An autonomous car (also known as a driverless car, auto, self-driving car, robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Many such vehicles are being developed, but as of May 2017 automated cars permitted on public roads are not yet fully autonomous. They all require a human driver at the wheel who is ready at a moment's notice to take control of the vehicle.
Autonomous cars use a variety of techniques to detect their surroundings, such as radar, laser light, 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.
Some demonstrative systems, precursory to autonomous cars, date back to the 1920s and 1930s. The first self-sufficient (and therefore, 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. A major milestone was achieved in 1995, with CMU's NavLab 5 completing the first autonomous coast-to-coast drive of the United States. Of the 2,849 miles between Pittsburgh, PA and San Diego, CA, 2,797 miles were autonomous (98.2%), completed with an average speed of 63.8 miles per hour (102.3 km/h). Since then, numerous major companies and research organizations have developed working prototype autonomous vehicles.
Among the potential benefits of autonomous cars is a significant reduction in traffic collisions; the resulting injuries; and related costs, including a lower need for insurance. Autonomous cars are also predicted to offer major increases in traffic flow; enhanced mobility for children, the elderly, disabled and poor people; the relief of travelers from driving and navigation chores; lower fuel consumption; significantly reduced needs for parking space in cities; a reduction in crime; and the facilitation of different business models for mobility as a service, especially those involved in the sharing economy.
Among the main obstacles to widespread adoption of autonomous vehicles, in addition to the technological challenges, are disputes concerning liability; the time period needed to turn an existing stock of vehicles from non-autonomous to autonomous; resistance by individuals to forfeit control of their cars; consumer concern about the safety of driverless cars; implementation of legal framework and establishment of government regulations for self-driving cars; risk of loss of privacy and security concerns, such as hackers or terrorism; concerns about the resulting loss of driving-related jobs in the road transport industry; and risk of increased suburbanization as driving becomes faster and less onerous without proper public policies in place to avoid more urban sprawl. Many of these issues are due to the fact that Autonomous Things such as autonomous vehicles (and self-navigating drones) are allowing, for the first time, the computers to roam freely, with all the related safety and security concerns.
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 behavior or capabilities as autonomous; for example Wood et al. (2012) writes "This Article generally uses the term 'autonomous,' instead of 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".
A classification system based on six different levels (ranging from none to fully automated systems) was published in 2014 by SAE International, an automotive standardization body, as J3016, Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. This classification system is based on the amount of driver intervention and attentiveness required, rather than the vehicle capabilities, although these are very closely related. In the United States in 2013, the National Highway Traffic Safety Administration (NHTSA) released a formal classification system, but abandoned this system when it adopted the SAE standard in September 2016.
SAE automated vehicle classifications:
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Modern self-driving cars generally use Bayesian Simultaneous localization and mapping (SLAM) algorithms, which fuse data from multiple sensors and an off-line map into current location estimates and map updates. SLAM with detection and tracking of other moving objects (DATMO), which also handles things such as cars and pedestrians, is a variant being developed by research at Google . Simpler systems may use roadside real-time locating system (RTLS) beacon systems to aid localisation. Typical sensors include lidar and stereo vision, GPS and IMU. Visual object recognition uses machine vision including neural networks. Educator Udacity is developing an open-source software stack.
Testing vehicles with varying degrees of autonomy can be done physically, in closed environments, on public roads (where permitted, typically with a license or permit or adhering to a specific set of operating principles) or virtually, i.e. in computer simulations.
When driven on public roads, the vehicles require at least one person to monitor their proper operation and "take over" when needed. Three of the best-known testing programs are:
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, Hyundai Motor Company, Volvo, Tesla Motors, Peugeot, Local Motors, 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, five US states (Nevada, Florida, California, Virginia, and Michigan) together with Washington, D.C. allowed the 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.
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. New Zealand is also planning to use Autonomous Vehicles to solve its public transport problems in Tauranga and Christchurch. 
Among the anticipated benefits of automated cars is the potential reduction in traffic collisions (and resulting deaths and injuries and costs), caused by human-driver errors, such as delayed reaction time, tailgating, rubbernecking, and other forms of distracted or aggressive driving. Consulting firm McKinsey & Company estimated that widespread use of autonomous vehicles could "eliminate 90% of all auto accidents in the United States, prevent up to US$190 billion in damages and health-costs annually and save thousands of lives."
If a human driver isn't required, automated cars could also reduce labor costs; relieve travelers from driving and navigation chores, thereby replacing behind-the-wheel commuting hours with more time for leisure or work; and also would lift constraints on occupant ability to drive, distracted and texting while driving, intoxicated, prone to seizures, or otherwise impaired. For the young, the elderly, people with disabilities, and low-income citizens, autonomous cars could provide enhanced mobility.
Additional advantages could include higher speed limits; smoother rides; and increased roadway capacity; and minimized traffic congestion, due to decreased need for safety gaps and higher speeds. Currently, maximum controlled-access highway throughput or capacity according to the U.S. Highway Capacity Manual is about 2,200 passenger vehicles per hour per lane, with about 5% of the available road space is taken up by cars. According to a study by researchers at Columbia University, autonomous cars could increase capacity by 273% (~8,200 cars per hour per lane). The study also estimated that with 100% connected vehicles using vehicle-to-vehicle communication, capacity could reach 12,000 passenger vehicles per hour (up 445% from 2,200 pc/h per lane) traveling safely at 120 km/h (75 mph) with a following gap of about 6 m (20 ft) of each other. Currently, at highway speeds drivers keep between 40 to 50 m (130 to 160 ft) away from the car in front. These increases in highway capacity could have a significant impact in traffic congestion, particularly in urban areas, and even effectively end highway congestion in some places.
There would also be an improved ability to manage traffic flow, combined with less need for traffic police, vehicle insurance; or even road signage, since automated cars could receive necessary communication electronically (although roadway signage may still be needed for any human drivers on the road). Reduced traffic congestion and the improvements in traffic flow due to widespread use of autonomous cars will also translate into better fuel efficiency.
Widespread adoption of autonomous cars could reduce the needs of road and parking space in urban areas, freeing scarce land for other uses such as parks, public spaces, retail outlets, housing, and other social uses. Some academics think it could also contribute, along with automated mass transit, to make dense cities much more efficient and livable.
The vehicles' increased awareness could reduce car theft, while the removal of the steering wheel—along with the remaining driver interface and the requirement for any occupant to assume a forward-facing position—would give the interior of the cabin greater ergonomic flexibility. Large vehicles, such as motorhomes, would attain appreciably enhanced ease of use.
When used for carsharing, the total number of cars is reduced. Furthermore, new business models (such as mobility as a service) can develop, which aim to be cheaper than car ownership by removing the cost of the driver. Finally, the robotic car could drive unoccupied to wherever it is required, such as to pick up passengers or to go in for maintenance (eliminating redundant passengers).
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In spite of the various benefits to increased vehicle automation, some foreseeable challenges persist, such as disputes concerning liability, the time needed to turn the existing stock of vehicles from nonautonomous to autonomous, resistance by individuals to forfeit control of their cars, customer concern about the safety of driverless cars, and the implementation of legal framework and establishment of government regulations for self-driving cars. Other obstacles could be missing driver experience in potentially dangerous situations, ethical problems in situations where an autonomous car's software is forced during an unavoidable crash to choose between multiple harmful courses of action, and possibly insufficient Adaptation to Gestures and non-verbal cues by police and pedestrians.
Possible technological obstacles for autonomous cars are:
A direct impact of widespread adoption of autonomous vehicles is the loss of driving-related jobs in the road transport industry. There could be resistance from professional drivers and unions who are threatened by job losses. In addition, there could be job losses in public transit services and crash repair shops. The automobile insurance industry might suffer as the technology makes certain aspects of these occupations obsolete.
Potential loss of privacy and risks of hacking. Sharing of information through V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols. There is also the risk of terrorist attacks. Self-driving cars could potentially be loaded with explosives and used as bombs.
The lack of stressful driving, more productive time during the trip, and the potential savings in travel time and cost could become an incentive to live far away from cities, where land is cheaper, and work in the city's core, thus increasing travel distances and inducing more urban sprawl, more fuel consumption and an increase in the carbon footprint of urban travel. There is also the risk that traffic congestion might increase, rather than decrease. Appropriate public policies and regulations, such as zoning, pricing, and urban design are required to avoid the negative impacts of increased suburbanization and longer distance travel.
Research shows that drivers in autonomous cars react later when they have to intervene in a critical situation, compared to if they were driving manually.
In mid‑October 2015 Tesla Motors rolled out version 7 of their software in the U.S. that included Tesla Autopilot capability. On 9 January 2016, Tesla rolled out version 7.1 as an over-the-air update, adding a new "summon" feature that allows cars to self-park at parking locations without the driver in the car. Tesla's autonomous driving features are ahead of others in the industry, and can be classified as somewhere between level 2 and level 3 under the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) five levels of vehicle automation. At this level the car can act autonomously but requires the full attention of the driver, who must be prepared to take control at a moment's notice. Autopilot should be used only on limited-access highways, and sometimes it will fail to detect lane markings and disengage itself. In urban driving the system will not read traffic signals or obey stop signs. The system also does not detect pedestrians or cyclists.
The first fatal accident involving a vehicle being driven by itself took place in Williston, Florida on 7 May 2016 while a Tesla Model S electric car was engaged in Autopilot mode. The occupant was killed in a crash with an 18-wheel tractor-trailer. On 28 June 2016 the National Highway Traffic Safety Administration (NHTSA) opened a formal investigation into the accident working with the Florida Highway Patrol. According to the NHTSA, preliminary reports indicate the crash occurred when the tractor-trailer made a left turn in front of the Tesla at an intersection on a non-controlled access highway, and the car failed to apply the brakes. The car continued to travel after passing under the truck’s trailer. The NHTSA's preliminary evaluation was opened to examine the design and performance of any automated driving systems in use at the time of the crash, which involved a population of an estimated 25,000 Model S cars. On 8 July 2016, the NHTSA requested Tesla Motors provide the agency detailed information about the design, operation and testing of its Autopilot technology. The agency also requested details of all design changes and updates to Autopilot since its introduction, and Tesla's planned updates schedule for the next four months.
According to Tesla, “neither autopilot nor the driver noticed the white side of the tractor-trailer against a brightly lit sky, so the brake was not applied.” The car attempted to drive full speed under the trailer, “with the bottom of the trailer impacting the windshield of the Model S.” Tesla also stated that this was Tesla’s first known autopilot death in over 130 million miles (208 million km) driven by its customers with Autopilot engaged. According to Tesla there is a fatality every 94 million miles (150 million km) among all type of vehicles in the U.S. However, this number also includes fatalities of the crashes, for instance, of motorcycle drivers with pedestrians.
In July 2016 the U.S. National Transportation Safety Board (NTSB) opened a formal investigation into the fatal accident while the Autopilot was engaged. The NTSB is an investigative body that only has the power to make policy recommendations. An agency spokesman said "It's worth taking a look and seeing what we can learn from that event, so that as that automation is more widely introduced we can do it in the safest way possible.". In January 2017, the NTSB released the report that concluded Tesla was not at fault; the investigation revealed that the Tesla car crash rate dropped by 40 percent after Autopilot was installed.
According to Tesla, starting 19 October 2016, all Tesla cars are built with hardware to allow full self-driving capability at the highest safety level (SAE Level 5). The hardware includes eight surround cameras and twelve ultrasonic sensors, in addition to the forward-facing radar with enhanced processing capabilities. The system will operate in "shadow mode" (processing without taking action) and send data back to Tesla to improve its abilities until the software is ready for deployment via over-the-air upgrades. After the required testing, Tesla hopes to enable full self-driving by the end of 2017 under certain conditions.
In August 2012, Google announced that their self-driving car had completed over 300,000 autonomous-driving miles (500,000 km) accident-free, typically having about a dozen cars on the road at any given time, and were starting to test them with single drivers instead of in pairs. In late-May 2014, Google revealed a new prototype of its driverless car, which had no steering wheel, gas pedal, or brake pedal, and was fully autonomous. As of March 2016[update], Google had test-driven their fleet of driverless cars in autonomous mode a total of 1,500,000 mi (2,400,000 km). In December 2016, Alphabet (Google's parent company) announced that the self-driving car technology would be spun-off to a new company called Waymo.
Based on Google's own accident reports, their test cars have been involved in 14 collisions, of which other drivers were at fault 13 times. It was not until 2016 that the car's software caused a crash.
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 in which another driver rolled through a stop sign, and one where a Google employee was controlling the car manually. 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. On 14 February 2016 a Google self-driving car attempted to avoid sandbags blocking its path. During the maneuver 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.” Google characterized the crash as a misunderstanding and a learning experience.
In March 2017, a self-driving Uber car was involved in an accident in Tempe, Arizona when another car failed to yield, resulting in the Uber vehicle flipping over.
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 behavior. 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.
Other disruptive effects will come from the use of autonomous vehicles to carry goods. Self-driving vans have the potential to make home deliveries significantly cheaper, transforming retail commerce and possibly rendering hypermarkets and supermarkets redundant. As of right now the U.S. Government defines automation into six levels, starting at level zero which means the human driver does everything and ending with level five, the automated system performs all the driving tasks. Also under the current law, manufacturers bear all the responsibility to self-certify vehicles for use on public roads. This means that currently as long as the vehicle is compliant within the regulatory framework, there are no specific federal legal barriers to a highly automated vehicle being offered for sale. Iyad Rahwan, an associate professor in the MIT Media lab said, “Most people want to live in a world where cars will minimize casualties, but everyone wants their own car to protect them at all costs.” Furthermore, industry standards and best practice are still needed in systems before they can be considered reasonably safe under real-world conditions.
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. In 2016, 7 states (Nevada, California, Florida, Michigan, Hawaii, Washington, and Tennessee), along with the District of Columbia, have enacted laws for autonomous vehicles. After the first fatal accident by Tesla's Autopilot system, revising laws or standards for autonomous car is carefully discussed globally.
In September 2016, the US National Economic Council and Department of Transportation released federal standards that describe how automated vehicles should react if their technology fails, how to protect passenger privacy, and how riders should be protected in the event of an accident. The new federal guidelines are meant to avoid a patchwork of state laws, while avoiding being so overbearing as to stifle innovation.
In June 2011, the Nevada Legislature passed a law to authorize the use of autonomous cars. Nevada thus became the first jurisdiction in the world where autonomous vehicles might be legally operated on public roads. According to the law, the Nevada Department of Motor Vehicles (NDMV) is responsible for setting safety and performance standards and the agency is responsible for designating areas where autonomous cars may be tested. This legislation was supported by Google in an effort to legally conduct further testing of its Google driverless car. The Nevada law defines an autonomous vehicle to be "a motor vehicle that uses artificial intelligence, sensors and global positioning system coordinates to drive itself without the active intervention of a human operator." The law also acknowledges that the operator will not need to pay attention while the car is operating itself. Google had further lobbied for an exemption from a ban on distracted driving to permit occupants to send text messages while sitting behind the wheel, but this did not become law. Furthermore, Nevada's regulations require a person behind the wheel and one in the passenger’s seat during tests.
In 2013, the government of the United Kingdom permitted the testing of autonomous cars on public roads. Prior to this, all testing of robotic vehicles in the UK had been conducted on private property.
In 2014 the Government of France announced that testing of autonomous cars on public roads would be allowed in 2015. 2000 km of road would be opened through the national territory, especially in Bordeaux, in Isère, Île-de-France and Strasbourg. At the 2015 ITS World Congress, a conference dedicated to intelligent transport systems, the very first demonstration of autonomous vehicles on open road in France was carried out in Bordeaux in early October 2015.
In spring of 2015, the Federal Department of Environment, Transport, Energy and Communications in Switzerland (UVEK) allowed Swisscom to test a driverless Volkswagen Passat on the streets of Zurich.
On 19 February 2016, Assembly Bill No. 2866 was introduced in California that would allow completely autonomous vehicles to operate on the road, including those without a driver, steering wheel, accelerator pedal, or brake pedal. The Bill states the Department of Motor Vehicles would need to comply with these regulations by 1 July 2018 for these rules to take effect. This bill has yet to pass the house of origin.
In 2016, the Singapore Land Transit Authority in partnership with UK automotive supplier Delphi Automotive Plc will launch preparations for a test run of a fleet of automated taxis for an on-demand autonomous cab service to take effect in 2017.
In December 2016, the California Department of Motor Vehicles ordered Uber to remove its self driving vehicles from the road in response to two red-light violations. Uber immediately blamed the violations on "human-error", and has suspended the drivers.
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.
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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.
A 2013 survey of 1,500 consumers across 10 countries by Cisco Systems found 57% "stated they would be likely to ride in a car controlled entirely by technology that does not require a human driver", with Brazil, India and China the most willing to trust autonomous technology.
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. 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.
In 2016, a survey in Germany examined the opinion of 1,603 people, who were representative in terms of age, gender, and education for the German population, towards partially, highly, and fully automated cars. Results showed that men and women differ in their willingness to use them. Men felt less anxiety and more joy towards automated cars, whereas women showed the exact opposite. The gender difference towards anxiety was especially pronounced between young men and women but decreased with participants’ age.
In 2016, a PwC survey, in the United States, showing the opinion of 1,584 people, highlights that "66 percent of respondents said they think autonomous cars are probably smarter than the average human driver". People are still worried about safety and mostly the fact of having the car hacked. Nevertheless, only 13% of the interviewees see no advantages in this new kind of cars.
With the emergence of autonomous cars, there are various ethical issues arising. While morally, the introduction of autonomous vehicles to the mass market seems inevitable due to a reduction of crashes by up to 90% and their accessibility to disabled, elderly, and young passengers, there still remain some ethical issues that have not yet been fully solved. Those include, but are not limited to: The moral, financial, and criminal responsibility for crashes, the decisions a car is to make right before a (fatal) crash, privacy issues, and potential job loss.
There are different opinions on who should be held liable in case of a crash, in particular with people being hurt. Many experts see the car manufacturers themselves responsible for those crashes that occur due to a technical malfunction or misconstruction. Besides the fact that the car manufacturer would be the source of the problem in a situation where a car crashes due to a technical issue, there is another important reason why car manufacturers could be held responsible: it would encourage them to innovate and heavily invest into fixing those issues, not only due to protection of the brand image, but also due to financial and criminal consequences. However, there are also voices that argue those using or owning the vehicle should be held responsible since they lastly know the risk that involves using such a vehicle. Experts suggest introducing a tax or insurances that would protect owners and users of autonomous vehicles of claims made by victims of an accident. Other possible parties that can be held responsible in case of a technical failure include software engineers that programmed the code for the autonomous operation of the vehicles, and suppliers of components of the AV.
Taking aside the question of legal liability and moral responsibility, the question arises how autonomous vehicles should be programmed to behave in an emergency situation where either passengers or other traffic participants are endangered. A very visual example of the moral dilemma that a software engineer or car manufacturer might face in programming the operating software is described in an ethical thought experiment, the trolley problem: a conductor of a trolley has the choice of staying on the planned track and running over 5 people, or turn the trolley onto a track where it would only kill one person, assuming there is no traffic on it. There are two main considerations that need to be addressed. First, on what moral basis would the decisions an autonomous vehicle would have to make be based on. Second, how could those be translated into software code. Researchers have suggested, in particular, two ethical theories to be applicable to the behavior of autonomous vehicles in cases of emergency: deontology and utilitarianism. Asimov’s three laws of robotics are a typical example of deontological ethics. The theory suggests that an autonomous car needs to follow strict written-out rules that it needs to follow in any situation. Utilitarianism suggests the idea that any decision must be made based on the goal to maximize utility. This needs a definition of utility which could be maximizing the number of people surviving in a crash. Critics suggest that autonomous vehicles should adapt a mix of multiple theories to be able to respond morally right in the instance of a crash.
Privacy-related issues arise mainly from the interconnectivity of autonomous cars, making it just another mobile device that can gather any information about an individual. This information gathering ranges from tracking of the routes taken, voice recording, video recording, preferences in media that is consumed in the car, behavioral patterns, to many more streams of information.
The implementation of autonomous vehicles to the mass market might cost up to 5 million jobs in the US alone, making up almost 3% of the workforce. Those jobs include drivers of taxis, buses, vans, trucks, and e-hailing vehicles. Many industries, such as the auto insurance industry are indirectly affected. This industry alone generates an annual revenue of about $220 billions, supporting 277,000 jobs. To put this into perspective – this is about the number of mechanical engineering jobs. The potential loss of a majority of those jobs due to an estimated decline of accidents by up to 90% will have a tremendous impact on those individuals involved. However, new jobs will be created, e.g. due to a higher demand for programmers to program the necessary software.
The film Eagle Eye ( 2008 ) Shia LaBeouf and Michelle Monaghan are driven around in a Porsche Cayenne that is controlled by ARIIA ( a giant supercomputer ).
Intelligent or self-driving cars are a common theme in science fiction literature. Examples include:
This is the first known fatality in just over 130 million miles where Autopilot was activated. Among all vehicles in the US, there is a fatality every 94 million miles. Worldwide, there is a fatality approximately every 60 million miles.
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