1.25 million people are killed on the world’s roads every year i.e. one death every 25 seconds; and 90% of all car crashes are caused by driver mistakes. You either need to make a better driver, or take the driver and human error out of the equation all together. Road traffic injuries are currently estimated to be the ninth leading cause of death across all age groups globally, and are predicted to become the seventh leading cause of death by 2030.

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2. Motivations of automated driving include reduction in accidents and traffic jams, reduction in fuel consumption and noxious emissions, supporting urban mobility and boosting the flexibility of unconfident and elderly drivers.

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3. I divide cars into ordinary (driver must), semi-autonomous (driver must), self-driving (driver optional) and driverless (no driver) depending on degree of autonomy and technological advancement; although various authors have used the terms autonomous, automated, driverless, self-driving and robotic car synonymously. Advanced driver assistance systems (ADAS) include adaptive cruise control, automated braking, automated parking, blind spot detection, lane departure warning systems etc. The purpose of ADAS is to reduce driver error, reduce accidents and increase efficiency in traffic and transport. Any car that has ADAS is autonomous car and they are divided into semi-autonomous (having ADAS but need human to drive) and fully autonomous. Fully autonomous cars are further divided into user-operated (self-driving) and driverless cars. Only semi-autonomous cars are there on the roads for commercial use in 2016. In designing fully autonomous car, leading developers disagree whether the vehicle should be without a driver or driver “optional.”

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4. Driverless car technology is not a farfetched, futuristic concept but a continuum of technology and a continuum of autonomy, and many of the technologies are already available in today’s car as ADAS (semi-autonomous cars). A driverless car is capable of sensing its environment and navigating without human input. A driverless car has an autopilot system allowing it to safely move from one place to another without help from a human driver. Ideally, the only role of a human in such a vehicle would be indicating the destination. Driverless cars could be available in less than a decade where computers control steering, braking, and accelerating, and free human drivers to work, text, or just relax.

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5. Operating a car on public roads is more complex than flying an airplane due to the frequency and proximity of interactions with often unpredictable objects including other vehicles, pedestrians, animals, buildings, trash and potholes. Autopilot of airplane operates unilaterally, so if another airplane gets in its way, they will collide; on the other hand, autopilot of driverless car would detect another car in its way and swerve or stop.

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6. Environmental inputs to driverless car comes through GPS, Lidar, radar, video camera, sonar and internet; and these inputs are integrated by sensor fusion and computer vision to be fed to artificial intelligence of on-board computer which ultimately drives the car. Driverless car may be called ‘Computer Car’.

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7. Driverless car artificial intelligence is self-learning in the sense that the algorithms used in operating a car modify themselves over time in response to previous operations, new information, and feedback. Furthermore, all the cars learn from every trip by every other car and they never forget. Self-learning algorithms are characterized by their dynamic adaptability. Therefore driverless cars are not programmed in the classical sense; they need to learn. We should avoid conceptualizing driverless cars as machines which are controlled by a detailed, exactly specified and in principle comprehensible software program. Instead we should conceptualize their behaviour as being the result of a long and varied program of learning. The capability of such cars can be analysed through simulation and testing but not just by examining its source code. The artificial intelligence has to be at least as accurate and reliable as human intelligence engaged in the driving.

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8. Driverless car’s computers are learning, the programmers are learning and the people are learning to get used to these things. Driverless car may change not only the way we drive but also how we use time and how urban landscapes are developed.

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9. Driverless cars have to stick to speed limits, because it would be illegal to program them otherwise. But practically no one on the road sticks to the speed limit, so driverless car is stuck in the slow lane when mixed with human driven cars. However, when surrounding human driven cars are breaking the speed limit and going more slowly could actually present a danger, then driverless car would accelerate to keep up.

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10. Driverless car ‘black-box’ would continuously and thoroughly record the vehicle’s operating performance data prior to accident to clearly identifying the cause of accident although some may object to it based on concerns about privacy. Loss of privacy also occurs via sharing of information through V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols.

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11. Technologically enhanced roads lag behind technologically enhanced vehicles due to cost and scalability. Our infrastructure including roads and cities are not ready for driverless cars.

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12. Technologically ideal driverless car would have convergence of sensor based and connected vehicle [vehicle to vehicle (V2V) plus vehicle to infrastructure (V2I)] solutions, driven on technologically enhanced roads.

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13. Driverless cars do not make judgments based on value; they make them based on protocol. We cannot expect a driverless car to make an ethical choice that no human is capable of making. All we expect is that driverless car should not make a wrong decision that a human is capable of making in split-second. Given the near-infinite number of potential situations that can result in an accident, driverless cars will need to decide instantaneously on a course of action when presented with multiple less-than-ideal outcomes and it may be programmed to select a path with the lowest damage or likelihood of collision.

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14. Major obstacles to mass adoption of driverless cars include cost, technology, infrastructure, consumer acceptance and policy including liability, regulation, insurance and legislation.

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15. The biggest obstacle to driverless car is how to endow the machines with the sort of everyday knowledge that humans acquire and use from childhood on. To have the car understand every single possibility is a massive challenge. Technology is the biggest barrier separating driverless cars from the consumer market. Technological barriers include problems of mapping system; issues that would prevent it from driving in heavy snow or rain; its inability to tell the color of traffic lights when sensors are blinded by sun or glare; they can’t handle parking garages because they can’t get a GPS signal; sensors detect objects as pixelated shapes, so the car would respond the same way – by swerving – to dodge a child on the road, or a newspaper that was floating past; they can’t see “unmapped” traffic lights or stop signs; they can’t detect uncovered manholes or potholes; they can’t detect police or construction worker’s hand gestures to tell a car to either go or to stop, they cannot figure out or predict behaviours of unpredictable humans like other drivers, pedestrians, cyclists; and they cannot make eye contact and subtle communication in different driving situations. On the top of it, technology is prone to hacking, computer virus and cyber-attacks.  It is almost impossible to make infallible driverless car and to predict human behaviour in interactions with highly automated systems.

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16. The more time people spend in their cars, the higher the incentive to purchase a fully autonomous vehicle.  Fully autonomous technology dramatically increases the return on investment and will therefore lead to rapid adoption.

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17. Although driverless cars, electric vehicles and car-sharing services are distinct business categories with unique technical, strategic and market challenges, there is a positive feedback cycle between them, each reinforcing the others, and together they massively disrupt the technology and the business of personal mobility.

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18. Driverless cars are immune from road rage, distracted drivers, drunken driving, speeding, aggressive driving, drowsiness, tiredness, carelessness, blind spots, inexperience, slow reaction time and ignoring road conditions; therefore safer than human drivers. However driverless cars may be no safer than an average driver and may increase total crashes when driverless and human-driven vehicles are mixed because driverless cars do not mix well with human drivers. Even if driverless cars can learn to interact with human-driven cars, human drivers will not be able to deal with driverless cars. The resulting confusion would lead to more accidents and congestion, rather than less.  Also not all fatalities are caused by drivers – therefore, replacing them with a computer, even an infallible one, may not prevent all deaths.

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19. Although Google self-driving car’s accident rates are twice as high as that of regular cars, Google cars have never been at fault: they’re usually hit from behind in slow-speed crashes by inattentive or aggressive humans unaccustomed to machine motorists that always follow the rules and proceed with caution.

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20. Although human drivers aren’t perfect, they drive 100 million miles for every fatality. The robot system has to perform at least at that level. It is anticipated that driverless cars will prevent 95% of all traffic collisions – but experts say the technology won’t be perfected until 2050, when driverless car would have become common and affordable.

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21. It isn’t yet legal to operate a self-driving car, except for experimental purposes. And even this use is limited to few states in the U.S. and on a per-vehicle-basis in Germany. Also a driver must be present in the car at all times, according to current laws. Also driver is responsible for the accidents caused by semi-autonomous or self-driving cars although manufacturers can still be held liable for negligence if there is evidence that an accident was caused by a product defect.

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22. Any reduction in traffic congestion and saving fuel by driverless cars will be offset by rebound effects i.e. increased vehicle travel resulting from faster or cheaper travel. However driverless car facilitate car-sharing, which allows households to reduce vehicle ownership and therefore total driving.

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23. Today private cars are used less than 5% of the time and remaining 95 % of the time parked. Shared driverless cars (driverless taxies) would vastly increase car utility time to as much as 75%. Driverless taxis could operate at as little as 20% of the cost of individual car ownership with enhanced convenience, relaxed travel, shorter travel time and improved safety; and eliminate much of the need for parking spaces, freeing up valuable space for other more attractive uses. Driverless electric cars involved in car-sharing services would work out to be cheaper and greener.

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24. Rape and molestation of a girl/woman passenger by driver and his associates in cars/buses is on the rise in India. Driverless taxies would solve this problem.

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25. It should be ensured that driverless cars do not harm people who do not use such cars; for example walking and cycling on roads, conventional public transit service and human-driven regular cars.

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26. Acceptance of driverless cars seems to be especially strong in developing nations (India, Brazil and China) than developed nations (Germany and Japan) although paradoxically road and city infrastructure necessary for driverless car is deficient in developing nations.

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Dr. Rajiv Desai. MD.

June 12, 2016

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Postscript:

Much of the discussion around driverless cars is in the “complete imagined future” mode where our infrastructure including roads & cities, and cultural expectations has already been reorganized around their potential and needs. The “complete imagined future” mode denies the problems associated with evolving the future condition. In my view, driverless car dream with all benefits would be realized only when all cars on roads will be electric driverless connected cars running on technologically enhanced roads in technologically enhanced cities along with driverless buses and trucks.

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Created by Webroute-Solutions