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A self-driving car, also known as an autonomous vehicle (AV), connected and autonomous vehicle (CAV), driverless car, robo-car, or robotic car, is a vehicle that is capable of sensing its environment and moving safely with little or no human input.
Self-driving cars combine a variety of sensors to perceive their surroundings, such as radar, lidar, sonar, GPS, odometry and inertial measurement units. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.
- 1 History
- 2 Definitions
- 3 Legal definition
- 4 Semi-automated vehicles
- 5 Technical challenges
- 6 Nature of the digital technology
- 7 Human factor challenges
- 8 Testing
- 9 Fields of application
- 10 Impact
- 11 Potential limits or obstacles
- 12 Potential changes for different industries
- 13 Incidents
- 14 Policy implications
- 15 Vehicle communication systems
- 16 Public opinion surveys
- 17 Moral issues
- 18 Anticipated launch of cars
- 19 In fiction
- 20 See also
- 21 References
- 22 Further reading
Experiments have been conducted on automated driving systems (ADS) since at least the 1920s; trials began in the 1950s. The first semi-automated car was developed in 1977, by Japan's Tsukuba Mechanical Engineering Laboratory, which required specially marked streets that were interpreted by two cameras on the vehicle and an analog computer. The vehicle reached speeds up to 30 kilometres per hour (19 mph) with the support of an elevated rail.
A landmark autonomous car appeared in the 1980s, with Carnegie Mellon University's Navlab and ALV projects funded by the United States' Defense Advanced Research Projects Agency (DARPA) starting in 1984 and Mercedes-Benz and Bundeswehr University Munich's EUREKA Prometheus Project in 1987. By 1985, the ALV had demonstrated self-driving speeds on two-lane roads of 31 kilometres per hour (19 mph) with obstacle avoidance added in 1986 and off-road driving in day and nighttime conditions by 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 mi (4,585 km) between Pittsburgh, Pennsylvania and San Diego, California, 2,797 mi (4,501 km) were autonomous (98.2%), completed with an average speed of 63.8 mph (102.7 km/h). From the 1960s through the second DARPA Grand Challenge in 2005, automated vehicle research in the United States was primarily funded by DARPA, the US Army, and the US Navy, yielding incremental advances in speeds, driving competence in more complex conditions, controls, and sensor systems. Companies and research organizations have developed prototypes.
The US allocated US$650 million in 1991 for research on the National Automated Highway System, which demonstrated automated driving through a combination of automation, embedded in the highway with automated technology in vehicles and cooperative networking between the vehicles and with the highway infrastructure. The program concluded with a successful demonstration in 1997 but without clear direction or funding to implement the system on a larger scale. Partly funded by the National Automated Highway System and DARPA, the Carnegie Mellon University Navlab drove 4,584 kilometres (2,848 mi) across America in 1995, 4,501 kilometres (2,797 mi) or 98% of it autonomously. Navlab's record achievement stood unmatched for two decades until 2015 when Delphi improved it by piloting an Audi, augmented with Delphi technology, over 5,472 kilometres (3,400 mi) through 15 states while remaining in self-driving mode 99% of the time. In 2015, the US states of Nevada, Florida, California, Virginia, and Michigan, together with Washington, DC, allowed the testing of automated cars on public roads.
From 2016 to 2018, the European Commission funded the innovation strategy development for connected and automated driving through the Coordination Actions CARTRE and SCOUT . Moreover, the Strategic Transport Research and Innovation Agenda (STRIA) Roadmap for Connected and Automated Transport was published in 2019.
In 2017, Audi stated that its latest A8 would be automated at speeds of up to 60 kilometres per hour (37 mph) using its "Audi AI". The driver would not have to do safety checks such as frequently gripping the steering wheel. The Audi A8 was claimed to be the first production car to reach Level 3 automated driving, and Audi would be the first manufacturer to use laser scanners in addition to cameras and ultrasonic sensors for their system.
In November 2017, Waymo announced that it had begun testing driverless cars without a safety driver in the driver position; however, there was still an employee in the car. In October 2018, Waymo announced that its test vehicles had traveled in automated mode for over 10,000,000 miles (16,000,000 km), increasing by about 1,000,000 miles (1,600,000 kilometres) per month. In December 2018, Waymo was the first to commercialize a fully autonomous taxi service in the US
A*STAR's Institute for Infocomm Research (I2R) has developed a self-driving vehicle which was the first to be approved in Singapore for public road testing at one-north in July 2015. It has ferried several dignitaries such as Prime Minister Lee Hsien Loong, Minister S. Iswaran, Minister Vivian Balakrishnan, and several ministers from other countries.
There is some inconsistency in the terminology used in the self-driving car industry. Various organizations have proposed to define an accurate and consistent vocabulary.
Such confusion has been documented in SAE J3016 which states that "Some vernacular usages associate autonomous specifically with full driving automation (Level 5), while other usages apply it to all levels of driving automation, and some state legislation has defined it to correspond approximately to any ADS [automated driving system] at or above Level 3 (or to any vehicle equipped with such an ADS)."
Terminology and safety considerations
Modern vehicles provide partly automated features such as keeping the car within its lane, speed controls or emergency braking. Nonetheless, differences remain between a fully autonomous self-driving car on one hand and driver assistance technologies on the other hand. According to the BBC, confusion between those concepts leads to deaths.
The Association of British Insurers considers the usage of the word autonomous in marketing for modern cars to be dangerous because car ads make motorists think 'autonomous' and 'autopilot' means a vehicle can drive itself when they still rely on the driver to ensure safety. Technology alone still is not able to drive the car.
When some car makers suggest or claim vehicles are self-driving, when they are only partly automated, drivers risk becoming excessively confident, leading to crashes, while fully self-driving cars are still a long way off in the UK.
Autonomous vs. automated
Autonomous means self-governing. Many historical projects related to vehicle automation have been automated (made automatic) subject to a heavy reliance on artificial aids in their environment, such as magnetic strips. Autonomous control implies satisfactory performance under significant uncertainties in the environment and the ability to compensate for system failures without external intervention.
One approach is to implement communication networks both in the immediate vicinity (for collision avoidance) and farther away (for congestion management). Such outside influences in the decision process reduce an individual vehicle's autonomy, while still not requiring human intervention.
Wood et al. (2012) wrote, "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." As of 2017, most commercial projects focused on automated vehicles that did not communicate with other vehicles or with an enveloping management regime. EuroNCAP defines autonomous in "Autonomous Emergency Braking" as: "the system acts independently of the driver to avoid or mitigate the accident." which implies the autonomous system is not the driver.
Autonomous versus cooperative
To enable a car to travel without any driver embedded within the vehicle, some companies use a remote driver.
According to SAE J3016,
Some driving automation systems may indeed be autonomous if they perform all of their functions independently and self-sufficiently, but if they depend on communication and/or cooperation with outside entities, they should be considered cooperative rather than autonomous.
PC Magazine defines a self-driving car as "A computer-controlled car that drives itself." The Union of Concerned Scientists states that self-driving cars are "cars or trucks in which human drivers are never required to take control to safely operate the vehicle. Also known as autonomous or 'driverless' cars, they combine sensors and software to control, navigate, and drive the vehicle."
A classification system with six levels – ranging from fully manual 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 is based on the amount of driver intervention and attentiveness required, rather than the vehicle's capabilities, although these are loosely related. In the United States in 2013, the National Highway Traffic Safety Administration (NHTSA) released a formal classification system, but abandoned it in favor of the SAE standard in 2016. Also in 2016, SAE updated its classification, called J3016_201609.
Levels of driving automation
In SAE's automation level definitions, "driving mode" means "a type of driving scenario with characteristic dynamic driving task requirements (e.g., expressway merging, high speed cruising, low speed traffic jam, closed-campus operations, etc.)"
- Level 0: The automated system issues warnings and may momentarily intervene but has no sustained vehicle control.
- Level 1 ("hands on"): The driver and the automated system share control of the vehicle. Examples are systems where the driver controls steering and the automated system controls engine power to maintain a set speed (Cruise Control) or engine and brake power to maintain and vary speed (Adaptive Cruise Control or ACC); and Parking Assistance, where steering is automated while speed is under manual control. The driver must be ready to retake full control at any time. Lane Keeping Assistance (LKA) Type II is a further example of Level 1 self-driving. The Automatic Emergency Braking feature which alerts the driver to a crash and deploys full braking capacity is also a Level 1 feature.
- Level 2 ("hands off"): The automated system takes full control of the vehicle: accelerating, braking, and steering. The driver must monitor the driving and be prepared to intervene immediately at any time if the automated system fails to respond properly. The shorthand "hands off" is not meant to be taken literally – contact between hand and wheel is often mandatory during SAE 2 driving, to confirm that the driver is ready to intervene.
- Level 3 ("eyes off"): The driver can safely turn their attention away from the driving tasks, e.g. the driver can text or watch a movie. The vehicle will handle situations that call for an immediate response, like emergency braking. The driver must still be prepared to intervene within some limited time, specified by the manufacturer, when called upon by the vehicle to do so.
- Level 4 ("mind off"): As level 3, but no driver attention is ever required for safety, e.g. the driver may safely go to sleep or leave the driver's seat. Self-driving is supported only in limited spatial areas (geofenced) or under special circumstances. Outside of these areas or circumstances, the vehicle must be able to safely abort the trip, e.g. park the car, if the driver does not retake control.
- Level 5 ("steering wheel optional"): No human intervention is required at all. An example would be a robotic taxi.
In the formal SAE definition below, note in particular the shift from SAE 2 to SAE 3: the human driver no longer has to monitor the environment. This is the final aspect of the "dynamic driving task" that is now passed over from the human to the automated system. At SAE 3, the human driver still has responsibility to intervene when asked to do so by the automated system. At SAE 4 the human driver is sometimes relieved of that responsibility and at SAE 5 the automated system will never need to ask for an intervention.
|SAE Level||Name||Narrative definition||Execution of
|Monitoring of driving environment||Fallback performance of dynamic driving task||System capability (driving modes)|
|Human driver monitors the driving environment|
|0||No Automation||The full-time performance by the human driver of all aspects of the dynamic driving task, even when "enhanced by warning or intervention systems"||Human driver||Human driver||Human driver||n/a|
|1||Driver Assistance||The driving mode-specific execution by a driver assistance system of "either steering or acceleration/deceleration"||using information about the driving environment and with the expectation that the human driver performs all remaining aspects of the dynamic driving task||Human driver and system||Some driving modes|
|2||Partial Automation||The driving mode-specific execution by one or more driver assistance systems of both steering and acceleration/deceleration||System|
|Automated driving system monitors the driving environment|
|3||Conditional Automation||The driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task||with the expectation that the human driver will respond appropriately to a request to intervene||System||System||Human driver||Some driving modes|
|4||High Automation||even if a human driver does not respond appropriately to a request to intervene||System||Many driving modes|
|5||Full Automation||under all roadway and environmental conditions that can be managed by a human driver||All driving modes|
"Autonomous vehicle" means a vehicle capable of navigating District roadways and interpreting traffic-control devices without a driver actively operating any of the vehicle's control systems. The term "autonomous vehicle" excludes a motor vehicle enabled with active safety systems or driver- assistance systems, including systems to provide electronic blind-spot assistance, crash avoidance, emergency braking, parking assistance, adaptive cruise control, lane-keep assistance, lane-departure warning, or traffic-jam and queuing assistance, unless the system alone or in combination with other systems enables the vehicle on which the technology is installed to drive without active control or monitoring by a human operator.
In the same district code, it is considered that:
An autonomous vehicle may operate on a public roadway; provided, that the vehicle:
- (1) Has a manual override feature that allows a driver to assume control of the autonomous vehicle at any time;
- (2) Has a driver seated in the control seat of the vehicle while in operation who is prepared to take control of the autonomous vehicle at any moment; and
- (3) Is capable of operating in compliance with the District's applicable traffic laws and motor vehicle laws and traffic control devices.
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Between manually driven vehicles (SAE Level 0) and fully autonomous vehicles (SAE Level 5), there are a variety of vehicle types that can be described to have some degree of automation. These are collectively known as semi-automated vehicles. As it could be a while before the technology and infrastructure are developed for full automation, it is likely that vehicles will have increasing levels of automation. These semi-automated vehicles could potentially harness many of the advantages of fully automated vehicles, while still keeping the driver in charge of the vehicle.
There are different systems that help the self-driving car control the car. Systems that need improvement include the car navigation system, the location system, the electronic map, the map matching, the global path planning, the environment perception, the laser perception, the radar perception, the visual perception, the vehicle control, the perception of vehicle speed and direction, and the vehicle control method.
The challenge for driverless car designers is to produce control systems capable of analyzing sensory data in order to provide accurate detection of other vehicles and the road ahead. 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. Waymo has developed a variant of SLAM with detection and tracking of other moving objects (DATMO), which also handles obstacles such as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) technologies to aid localization. Typical sensors include lidar, stereo vision, GPS and IMU. Control systems on automated cars may use Sensor Fusion, which is an approach that integrates information from a variety of sensors on the car to produce a more consistent, accurate, and useful view of the environment. Heavy rainfall, hail, or snow could impede the car sensors.
Driverless vehicles require some form of machine vision for the purpose of visual object recognition. Automated cars are being developed with deep neural networks, a type of deep learning architecture with many computational stages, or levels, in which neurons are simulated from the environment that activate the network. The neural network depends on an extensive amount of data extracted from real-life driving scenarios, enabling the neural network to "learn" how to execute the best course of action.
In May 2018, researchers from the Massachusetts Institute of Technology announced that they had built an automated car that can navigate unmapped roads. Researchers at their Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new system, called MapLite, which allows self-driving cars to drive on roads that they have never been on before, without using 3D maps. The system combines the GPS position of the vehicle, a "sparse topological map" such as OpenStreetMap, (i.e. having 2D features of the roads only), and a series of sensors that observe the road conditions.
Nature of the digital technology
This section is written like a personal reflection, personal essay, or argumentative essay that states a Wikipedia editor's personal feelings or presents an original argument about a topic. (November 2019) (Learn how and when to remove this template message)
Autonomous vehicles, as digital technology, have certain characteristics that distinguish them from other types of technologies and vehicles. Due to these characteristics, autonomous vehicles are able to be more transformative and agile to possible changes. The characteristics will be explained based on the following subjects: homogenization and decoupling, connectivity, reprogrammable and smart, digital traces and modularity.
Homogenization and decoupling
Homogenization comes from the fact that all digital information assumes the same form. During the ongoing evolution of the digital era, certain industry standards have been developed on how to store digital information and in what type of format. This concept of homogenization also applies to autonomous vehicles. In order for autonomous vehicles to perceive their surroundings, they have to use different techniques each with their own accompanying digital information (e.g. radar, GPS, motion sensors and computer vision). Due to homogenization, the digital information from these different techniques is stored in a homogeneous way. This implies that all digital information comes in the same form, which means their differences are decoupled, and digital information can be transmitted, stored and computed in a way that the vehicles and its operating system can better understand and act upon it. Homogenization also helps to exponentially increase the computing power of hard- and software (Moore's law) which also supports the autonomous vehicles to understand and act upon the digital information in a more cost-effective way, therefore lowering the marginal costs.;
Connectivity means that users of a certain digital technology can connect easily with other users, other applications or even other enterprises. In the case of autonomous vehicles, it is essential for them to connect with other 'devices' in order to function most effectively. Autonomous vehicles are equipped with communication systems which allow them to communicate with other autonomous vehicles and roadside units to provide them, amongst other things, with information about road work or traffic congestion. In addition, scientists believe that the future will have computer programs that connect and manage each individual autonomous vehicle as it navigates through an intersection. This type of connectivity must replace traffic lights and stop signs. These types of characteristics drive and further develop the ability of autonomous vehicles to understand and cooperate with other products and services (such as intersection computer systems) in the autonomous vehicles market. This could lead to a network of autonomous vehicles all using the same network and information available on that network. Eventually, this can lead to more autonomous vehicles using the network because the information has been validated through the usage of other autonomous vehicles. Such movements will strengthen the value of the network and is called network externalities.;
Another characteristic of autonomous vehicles is that the core product will have a greater emphasis on the software and its possibilities, instead of the chassis and its engine. This is because autonomous vehicles have software systems that drive the vehicle meaning that updates through reprogramming or editing the software can enhance the benefits of the owner (e.g. update in better distinguishing blind person vs. non-blind person so that the vehicle will take extra caution when approaching a blind person). A characteristic of this reprogrammable part of autonomous vehicles is that the updates need not only to come from the supplier, because through machine learning, smart autonomous vehicles can generate certain updates and install them accordingly (e.g. new navigation maps or new intersection computer systems). These reprogrammable characteristics of the digital technology and the possibility of smart machine learning give manufacturers of autonomous vehicles the opportunity to differentiate themselves on software. This also implies that autonomous vehicles are never finished because the product can continuously be improved.
Autonomous vehicles are equipped with different sorts of sensors and radars. As said, this allows them to connect and interoperate with computers from other autonomous vehicles and/or roadside units. This implies that autonomous vehicles leave digital traces when they connect or interoperate. The data that comes from these digital traces can be used to develop new (to be determined) products or updates to enhance autonomous vehicles' driving ability or safety.
Traditional vehicles and their accompanying technologies are manufactured as a product that will be complete, and unlike autonomous vehicles, they can only be improved if they are redesigned or reproduced. As said, autonomous vehicles are produced but due to their digital characteristics never finished. This is because autonomous vehicles are more modular since they are made up out of several modules which will be explained hereafter through a Layered Modular Architecture. The Layered Modular Architecture extends the architecture of purely physical vehicles by incorporating four loosely coupled layers of devices, networks, services and contents into Autonomous Vehicles. These loosely coupled layers can interact through certain standardized interfaces.
- (1) The first layer of this architecture consists of the device layer. This layer consists of the following two parts: logical capability and physical machinery. The physical machinery refers to the actual vehicle itself (e.g. chassis and carrosserie). When it comes to digital technologies, the physical machinery is accompanied by a logical capability layer in the form of operating systems that helps to guide the vehicles itself and make it autonomous. The logical capability provides control over the vehicle and connects it with the other layers.;
- (2) On top of the device layer comes the network layer. This layer also consists of two different parts: physical transport and logical transmission. The physical transport layer refers to the radars, sensors and cables of the autonomous vehicles which enable the transmission of digital information. Next to that, the network layer of autonomous vehicles also has a logical transmission which contains communication protocols and network standard to communicate the digital information with other networks and platforms or between layers. This increases the accessibility of the autonomous vehicles and enables the computational power of a network or platform.;
- (3) The service layer contains the applications and their functionalities that serves the autonomous vehicle (and its owners) as they extract, create, store and consume content with regards to their own driving history, traffic congestion, roads or parking abilities for example.; and
- (4) The final layer of the model is the contents layer. This layer contains the sounds, images and videos. The autonomous vehicles store, extract and use to act upon and improve their driving and understanding of the environment. The contents layer also provides metadata and directory information about the content's origin, ownership, copyright, encoding methods, content tags, geo-time stamps, and so on (Yoo et al., 2010).
The consequence of layered modular architecture of autonomous vehicles (and other digital technologies) is that it enables the emergence and development of platforms and ecosystems around a product and/or certain modules of that product. Traditionally, automotive vehicles were developed, manufactured and maintained by traditional manufacturers. Nowadays app developers and content creators can help to develop more comprehensive product experience for the consumers which creates a platform around the product of autonomous vehicles.
Human factor challenges
Self-driving cars are already exploring the difficulties of determining the intentions of pedestrians, bicyclists, and animals, and models of behavior must be programmed into driving algorithms. Human road users also have the challenge of determining the intentions of autonomous vehicles, where there is no driver with which to make eye contact or exchange hand signals. Drive.ai is testing a solution to this problem that involves LED signs mounted on the outside of the vehicle, announcing status such as "going now, don't cross" vs. "waiting for you to cross".
Two human-factor challenges are important for safety. One is the handoff from automated driving to manual driving, which may become necessary due to unfavorable or unusual road conditions, or if the vehicle has limited capabilities. A sudden handoff could leave a human driver dangerously unprepared in the moment. In the long term, humans who have less practice at driving might have a lower skill level and thus be more dangerous in manual mode. The second challenge is known as risk compensation: as a system is perceived to be safer, instead of benefiting entirely from all of the increased safety, people engage in riskier behavior and enjoy other benefits. Semi-automated cars have been shown to suffer from this problem, for example with users of Tesla Autopilot ignoring the road and using electronic devices or other activities against the advice of the company that the car is not capable of being completely autonomous. In the near future, pedestrians and bicyclists may travel in the street in a riskier fashion if they believe self-driving cars are capable of avoiding them.
In order for people to buy self-driving cars and vote for the government to allow them on roads, the technology must be trusted as safe. Self-driving elevators were invented in 1900, but the high number of people refusing to use them slowed adoption for several decades until operator strikes increased demand and trust was built with advertising and features like the emergency stop button. 
The testing of vehicles with varying degrees of automation can be carried out either physically, in a closed environment or, where permitted, on public roads (typically requiring a license or permit, or adhering to a specific set of operating principles), or in a virtual environment, i.e. using computer simulations. When driven on public roads, automated vehicles require a person to monitor their proper operation and "take over" when needed. For example, New York state has strict requirements for the test driver, such that the vehicle can be corrected at all times by a licensed operator; highlighted by Cardian Cube Company's application and discussions with New York State officials and the NYS DMV.
Russian internet-company Yandex started to develop self-driving cars in 2016. In February 2018, they tested the prototype of an unmanned taxi on the streets of Moscow. In June 2018, a Yandex self-driving vehicle completed a 485-mile (780 km) trip on a federal highway from Moscow to Kazan, staying in autonomous mode for 99% of the time. In August 2018, Yandex-taxi began working with self-driving cars in the Russian town of Innopolis, and they plan to operate two unmanned vehicles with five stops within the town. In Las Vegas in January 2019, Yandex tested an unmanned vehicle for the first time outside Russia. Testing continued during the international Consumer Electronics Show between 8 and 11 January. Yandex received permission from the Israeli Ministry of Transport to test the company's unmanned vehicle on the public roads in 2019.
The progress of automated vehicles can be assessed by computing the average distance driven between "disengagements", when the automated system is switched off, typically by the intervention of a human driver. In 2017, Waymo reported 63 disengagements over 352,545 mi (567,366 km) of testing, an average distance of 5,596 mi (9,006 km) between disengagements, the highest among companies reporting such figures. Waymo also traveled a greater total distance than any of the other companies. Their 2017 rate of 0.18 disengagements per 1,000 mi (1,600 km) was an improvement over the 0.2 disengagements per 1,000 mi (1,600 km) in 2016, and 0.8 in 2015. In March 2017, Uber reported an average of just 0.67 mi (1.08 km) per disengagement. In the final three months of 2017, Cruise (now owned by GM) averaged 5,224 mi (8,407 km) per disengagement over a total distance of 62,689 mi (100,888 km). In July 2018, the first electric driverless racing car, "Robocar", completed a 1.8-kilometer track, using its navigation system and artificial intelligence.
|Car maker||2016||2018 (000)|
|Total distance traveled||Distance between
|Total distance traveled|
|Waymo||5.1 mi (8.2 km)||635.9 mi (1,023.4 km)||11.1 mi (17.9 km)||1,272 mi (2,047 km)|
|BMW||.6 mi (0.97 km)||.6 mi (0.97 km)|
|Nissan||2.6 mi (4.2 km)||6.0 mi (9.7 km)||.2 mi (0.32 km)||5.4 mi (8.7 km)|
|Ford||.2 mi (0.32 km)||.6 mi (0.97 km)|
|General Motors||.05 mi (0.080 km)||8.1 mi (13.0 km)||5.2 mi (8.4 km)||448 mi (721 km)|
|Delphi Automotive Systems||.01 mi (0.016 km)||2.7 mi (4.3 km)|
|Tesla||.003 mi (0.0048 km)||.5 mi (0.80 km)|
|Mercedes-Benz||.002 mi (0.0032 km)||.7 mi (1.1 km)||.002 mi (0.0032 km)||1.7 mi (2.7 km)|
|Bosch||0.007 mi (0.011 km)||1.0 mi (1.6 km)|
|Volkswagen||.006 mi (0.0097 km)||.009 mi (0.014 km)|
|Zoox||1.9 mi (3.1 km)||30.8 mi (49.6 km)|
|Nuro||1 mi (1.6 km)||20.7 mi (33.3 km)|
|Pony.ai||1 mi (1.6 km)||16.3 mi (26.2 km)|
|Baidu||.2 mi (0.32 km)||18.0 mi (29.0 km)|
|Aurora||.09 mi (0.14 km)||32.9 mi (52.9 km)|
|Applie||.001 mi (0.0016 km)||79.7 mi (128.3 km)|
|Uber||.0004 mi (0.00064 km)||26.9 mi (43.3 km)|
Fields of application
Autonomous trucks and vans
Companies such as Otto and Starsky Robotics have focused on autonomous trucks. Automation of trucks is important, not only due to the improved safety aspects of these very heavy vehicles, but also due to the ability of fuel savings through platooning.
In Europe, cities in Belgium, France, Italy and the UK are planning to operate transport systems for automated cars, and Germany, the Netherlands, and Spain have allowed public testing in traffic. In 2015, the UK launched public trials of the LUTZ Pathfinder automated pod in Milton Keynes. Beginning in summer 2015, the French government allowed PSA Peugeot-Citroen to make trials in real conditions in the Paris area. The experiments were planned to be extended to other cities such as Bordeaux and Strasbourg by 2016. The alliance between French companies THALES and Valeo (provider of the first self-parking car system that equips Audi and Mercedes premi) is testing its own system. New Zealand is planning to use automated vehicles for public transport in Tauranga and Christchurch.
In China, Baidu and King Long produce automated minibus, a vehicle with 14 seats, but without driving seat. With 100 vehicles produced, 2018 will be the first year with commercial automated service in China.
According to a 2020 study, self-driving cars will increase productivity, and housing affordability, as well as reclaim land used for parking. However, self-driving cars will cause greater energy use, traffic congestion and sprawl.
Driving safety experts predict that once driverless technology has been fully developed, traffic collisions (and resulting deaths and injuries and costs) caused by human error, such as delayed reaction time, tailgating, rubbernecking, and other forms of distracted or aggressive driving should be substantially reduced. 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".
According to motorist website "TheDrive.com" operated by Time magazine, none of the driving safety experts they were able to contact were able to rank driving under an autopilot system at the time (2017) as having achieved a greater level of safety than traditional fully hands-on driving, so the degree to which these benefits asserted by proponents will manifest in practice cannot be assessed. Confounding factors that could reduce the net effect on safety may include unexpected interactions between humans and partly or fully automated vehicles, or between different types of vehicle system; complications at the boundaries of functionality at each automation level (such as handover when the vehicle reaches the limit of its capacity); the effect of the bugs and flaws that inevitably occur in complex interdependent software systems; sensor or data shortcomings; and successful compromise by malicious interveners.
To help reduce the possibility of these confounding factors, some companies have begun to open-source parts of their driverless systems. Udacity for instance is developing an open-source software stack, and some companies are having similar approaches.
Automated cars could 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, automated cars could provide enhanced mobility. 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.
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 US 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. One study estimated that automated 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 545% 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. Human drivers at highway speeds keep between 40 to 50 m (130 to 160 ft) away from the vehicle 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. The ability for authorities to manage traffic flow would increase, given the extra data and driving behavior predictability combined with less need for traffic police and even road signage.
Energy and environmental impacts
Vehicle automation can improve fuel economy of the car by optimizing the drive cycle. Reduced traffic congestion and the improvements in traffic flow due to widespread use of automated cars will translate into higher fuel efficiency. Additionally, self-driving cars will be able to accelerate and brake more efficiently, meaning higher fuel economy from reducing wasted energy typically associated with inefficient changes to speed. However, the improvement in vehicle energy efficiency does not necessarily translate to net reduction in energy consumption and positive environmental outcomes. It is expected that convenience of the automated vehicles encourages the consumers to travel more, and this induced demand may partially or fully offset the fuel efficiency improvement brought by automation. Overall, the consequences of vehicle automation on global energy demand and emissions are highly uncertain, and heavily depends on the combined effect of changes in consumer behavior, policy intervention, technological progress and vehicle technology.
Manually driven vehicles are reported to be used only 4–5% of the time, and being parked and unused for the remaining 95–96% of the time. Autonomous taxis could, on the other hand, be used continuously after it has reached its destination. This could dramatically reduce the need for parking space. For example, in Los Angeles a 2015 study found 14% of the land is used for parking alone, equivalent to some 1,702 hectares (4,210 acres). This combined with the potential reduced need for road space due to improved traffic flow, could free up large amounts of land in urban areas, which could then be used for parks, recreational areas, buildings, among other uses; making cities more livable.
Besides this, privately owned self-driving cars, also capable of self-parking would provide another advantage: the ability to drop off and pick up passengers even in places where parking is prohibited. This would benefit park and ride facilities.
By reducing the labor and other costs of mobility as a service, automated cars could reduce the number of cars that are individually owned, replaced by taxi/pooling and other car-sharing services. This would also dramatically reduce the size of the automotive production industry, with corresponding environmental and economic effects. Assuming the increased efficiency is not fully offset by increases in demand, more efficient traffic flow could free roadway space for other uses such as better support for pedestrians and cyclists.
The vehicles' increased awareness could aid the police by reporting on illegal passenger behavior, while possibly enabling other crimes, such as deliberately crashing into another vehicle or a pedestrian. However, this may also lead to much expanded mass surveillance if there is wide access granted to third parties to the large data sets generated.
A direct impact of widespread adoption of automated 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. A frequently cited paper by Michael Osborne and Carl Benedikt Frey found that automated cars would make many jobs redundant.
Privacy could be an issue when having the vehicle's location and position integrated into an interface that other people have access to. In addition, there is the risk of automotive hacking through the 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 housing is cheaper, and work in the city's core, thus increasing travel distances and inducing more urban sprawl, raising energy consumption and enlarging 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.
Some[who?] believe that once automation in vehicles reaches higher levels and becomes reliable, drivers will pay less attention to the road. Research shows that drivers in automated cars react later when they have to intervene in a critical situation, compared to if they were driving manually. Depending on the capabilities of automated vehicles and the frequency with which human intervention is needed, this may counteract any increase in safety, as compared to all-human driving, that may be delivered by other factors.
Ethical and moral reasoning come into consideration when programming the software that decides what action the car takes in an unavoidable crash; whether the automated car will crash into a bus, potentially killing people inside; or swerve elsewhere, potentially killing its own passengers or nearby pedestrians. A question that programmers of AI systems find difficult to answer is "what decision should the car make that causes the 'smallest' damage to people's lives?" Adding to the challenge of determining machine ethics, is the fact that morality is not universal.
The ethics of automated vehicles are still being articulated, and may lead to controversy. They may also require closer consideration of the variability, context-dependency, complexity and non-deterministic nature of human ethics. Different human drivers make various ethical decisions when driving, such as avoiding harm to themselves, or putting themselves at risk to protect others. These decisions range from rare extremes such as self-sacrifice or criminal negligence, to routine decisions good enough to keep the traffic flowing but bad enough to cause accidents, road rage and stress.
Human thought and reaction time may sometimes be too slow to detect the risk of an upcoming fatal crash, think through the ethical implications of the available options, or take an action to implement an ethical choice. Whether a particular automated vehicle's capacity to correctly detect an upcoming risk, analyse the options or choose a 'good' option from among bad choices would be as good or better than a particular human's may be difficult to predict or assess. This difficulty may be in part because the level of automated vehicle system understanding of the ethical issues at play in a given road scenario, sensed for an instant from out of a continuous stream of synthetic physical predictions of the near future, and dependent on layers of pattern recognition and situational intelligence, may be opaque to human inspection because of its origins in probabilistic machine learning rather than a simple, plain English 'human values' logic of parsable rules. The depth of understanding, predictive power and ethical sophistication needed will be hard to implement, and even harder to test or assess.
The scale of this challenge may have other effects. There may be few entities able to marshal the resources and AI capacity necessary to meet it, as well as the capital necessary to take an automated vehicle system to market and sustain it operationally for the life of a vehicle, and the legal capacity to deal with the potential for liability for a significant proportion of traffic accidents. This may have the effect of narrowing the number of different system operators, and eroding the diverse global vehicle market down to a small number of system suppliers.
Potential limits or obstacles
The sort of hoped-for potential benefits from increased vehicle automation described may be limited by foreseeable challenges, such as disputes over liability, the time needed to turn over the existing stock of vehicles from non-automated to automated, and thus a long period of humans and autonomous vehicles sharing the roads, resistance by individuals to having to forfeit control of their cars, concerns about the safety of driverless in practice, and the implementation of a legal framework and consistent global government regulations for self-driving cars.
Other obstacles could include de-skilling and lower levels of driver experience for dealing with potentially dangerous situations and anomalies, ethical problems where an automated vehicle's software is forced during an unavoidable crash to choose between multiple harmful courses of action ('the trolley problem'), concerns about making large numbers of people currently employed as drivers unemployed, the potential for more intrusive mass surveillance of location, association and travel as a result of police and intelligence agency access to large data sets generated by sensors and pattern-recognition AI, and possibly insufficient understanding of verbal sounds, gestures and non-verbal cues by police, other drivers or pedestrians.
Possible technological obstacles for automated cars are:
- Artificial Intelligence is still not able to function properly in chaotic inner-city environments.
- A car's computer could potentially be compromised, as could a communication system between cars.
- Susceptibility of the car's sensing and navigation systems to different types of weather (such as snow) or deliberate interference, including jamming and spoofing.
- Avoidance of large animals requires recognition and tracking, and Volvo found that software suited to caribou, deer, and elk was ineffective with kangaroos.
- 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 automated cars to function optimally.
Social challenges include:
- Government over-regulation, or even uncertainty about potential future regulation, may delay deployment of automated cars on the road.
- Employment – Companies working on the technology have an increasing recruitment problem in that the available talent pool has not grown with demand. As such, education and training by third-party organisations such as providers of online courses and self-taught community-driven projects such as DIY Robocars and Formula Pi have quickly grown in popularity, while university level extra-curricular programmes such as Formula Student Driverless have bolstered graduate experience. Industry is steadily increasing freely available information sources, such as code, datasets and glossaries to widen the recruitment pool.
Potential changes for different industries
The traditional automobile industry is subject to changes driven by technology and market demands. These changes include breakthrough technological advances and when the market demands and adopts new technology quickly. In the rapid advance of both factors, the end of the era of incremental change was recognized. When the transition is made to a new technology, new entrants to the automotive industry present themselves, which can be distinguished as mobility providers such as Uber and Lyft, as well as tech giants such as Google and Nvidia. As new entrants to the industry arise, market uncertainty naturally occurs due to the changing dynamics. For example, the entrance of tech giants, as well as the alliances between them and traditional car manufacturers causes a variation in the innovation and production process of autonomous vehicles. Additionally, the entrance of mobility providers has caused ambiguous user preferences. As a result of the rise of mobility providers, the number of vehicles per capita has flatlined. In addition, the rise of the sharing economy also contributes to market uncertainty and causes forecasters to question whether personal ownership of vehicles is still relevant as new transportation technology and mobility providers are becoming preferred among consumers.
With the aforementioned ambiguous user preference regarding the personal ownership of autonomous vehicles, it is possible that the current mobility provider trend will continue as it rises in popularity. Established providers such as Uber and Lyft are already significantly present within the industry, and it is likely that new entrants will enter when business opportunities arise.
Healthcare, car repair, and car insurance
With the increasing reliance of autonomous vehicles on interconnectivity and the availability of big data which is made usable in the form of real-time maps, driving decisions can be made much faster in order to prevent collisions. Numbers made available by the US government state that 94% of the vehicle accidents are due to human failures. As a result, major implications for the healthcare industry become apparent. Numbers from the National Safety Council on killed and injured people on US roads multiplied by the average costs of a single incident reveal that an estimated US$500 billion loss may be imminent for the US healthcare industry when autonomous vehicles are dominating the roads. It is likely the anticipated decrease in traffic accidents will positively contribute to the widespread acceptance of autonomous vehicles, as well as the possibility to better allocate healthcare resources. As collisions are less likely to occur, and the risk for human errors is reduced significantly, the repair industry will face an enormous reduction of work that has to be done on the reparation of car frames. Meanwhile, as the generated data of the autonomous vehicle is likely to predict when certain replaceable parts are in need of maintenance, car owners and the repair industry will be able to proactively replace a part that will fail soon. This "Asset Efficiency Service" would implicate a productivity gain for the automotive repair industry. As fewer collisions implicate less money spent on repair costs, the role of the insurance industry is likely to be altered as well. It can be expected that the increased safety of transport due to autonomous vehicles will lead to a decrease in payouts for the insurers, which is positive for the industry, but fewer payouts may imply a demand drop for insurances in general. The insurance industry may have to create new insurance models in the near future to accommodate the changes. An unexpected disadvantage of the widespread acceptance of autonomous vehicles would be a reduction in organs available for transplant.
Rescue, emergency response, and military
The technique used in autonomous driving also ensures life savings in other industries. The implementation of autonomous vehicles with rescue, emergency response, and military applications has already led to a decrease in deaths. Military personnel use autonomous vehicles to reach dangerous and remote places on earth to deliver fuel, food and general supplies, and even rescue people. In addition, a future implication of adopting autonomous vehicles could lead to a reduction in deployed personnel, which will lead to a decrease in injuries, since the technological development allows autonomous vehicles to become more and more autonomous. Another future implication is the reduction of emergency drivers when autonomous vehicles are deployed as fire trucks or ambulances. An advantage could be the use of real-time traffic information and other generated data to determine and execute routes more efficiently than human drivers. The time savings can be invaluable in these situations.
Interior design and entertainment
With the driver decreasingly focused on operating a vehicle, the interior design and media-entertainment industry will have to reconsider what passengers of autonomous vehicles are doing when they are on the road. Vehicles need to be redesigned, and possibly even be prepared for multipurpose usage. In practice, it will show that travelers have more time for business and/or leisure. In both cases, this gives increasing opportunities for the media-entertainment industry to demand attention. Moreover, the advertisement business is able to provide location based ads without risking driver safety.
Telecommunication and energy
All cars can benefit from information and connections, but autonomous cars “Will be fully capable of operating without C-V2X." In addition, the earlier mentioned entertainment industry is also highly dependent on this network to be active in this market segment. This implies higher revenues for the telecommunication industry.
Since many autonomous vehicles are going to rely on electricity to operate, the demand for lithium batteries increases. Similarly, radar, sensors, lidar, and high-speed internet connectivity require higher auxiliary power from vehicles, which manifests as greater power draw from batteries. The larger battery requirement causes a necessary increase in supply of these type of batteries for the chemical industry. On the other hand, with the expected increase of battery powered (autonomous) vehicles, the petroleum industry is expected to undergo a decline in demand. As this implication depends on the adoption rate of autonomous vehicles, it is unsure to what extent this implication will disrupt this particular industry. This transition phase of oil to electricity allows companies to explore whether there are business opportunities for them in the new energy ecosystem.
Restaurant, hotels, and airlines
Driver interactions with the vehicle will be less common within the near future, and in the more distant future the responsibility will lie entirely with the vehicle. As indicated above, this will have implications for the entertainment- and interior design industry. For roadside restaurants, the implication will be that the need for customers to stop driving and enter the restaurant will vanish, and the autonomous vehicle will have a double function. Moreover, accompanied with the rise of disruptive platforms such as Airbnb that have shaken up the hotel industry, the fast increase of developments within the autonomous vehicle industry might cause another implication for their customer bases. In the more distant future, the implication for motels might be that a decrease in guests will occur, since autonomous vehicles could be redesigned as fully equipped bedrooms. The improvements regarding the interior of the vehicles might additionally have implications for the airline industry. In the case of relatively short-haul flights, waiting times at customs or the gate imply lost time and hassle for customers. With the improved convenience in future car travel, it is possible that customers might go for this option, causing a loss in customer bases for airline industry.
Elderly and disabled
The elderly and persons with disabilities (such as persons who are hearing-impaired, vision-impaired, mobility-impaired, or cognitively-impaired) are potential beneficiaries of adoption of autonomous vehicles; however, the extent to which such populations gain greater mobility from the adoption of AV technology depends on the specific designs and regulations adopted.
Children and teens, who are not able to drive a vehicle themselves, are also benefiting of the introduction of autonomous cars[when?]. Daycares and schools are able to come up with automated pick up and drop off systems by car in addition to walking, cycling and busing, causing a decrease of reliance on parents and childcare workers. The extent to which human actions are necessary for driving will vanish. Since current vehicles require human actions to some extent, the driving school industry will not be disrupted until the majority of autonomous transportation is switched to the emerged dominant design. It is plausible that in the distant future driving a vehicle will be considered as a luxury, which implies that the structure of the industry is based on new entrants and a new market.
In mid‑October 2015, Tesla Motors rolled out version 7 of their software in the US 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 automated driving features is currently classified as a Level 2 driver assistance system according to the Society of Automotive Engineers' (SAE) five levels of vehicle automation. At this level the car can be automated 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.
On 20 January 2016, the first known fatal crash of a Tesla with Autopilot occurred in China's Hubei province. According to China's 163.com news channel, this marked "China's first accidental death due to Tesla's automatic driving (system)". Initially, Tesla pointed out that the vehicle was so badly damaged from the impact that their recorder was not able to conclusively prove that the car had been on Autopilot at the time; however, 163.com pointed out that other factors, such as the car's absolute failure to take any evasive actions prior to the high speed crash, and the driver's otherwise good driving record, seemed to indicate a strong likelihood that the car was on Autopilot at the time. A similar fatal crash occurred four months later in Florida. In 2018, in a subsequent civil suit between the father of the driver killed and Tesla, Tesla did not deny that the car had been on Autopilot at the time of the accident, and sent evidence to the victim's father documenting that fact.
The second known 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 US National Highway Traffic Safety Administration (NHTSA) opened a formal investigation into the accident working with the Florida Highway Patrol. According to 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. 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, 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 claimed that this was Tesla's first known autopilot death in over 130 million miles (210 million kilometers) driven by its customers with Autopilot engaged, however by this statement, Tesla was apparently refusing to acknowledge claims that the January 2016 fatality in Hubei China had also been the result of an autopilot system error. According to Tesla there is a fatality every 94 million miles (151 million kilometers) among all type of vehicles in the US However, this number also includes fatalities of the crashes, for instance, of motorcycle drivers with pedestrians.
In July 2016, the US 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 has the power to make only 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 for Tesla cars, the 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 2019 under certain conditions.
Waymo originated as a self-driving car project within Google. In August 2012, Google announced that their vehicles had completed over 300,000 automated-driving miles (500,000 km) accident-free, typically involving about a dozen cars on the road at any given time, and that they were starting to test with single drivers instead of in pairs. In late-May 2014, Google revealed a new prototype that had no steering wheel, gas pedal, or brake pedal, and was fully automated . As of March 2016[update], Google had test-driven their fleet in automated mode a total of 1,500,000 mi (2,400,000 km). In December 2016, Google Corporation announced that its technology would be spun off to a new company called Waymo, with both Google and Waymo becoming subsidiaries of a new parent company called Alphabet.
According to Google's accident reports as of early 2016, their test cars had been involved in 14 collisions, of which other drivers were at fault 13 times, although in 2016 the car's software caused a crash.
In June 2015, Brin confirmed that 12 vehicles had suffered collisions as of that date. Eight involved rear-end collisions 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 their vehicle was rear-ended by a car whose driver failed to brake at a traffic light. This was the first time that a collision resulted in injuries. On 14 February 2016 a Google vehicle attempted to avoid sandbags blocking its path. During the maneuver it struck a bus. Google stated, "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. No injuries were reported in the crash.
By 22 December 2017, Uber had completed 2 million miles (3.2 million kilometers) in automated mode.
On 18 March 2018, Elaine Herzberg became the first pedestrian to be killed by a self-driving car in the United States after being hit by an Uber vehicle, also in Tempe. Herzberg was crossing outside of a crosswalk, approximately 400 feet from an intersection. This marks the first time an individual outside an auto-piloted car is known to have been killed by such a car.
The first death of an essentially uninvolved third party is likely to raise new questions and concerns about the safety of automated cars in general. Some experts say a human driver could have avoided the fatal crash. Arizona Governor Doug Ducey later suspended the company's ability to test and operate its automated cars on public roadways citing an "unquestionable failure" of the expectation that Uber make public safety its top priority. Uber has pulled out of all self-driving-car testing in California as a result of the accident. On 24 May 2018 the US National Transport Safety Board issued a preliminary report.
On 9 November 2017, a Navya automated self-driving bus with passengers was involved in a crash with a truck. The truck was found to be at fault of the crash, reversing into the stationary automated bus. The automated bus did not take evasive actions or apply defensive driving techniques such as flashing its headlights, or sounding the horn. As one passenger commented, "The shuttle didn't have the ability to move back. The shuttle just stayed still."
According to a Wonkblog reporter, if fully automated 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.
Policy-makers will have to take a new look at how infrastructure is to be built and how money will be allotted to build for automated vehicles. The need for traffic signals could potentially be reduced with the adoption of smart highways. Due to smart highways and with the assistance of smart technological advances implemented by policy change, the dependence on oil imports may be reduced because of less time being spent on the road by individual cars which could have an effect on policy regarding energy. On the other hand, automated vehicles could increase the overall number of cars on the road which could lead to a greater dependence on oil imports if smart systems are not enough to curtail the impact of more vehicles. However, due to the uncertainty of the future of automated vehicles, policy makers may want to plan effectively by implementing infrastructure improvements that can be beneficial to both human drivers and automated vehicles. Caution needs to be taken in acknowledgment to public transportation and that the use may be greatly reduced if automated vehicles are catered to through policy reform of infrastructure with this resulting in job loss and increased unemployment.
Other disruptive effects will come from the use of automated vehicles to carry goods. Self-driving vans have the potential to make home deliveries significantly cheaper, transforming retail commerce and possibly making hypermarkets and supermarkets redundant. As of 2019[update] the US Department of Transportation 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 in the US 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.
Researchers have pushed against this arguing that self-driving cars will have a deeply negative impact on urban life especially if they are programmed to kill. Moreover, they require a sensor-based infrastructure that would constitute an all-encompassing surveillance apparatus. They would also exasperate existing mobility inequalities driven by the interests of car companies and technology companies while taking investment away from more equatable and sustainable mobility initiatives such as public transportation.
The 1968 Vienna Convention on Road Traffic, subscribed to by over 70 countries worldwide, establishes principles to govern traffic laws. One of the fundamental principles of the convention has been the concept that a driver is always fully in control and responsible for the behavior of a vehicle in traffic. The progress of technology that assists and takes over the functions of the driver is undermining this principle, implying that much of the groundwork must be rewritten.
Legal status in the United States
In the United States, a non-signatory country to the Vienna Convention, state vehicle codes generally do not envisage—but do not necessarily prohibit—highly automated vehicles as of 2012[update]. To clarify the legal status of and otherwise regulate such vehicles, several states have enacted or are considering specific laws. By 2016, seven states (Nevada, California, Florida, Michigan, Hawaii, Washington, and Tennessee), along with the District of Columbia, have enacted laws for automated vehicles. Incidents such as the first fatal accident by Tesla's Autopilot system have led to discussion about revising laws and standards for automated cars.
In September 2016, the US National Economic Council and US 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 automated cars. Nevada thus became the first jurisdiction in the world where automated vehicles might be legally operated on public roads. According to the law, the Nevada Department of Motor Vehicles is responsible for setting safety and performance standards and the agency is responsible for designating areas where automated 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 automated 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 April 2012, Florida became the second state to allow the testing of automated cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google Headquarters in Mountain View. In December 2013, Michigan became the fourth state to allow testing of driverless cars on public roads. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
On 19 February 2016, California Assembly Bill 2866 was introduced in California that would allow automated vehicles to operate on public roads, including those without a driver, steering wheel, accelerator pedal, or brake pedal. The bill states that the California Department of Motor Vehicles would need to comply with these regulations by 1 July 2018 for these rules to take effect. As of November 2016[update], this bill has yet to pass the house of origin.
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.
Legislation in Europe
In 2013, the government of the United Kingdom permitted the testing of automated cars on public roads. Before 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 automated 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 automated vehicles on open road in France was carried out in Bordeaux in early October 2015.
In 2015, a preemptive lawsuit against various automobile companies such as GM, Ford, and Toyota accused them of "Hawking vehicles that are vulnerable to hackers who could hypothetically wrest control of essential functions such as brakes and steering."
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.
As of April 2017, it is possible to conduct public road tests for development vehicles in Hungary, furthermore the construction of a closed test track, the ZalaZone test track, suitable for testing highly automated functions is also under way near the city of Zalaegerszeg.
Legislation in Asia
In 2016, the Singapore Land Transit Authority in partnership with UK automotive supplier Delphi Automotive, began launch preparations for a test run of a fleet of automated taxis for an on-demand automated cab service to take effect in 2017.
In 2017, the South Korean government stated that the lack of universal standards is preventing its own legislation from pushing new domestic rules. However, once the international standards are settled, South Korea's legislation will resemble the international standards.
Self-driving car liability is a developing area of law and policy that will determine who is liable when an automated car causes physical damage to persons, or breaks road rules. When automated cars shift the control of driving from humans to automated car technology, there may be a need for existing liability laws to evolve in order to fairly identify the parties responsible for damage and injury, and to address the potential for conflicts of interest between human occupants, system operator, insurers, and the public purse. Increases in the use of automated car technologies (e.g. advanced driver-assistance systems) may prompt incremental shifts in this responsibility for driving. It is claimed by proponents to have potential to affect the frequency of road accidents, although it is difficult to assess this claim in the absence of data from substantial actual use. If there was a dramatic improvement in safety, the operators may seek to project their liability for the remaining accidents onto others as part of their reward for the improvement. However, there is no obvious reason why they should escape liability if any such effects were found to be modest or nonexistent, since part of the purpose of such liability is to give an incentive to the party controlling something to do whatever is necessary to avoid it causing harm. Potential users may be reluctant to trust an operator if it seeks to pass its normal liability on to others.
In any case, a well-advised person who is not controlling a car at all (Level 5) would be understandably reluctant to accept liability for something out of their control. And when there is some degree of sharing control possible (Level 3 or 4), a well-advised person would be concerned that the vehicle might try to pass back control at the last seconds before an accident, to pass responsibility and liability back too, but in circumstances where the potential driver has no better prospects of avoiding the crash than the vehicle, since they have not necessarily been paying close attention, and if it is too hard for the very smart car it might be too hard for a human. Since operators, especially those familiar with trying to ignore existing legal obligations (under a motto like 'seek forgiveness, not permission'), such as Waymo or Uber, could be normally expected to try to avoid responsibility to the maximum degree possible, there is potential for attempt to let the operators evade being held liable for accidents while they are in control.
As higher levels of automation are commercially introduced (Level 3 and 4), the insurance industry may see a greater proportion of commercial and product liability lines while personal automobile insurance shrinks.
Vehicle communication systems
Vehicle networking may be desirable due to difficulty with computer vision being able to recognize brake lights, turn signals, buses, and similar things. However, the usefulness of such systems would be diminished by the fact current cars are equipped with them; they may also pose privacy concerns.
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 US National Highway Traffic Safety Administration, vehicular communication systems could help avoid up to 79% of all traffic accidents.
There have so far been no complete implementation of peer-to-peer networking on the scale required for traffic: each individual vehicle would have to connect with potentially hundreds of different vehicles that could be going in and out of range.
In 2012, computer scientists at the University of Texas in Austin began developing smart intersections designed for automated 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.
In 2017, Researchers from Arizona State University developed a 1/10 scale intersection and proposed an intersection management technique called Crossroads. It was shown that Crossroads is very resilient to network delay of both V2I communication and Worst-case Execution time of the intersection manager. In 2018, a robust approach was introduced which is resilient to both model mismatch and external disturbances such as wind and bumps.
Among connected cars, an unconnected one is the weakest link and will be increasingly banned from busy high-speed roads, as predicted by the Helsinki think tank, Nordic Communications Corporation, in January 2016.
Public opinion surveys
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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 US$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 automated 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 automated 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. The survey also gave results on potential consumer opinion on interest of purchasing an automated car, stating that 37% of surveyed current owners were either "definitely" or "probably" interested in purchasing an automated car.
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.
A Pew Research Center survey of 4,135 US adults conducted 1–15 May 2017 finds that many Americans anticipate significant impacts from various automation technologies in the course of their lifetimes—from the widespread adoption of automated vehicles to the replacement of entire job categories with robot workers.
Results from two opinion surveys of 54 and 187 US adults respectively were published in 2019. A new standardised questionnaire, the autonomous vehicle acceptance model (AVAM) was developed, including additional description to help respondents better understand the implications of different automation levels. Results showed that users were less accepting of high autonomy levels and displayed significantly lower intention to use highly autonomous vehicles. Additionally, partial autonomy (regardless of level) was perceived as requiring uniformly higher driver engagement (usage of hands, feet and eyes) than full autonomy.
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With the emergence of automated automobiles, various ethical issues arise. While the introduction of automated vehicles to the mass market is said to be inevitable due to a presumed but untestable potential for reduction of crashes by "up to" 90% and their potential greater accessibility to disabled, elderly, and young passengers, a range of ethical issues have not been fully addressed. Those include, but are not limited to: the moral, financial, and criminal responsibility for crashes and breaches of law; the decisions a car is to make right before a (fatal) crash; privacy issues including potential for mass surveillance; potential for massive job losses and unemployment among drivers; de-skilling and loss of independence by vehicle users; exposure to hacking and malware; and the further concentration of market and data power in the hands of a few global conglomerates capable of consolidating AI capacity, and of lobbying governments to facilitate the shift of liability onto others and their potential destruction of existing occupations and industries.
There are different opinions on who should be held liable in case of a crash, especially 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[who?] that argue those using or owning the vehicle should be held responsible since they know the risks involved in using such a vehicle. Experts[who?] suggest introducing a tax or insurances that would protect owners and users of automated 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 automated 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 automated vehicles should be programmed to behave in an emergency situation where either passengers or other traffic participants like: pedestrians, bicyclists and other drivers are endangered. A 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 five people, or turn the trolley onto a track where it would kill only one person, assuming there is no traffic on it. When a self-driving car is in following scenario: it's driving with passengers and suddenly a person appears in its way. The car has to decide between the two options, either to run the person over or to avoid hitting the person by swerving into a wall, killing the passengers. There are two main considerations that need to be addressed. First, what moral basis would be used by an automated vehicle to make decisions? Second, how could those be translated into software code? Researchers have suggested, in particular, two ethical theories to be applicable to the behavior of automated 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 automated 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 automated vehicles should adapt a mix of multiple theories to be able to respond morally right in the instance of a crash.
Many 'trolley' discussions skip over the practical problems of how a probabilistic machine learning vehicle AI could be sophisticated enough to understand that a deep problem of moral philosophy is presenting itself from instant to instant while using a dynamic projection into the near future, what sort of moral problem it actually would be if any, what the relevant weightings in human value terms should be given to all the other humans involved who will be probably unreliably identified, and how reliably it can assess the probable outcomes. These practical difficulties, and those around testing and assessment of solutions to them, may present as much of a challenge as the theoretical abstractions.
While most trolley conundrums involve hyperbolic and unlikely fact patterns, it is inevitable mundane ethical decisions and risk calculations such as the precise millisecond a car should yield to a yellow light or how closely to drive to a bike lane will need to be programmed into the software of autonomous vehicles. Algorithms dictate, for example, how closely to drive to a bike lane or the precise moment an autonomous car should yield to a yellow light. Mundane ethical situations may even be more relevant than rare fatal circumstances because of the specificity implicated and their large scope. Mundane situations involving drivers and pedestrians are so prevalent that, in the aggregate, produce large amounts of injuries and deaths. Hence, even incremental permutations of moral algorithms can have a notable effect when considered in their entirety.
Privacy-related issues arise mainly from the interconnectivity of automated 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 data and communications infrastructure needed to support these vehicles may also be capable of surveillance, especially if coupled to other data sets and advanced analytics.
The implementation of automated 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 US$220 billion, 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 will have a tremendous impact on those individuals involved. Both India and China have placed bans on automated cars with the former citing protection of jobs.
The Massachusetts Institute of Technology (MIT) has animated the trolley problem in the context of autonomous cars in a website called The Moral Machine. The Moral Machine generates random scenarios in which autonomous cars malfunction and forces the user to choose between two harmful courses of action. MIT's Moral Machine experiment has collected data involving over 40 million decisions from people in 233 countries to ascertain peoples’ moral preferences. The MIT study illuminates that ethical preferences vary among cultures and demographics and likely correlate with modern institutions and geographic traits.
Global trends of the MIT study highlight that, overall, people prefer to save the lives of humans over other animals, prioritize the lives of many rather than few, and spare the lives of young rather than old. Men are slightly more likely to spare the lives of women, and religious affiliates are slightly more likely to prioritize human life. The lives of criminals were prioritized more than cats, but the lives of dogs were prioritized more than the lives of criminals. The lives of homeless were spared more than the elderly, but the lives of homeless were spared less often than the obese.
People overwhelmingly express a preference for autonomous vehicles to be programmed with utilitarian ideas, that is, in a manner that generates the least harm and minimizes driving casualties. While people want others to purchase utilitarian promoting vehicles, they themselves prefer to ride in vehicles that prioritize the lives of people inside the vehicle at all costs. This presents a paradox in which people prefer that others drive utilitarian vehicles designed to maximize the lives preserved in a fatal situation but want to ride in cars that prioritize the safety of passengers at all costs. People disapprove of regulations that promote utilitarian views and would be less willing to purchase a self-driving car that may opt to promote the greatest good at the expense of its passengers.
Bonnefon et al. conclude that the regulation of autonomous vehicle ethical prescriptions may be counterproductive to societal safety. This is because, if the government mandates utilitarian ethics and people prefer to ride in self-protective cars, it could prevent the large scale implementation of self-driving cars. Delaying the adoption of autonomous cars vitiates the safety of society as a whole because this technology is projected to save so many lives. This is a paradigmatic example of the tragedy of the commons in which rational actors cater to their self-interested preferences at the expense of societal utility.
Anticipated launch of cars
In December 2015, Tesla CEO Elon Musk predicted that a completely automated car would be introduced by the end of 2018; in December 2017, he announced that it would take another two years to launch a fully self-driving Tesla onto the market. Waymo launched a ride hailing service in Phoenix in December, 2018. Drive.ai is doing a trial run in Frisco, Texas and Arlington Texas.
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The automated and occasionally sentient self-driving car story has earned its place in both literary science fiction and pop sci-fi.
- A VW Beetle named Dudu [de] features in the 1971 to 1978 German Superbug (film series) of movies similar to Disney's Herbie, but with an electronic brain. (Herbie, also a Beetle, was depicted as an anthropomorphic car with its own spirit.)
- In the film Batman (1989), starring Michael Keaton, the Batmobile is shown to be able to drive to Batman's current location with some navigation commands from Batman and possibly some automation. In the 1992 sequel Batman Returns the Batmobile's self-driving system is hijacked by The Penguin, who wreaks havoc through the city to frame Batman until Bruce undoes the sabotage.
- The film Total Recall (1990), starring Arnold Schwarzenegger, features taxis called Johnny Cabs controlled by artificial intelligence in the shape of an android bust, while still possessing a joystick for manual control.
- The film Demolition Man (1993), starring Sylvester Stallone and set in 2032, features vehicles that can be self-driven or commanded to "Auto Mode" where a voice-controlled computer operates the vehicle.
- The film Timecop (1994), starring Jean-Claude Van Damme, set in 2004 and 1994, has automated cars.
- Another Arnold Schwarzenegger movie, The 6th Day (2000), features an automated car commanded by Michael Rapaport.
- The film Minority Report (2002), set in Washington, DC in 2054, features an extended chase sequence involving automated cars. The vehicle of protagonist John Anderton is transporting him when its systems are overridden by police in an attempt to bring him into custody.
- The film The Incredibles (2004), Mr. Incredible makes his car (later revealed to be called the Incredibile) automated while it changes him into his supersuit when driving to catch up to a car of robbers on the run. The car reappears in the sequel Incredibles 2 (2018) where it is used by Dash and Violet Parr to escape from brainwashed superheroes controlled by the villain Screenslaver and to board Winston Deavor's ship.
- The film I, Robot (2004), set in Chicago in 2035, features automated vehicles driving on highways, allowing the car to travel safer at higher speeds than if manually controlled. The option to manually operate the vehicles is available.
- In 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).
- The film Hot Tub Time Machine 2 (2015) features automated cars that appear ten years in the future from the film's present time. One car targets Lou Dorchen after he insults it and it later helps the main characters return to the hot tub time machine after Lou apologizes to it for his insults.
- In the CGI animated short film "You Are Not Alone" (2016), which is set in 2058, an automated car helps the main protagonist reach the surface to find her sister. The car later sacrifices itself to help the protagonist escape from the pursuing authorities.
- Geostorm (2017), set in 2022, features a self-driving taxi stolen by protagonists Max Lawson and Sarah Wilson to protect the President from mercenaries and a superstorm.
- The film Logan (2017), set in 2029, features fully automated trucks.
- Blade Runner 2049 (2017) opens with LAPD Replicant cop K waking up in his modern Spinner (a flying police car, now featuring automatic driver and separable surveillance roof drone) on approach to a protein farm in northern California.
- Upgrade (2018), set in a not too distant future, highlights the hazardous side to automated cars as their driving systems can get hijacked and imperil the passengers.
- In the film Child's Play (2019) Chucky hijacks a self-driving "Kaslan Car" for the murder of Mike's mother, making it crash into normal cars at a department store's parking lot.
Intelligent or self-driving cars are a common theme in science fiction literature. Examples include:
- In Isaac Asimov's science-fiction short story, "Sally" (first published May–June 1953), automated cars have "positronic brains" and communicate via honking horns and slamming doors, and save their human caretaker. Due to the high cost of the brain, few can afford a personal vehicle, so buses have become the norm.
- Peter F. Hamilton's Commonwealth Saga series features intelligent or self-driving vehicles.
- In Robert A Heinlein's novel, The Number of the Beast (1980), Zeb Carter's driving and flying car "Gay Deceiver" is at first semi-automated and later, after modifications by Zeb's wife Deety, becomes sentient and capable of fully autonomous operation.
- In Edizioni Piemme's series Geronimo Stilton, a robotic vehicle called "Solar" is in the 54th book.
- Alastair Reynolds' series, Revelation Space, features intelligent or self-driving vehicles.
- In Daniel Suarez' novels Daemon (2006) and Freedom™ (2010) driverless cars and motorcycles are used for attacks in a software-based open-source warfare. The vehicles are modified for this using 3D printers and distributed manufacturing and are also able to operate as swarms.
- "Gone in 60 Seconds" season 2, episode 6 of 2015 TV series CSI: Cyber features three seemingly normal customized vehicles, a 2009 Nissan Fairlady Z Roadster, a BMW M3 E90 and a Cadillac CTS-V, and one stock luxury BMW 7 Series, being remote-controlled by a computer hacker.
- "Handicar", season 18, episode 4 of 2014 TV series South Park features a Japanese automated car that takes part in the Wacky Races-style car race.
- KITT and KARR, the Pontiac Firebird Trans-Ams in the 1982 TV series Knight Rider, were sentient and autonomous. The KITT and KARR based Ford Mustangs from Knight Rider were also sentient and autonomous, like their Firebird counterparts.
- "Driven", series 4 episode 11 of the 2003 TV series NCIS features a robotic vehicle named "Otto", part of a high-level project of the Department of Defense, which causes the death of a Navy Lieutenant, and then later almost kills Abby.
- The TV series Viper features a silver/grey armored assault vehicle, called The Defender, which masquerades as a flame-red 1992 Dodge Viper RT/10 and later as a 1998 cobalt blue Dodge Viper GTS. The vehicle's sophisticated computer systems allow it to be controlled via remote on some occasions.
- Black Mirror episode "Hated in the Nation" briefly features a self-driving SUV with a touchscreen interface on the inside.
- Bull has a show discussing the effectiveness and safety of self-driving cars in an episode call E.J.
- In "Rescue Bot Academy", season 3, episode 19 of Transformers: Rescue Bots, Chief Burns tells Jerry that the Autobot Blurr (whom Jerry had seen crash into a statue and discovered that there was no driver) is a self-driving car made by Doc Greene to prevent Blurr's secret from being revealed.
- Automated guideway transit
- Automatic train operation
- Automobile safety
- Automotive navigation system
- Advanced Driver Assistance Systems
- Connected car
- DARPA Grand Challenge: 2004, 2007
- DARPA Robotics Challenge (2012)
- Dutch Automated Vehicle Initiative
- Death by GPS
- Driverless tractor
- Hybrid navigation
- Intelligent transportation system
- Mobility as a service (transport)
- Personal rapid transit
- Platoon (automobile)
- Technological unemployment
- Unmanned ground vehicle
- Unmanned aerial vehicle
- Vehicle infrastructure integration
- Vision processing unit
- Measurement of Assured Clear Distance Ahead
- Electronic stability control
- Precrash system
- Taeihagh, Araz; Lim, Hazel Si Min (2 January 2019). "Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks". Transport Reviews. 39 (1): 103–128. arXiv:1807.05720. doi:10.1080/01441647.2018.1494640. ISSN 0144-1647.
- Maki, Sydney; Sage, Alexandria (19 March 2018). "Self-driving Uber car kills Arizona woman crossing street". Reuters. Retrieved 14 April 2019.
- Thrun, Sebastian (2010). "Toward Robotic Cars". Communications of the ACM. 53 (4): 99–106. doi:10.1145/1721654.1721679.
- Gehrig, Stefan K.; Stein, Fridtjof J. (1999). Dead reckoning and cartography using stereo vision for an automated car. IEEE/RSJ International Conference on Intelligent Robots and Systems. 3. Kyongju. pp. 1507–1512. doi:10.1109/IROS.1999.811692. ISBN 0-7803-5184-3.
- Lassa, Todd (January 2013). "The Beginning of the End of Driving". Motor Trend. Retrieved 1 September 2014.
- "European Roadmap Smart Systems for Automated Driving" (PDF). EPoSS. 2015. Archived from the original (PDF) on 12 February 2015.
- Lim, Hazel Si Min; Taeihagh, Araz (2019). "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities". Sustainability. 11 (20): 5791. doi:10.3390/su11205791.
- "Self-driving trucks are here — here's how they will transform the trucking industry" (Video). CNBC Videos. 13 April 2019. Retrieved 14 April 2019 – via Yahoo.
- "'Phantom Auto' will tour city". The Milwaukee Sentinel. 8 December 1926. Retrieved 23 July 2013.
- Vanderblit, Tom (6 February 2012). "Autonomous Cars Through The Ages". Wired. Retrieved 26 July 2018.
- Marc Weber. "Where to? A History of Autonomous Vehicles". Computer History Museum. Retrieved 26 July 2018.
- "Carnegie Mellon". Navlab: The Carnegie Mellon University Navigation Laboratory. The Robotics Institute. Retrieved 20 December 2014.
- Kanade, Takeo (February 1986). Autonomous land vehicle project at CMU. CSC '86 Proceedings of the 1986 ACM Fourteenth Annual Conference on Computer Science. Csc '86. pp. 71–80. doi:10.1145/324634.325197. ISBN 9780897911771.
- Wallace, Richard (1985). "First results in robot road-following" (PDF). JCAI'85 Proceedings of the 9th International Joint Conference on Artificial Intelligence. Archived from the original (PDF) on 6 August 2014.
- Schmidhuber, Jürgen (2009). "Prof. Schmidhuber's highlights of robot car history". Retrieved 15 July 2011.
- Turk, M.A.; Morgenthaler, D.G.; Gremban, K.D.; Marra, M. (May 1988). "VITS-a vision system for automated land vehicle navigation". IEEE Transactions on Pattern Analysis and Machine Intelligence. 10 (3): 342–361. doi:10.1109/34.3899. ISSN 0162-8828.
- University, Carnegie Mellon. "Look, Ma, No Hands-CMU News - Carnegie Mellon University". cmu.edu. Retrieved 2 March 2017.
- "Navlab 5 Details". cs.cmu.edu. Retrieved 2 March 2017.
- Crowe, Steve. "Back to the Future: Autonomous Driving in 1995 - Robotics Trends". roboticstrends.com. Retrieved 2 March 2017.
- "NHAA Journal". cs.cmu.edu. Retrieved 5 March 2017.
- Council, National Research (2002). Technology Development for Army Unmanned Ground Vehicles. doi:10.17226/10592. ISBN 9780309086202.
- Ackerman, Evan (25 January 2013). "Video Friday: Bosch and Cars, ROVs and Whales, and Kuka Arms and Chainsaws". IEEE Spectrum. Retrieved 26 February 2013.
- "Audi of America / news / Pool / Reaffirmed Mission for Autonomous Audi TTS Pikes Peak". AudiUSA.com. Archived from the original on 10 July 2012. Retrieved 28 April 2012.
- "Nissan car drives and parks itself at Ceatec". BBC. 4 October 2012. Retrieved 4 January 2013.
- "Toyota sneak previews self-drive car ahead of tech show". BBC. 4 January 2013. Retrieved 4 January 2013.
- Rosen, Rebecca (9 August 2012). "Google's Self-Driving Cars: 300,000 Miles Logged, Not a Single Accident Under Computer Control". The Atlantic. Retrieved 10 August 2012.
- "Vislab, University of Parma, Italy – 8000 miles driverless test begins". Archived from the original on 14 November 2013. Retrieved 27 October 2013.
- "VisLab Intercontinental Autonomous Challenge: Inaugural Ceremony – Milan, Italy". Retrieved 27 October 2013.
- Selyukh, Alina. "A 24-Year-Old Designed A Self-Driving Minibus; Maker Built It In Weeks". All Tech Considered. NPR. Retrieved 21 July 2016.
- Novak, Matt. "The National Automated Highway System That Almost Was". Smithsonian. Retrieved 8 June 2018.
- "Back to the Future: Autonomous Driving in 1995 – Robotics Business Review". Robotics Business Review. 3 April 2015. Retrieved 8 June 2018.
- "This Is Big: A Robo-Car Just Drove Across the Country". WIRED. Retrieved 8 June 2018.
- Ramsey, John (1 June 2015). "Self-driving cars to be tested on Virginia highways". Richmond Times-Dispatch. Retrieved 4 June 2015.
- Meyer, Gereon (2018). European Roadmaps, Programs, and Projects for Innovation in Connected and Automated Road Transport. In: G. Meyer, S. Beiker, Road Vehicle Automation 5. Springer 2018. doi:10.1007/978-3-319-94896-6_3.
- European Commission (2019). STRIA Roadmap Connected and Automated Transport: Road, Rail and Waterborne (PDF).
- McAleer, Michael (11 July 2017). "Audi's self-driving A8: drivers can watch YouTube or check emails at 60km/h". The Irish Times. Retrieved 11 July 2017.
- Hawkins, Andrew J. (7 November 2017). "Waymo is first to put fully self-driving cars on US roads without a safety driver". theverge.com. Retrieved 7 November 2017.
- "Early rider program - FAQ – Early Rider Program – Waymo". Waymo. Retrieved 30 November 2018.
- "On the Road – Waymo". Waymo. Archived from the original on 23 March 2018. Retrieved 27 July 2018.
- "Waymo launches nation's first commercial self-driving taxi service in Arizona". Washington Post. Retrieved 6 December 2018.
- "Autonomous Vehicles". Smart Nation Singapore. 2 May 2019. Retrieved 31 August 2019.
- Umar Zakir Abdul, Hamid; et al. (2019). "Current Landscape of the Automotive Field in the ASEAN Region: Case Study of Singapore, Malaysia and Indonesia - A Brief Overview". ASEAN Journal of Automotive Technology. 1 (1). Retrieved 5 October 2019.
- Leggett, Theo (22 May 2018). "Who is to blame for 'self-driving car' deaths?". BBC News – via www.bbc.co.uk.
- Cellan-Jones, Rory (12 June 2018). "Insurers warning on 'autonomous' cars". BBC News – via www.bbc.co.uk.
- Antsaklis, Panos J.; Passino, Kevin M.; Wang, S.J. (1991). "An Introduction to Autonomous Control Systems" (PDF). IEEE Control Systems Magazine. 11 (4): 5–13. doi:10.1109/37.88585. Archived from the original (PDF) on 16 May 2017. Retrieved 21 January 2019.
- Wood, S. P.; Chang, J.; Healy, T.; Wood, J. "The potential regulatory challenges of increasingly autonomous motor vehicles". 52nd Santa Clara Law Review. 4 (9): 1423–1502.
- "Autonomous Emergency Braking – Euro NCAP". euroncap.com.
- "self-driving car Definition from PC Magazine Encyclopedia". pcmag.com.
- "Self-Driving Cars Explained". Union of Concerned Scientists.
- "Support – Autopilot". Tesla. Archived from the original on 10 April 2019. Retrieved 6 September 2019.
- "AdaptIVe system classification and glossary on Automated driving" (PDF). Archived from the original (PDF) on 7 October 2017. Retrieved 11 September 2017.
- "AUTOMATED DRIVING LEVELS OF DRIVING AUTOMATION ARE DEFINED IN NEW SAE INTERNATIONAL STANDARD J3016" (PDF). 2017. Archived from the original (PDF) on 20 November 2016.
- "U.S. Department of Transportation Releases Policy on Automated Vehicle Development". National Highway Traffic Safety Administration. 30 May 2013. Retrieved 18 December 2013.
- SAE International
- "Automated Driving – Levels of Driving Automation are Defined in New SAE International Standard J3016" (PDF). SAE International. 2014. Archived (PDF) from the original on 1 July 2018.
- Hancock, P. A.; Nourbakhsh, Illah; Stewart, Jack (16 April 2019). "On the future of transportation in an era of automated and autonomous vehicles". Proceedings of the National Academy of Sciences of the United States of America. 116 (16): 7684–7691. doi:10.1073/pnas.1805770115. ISSN 0027-8424. PMC 6475395. PMID 30642956.
- Zhao, Jianfeng; Liang, Bodong; Chen, Qiuxia (2 January 2018). "The key technology toward the self-driving car". International Journal of Intelligent Unmanned Systems. 6 (1): 2–20. doi:10.1108/IJIUS-08-2017-0008. ISSN 2049-6427.
- Zhu, Wentao; Miao, Jun; Hu, Jiangbi; Qing, Laiyun (27 March 2014). "Vehicle detection in driving simulation using extreme learning machine". Neurocomputing. 128: 160–165. doi:10.1016/j.neucom.2013.05.052.
- Durrant-Whyte, H.; Bailey, T. (5 June 2006). "Simultaneous localization and mapping". IEEE Robotics & Automation Magazine. 13 (2): 99–110. CiteSeerX 10.1.1.135.9810. doi:10.1109/mra.2006.1638022. ISSN 1070-9932.
- Huval, Brody; Wang, Tao; Tandon, Sameep; Kiske, Jeff; Song, Will; Pazhayampallil, Joel (2015). "An Empirical Evaluation of Deep Learning on Highway Driving". arXiv:1504.01716 [cs.RO].
- Peter Corke, Jorge Lobo, Jorge Dias (1 June 2007). "An Introduction to Inertial and Visual Sensing". The International Journal of Robotics Research. 26 (6): 519–535. CiteSeerX 10.1.1.93.5523. doi:10.1177/0278364907079279.CS1 maint: multiple names: authors list (link)
- "How Self-Driving Cars Work". 14 December 2017. Retrieved 18 April 2018.
- Schmidhuber, Jürgen (January 2015). "Deep learning in neural networks: An overview". Neural Networks. 61: 85–117. arXiv:1404.7828. doi:10.1016/j.neunet.2014.09.003. PMID 25462637.
- Hawkins, Andrew J. (13 May 2018). "MIT built a self-driving car that can navigate unmapped country roads". theverge.com. Retrieved 14 May 2018.
- Connor-Simons, Adam; Gordon, Rachel (7 May 2018). "Self-driving cars for country roads: Today's automated vehicles require hand-labeled 3-D maps, but CSAIL's MapLite system enables navigation with just GPS and sensors". Retrieved 14 May 2018.
- Reuters, Thomson, No lights, no signs, no accidents - future intersections for..., retrieved 26 November 2018
- "What's big, orange and covered in LEDs? This start-up's new approach to self-driving cars". NBC News.
- Gold, Christian; Körber, Moritz; Hohenberger, Christoph; Lechner, David; Bengler, Klaus (1 January 2015). "Trust in Automation – Before and After the Experience of Take-over Scenarios in a Highly Automated Vehicle". Procedia Manufacturing. 3: 3025–3032. doi:10.1016/j.promfg.2015.07.847. ISSN 2351-9789.
- "Survey Data Suggests Self-Driving Cars Could Be Slow To Gain Consumer Trust". GM Authority. Retrieved 3 September 2018.
- "Remembering When Driverless Elevators Drew Skepticism". NPR.org.
- "Episode 642: The Big Red Button". NPR.org.
- "Mcity testing center". University of Michigan. 8 December 2016. Retrieved 13 February 2017.
- "Adopted Regulations for Testing of Autonomous Vehicles by Manufacturers". DMV. 18 June 2016. Retrieved 13 February 2017.
- "The Pathway to Driverless Cars: A Code of Practice for testing". 19 July 2015. Retrieved 8 April 2017.
- "Automobile simulation example". Cyberbotics. 18 June 2018. Retrieved 18 June 2018.
- Hallerbach, Sven; Xia, Yiqun; Eberle, Ulrich; Koester, Frank (3 April 2018). "Simulation-based Identification of Critical Scenarios for Cooperative and Automated Vehicles". Toolchain for simulation-based development and testing of Automated Driving. SAE World Congress 2018. SAE Technical Paper Series. 1. pp. 93–106. doi:10.4271/2018-01-1066. Retrieved 22 December 2018.
- "Apply for an Autonomous Vehicle Technology Demonstration / Testing Permit". 9 May 2017.
- Krok, Andrew. "Apple increases self-driving test fleet from 3 to 27". Roadshow. Retrieved 26 January 2018.
- Hall, Zac (20 March 2018). "Apple ramping self-driving car testing, more CA permits than Tesla and Uber". Electrek. Retrieved 21 March 2018.
- Ведомости (16 February 2018). ""Яндекс" испытал беспилотное такси на заснеженных улицах Москвы" ["Yandex" has tested a driverless taxi on snowy streets of Moscow]. vedomosti.ru. Retrieved 30 July 2019.
- "A Year of Yandex Self-Driving Milestones". yandex.com. Retrieved 1 May 2019.
- "Yandex Self-Driving Car. First Long-Distance Ride". youtube.com. Retrieved 1 May 2019.
- "Компания "Яндекс" презентовала беспилотный автомобиль" [Yandex presented driverless car]. priumnojay.ru. Retrieved 30 July 2019.
- ""Яндекс" начал испытания собственного беспилотного автомобиля в Лас-Вегасе" ["Yandex" has started testing their driverless car in Las Vegas]. abctv.kz. Retrieved 30 July 2019.
- "Беспилотники Яндекса испытают в Израиле" [Yandex's driverless cars will be tested in Israel]. robotrends.ru (in Russian). Retrieved 30 July 2019.
- Wang, Brian (25 March 2018). "Uber' self-driving system was still 400 times worse [than] Waymo in 2018 on key distance intervention metric". NextBigFuture.com. Retrieved 25 March 2018.
- "First self-driving race car completes 1.8 kilometre track". euronews. 16 July 2018. Retrieved 17 July 2018.
- California Department of Motor Vehicles. "Distance between disengagements". Statista. Retrieved 21 December 2019.
- "Driverless cars take to the road". E.U.CORDIS Research Program CitynetMobil. Retrieved 27 October 2013.
- "Snyder OKs self-driving vehicles on Michigan's roads". Detroit News. 27 December 2013. Retrieved 1 January 2014.
- "BBC News – UK to allow driverless cars on public roads in January". BBC News. 30 July 2014. Retrieved 4 March 2015.
- Burn-Callander, Rebecca (11 February 2015). "This is the Lutz pod, the UK's first driverless car". Daily Telegraph. Retrieved 11 February 2015.
- "Autonomous vehicle: the automated driving car of the future". PSA PEUGEOT CITROËN. Archived from the original on 26 September 2015. Retrieved 2 October 2015.
- Valeo Autonomous iAV Car Driving System CES 2015. YouTube. 5 January 2015.
- Hayward, Michael (26 January 2017). "First New Zealand autonomous vehicle demonstration kicks off at Christchurch Airport". stuff.co.nz. Retrieved 23 March 2017.
- "Self-driving car to take on Tauranga traffic this week". Bay of Plenty Times. 15 November 2016. Retrieved 23 March 2017.
- "NZ's first self-drive vehicle demonstration begins". stuff.co.nz. 17 November 2016. Retrieved 23 March 2017.
- Frykberg, Eric (28 June 2016). "Driverless buses: 'It is going to be big'". Radio New Zealand. Retrieved 23 March 2017.
- "China's first Level 4 self-driving shuttle enters volume production". newatlas.com.
- LLC, Baidu USA (4 July 2018). "Baidu Joins Forces with Softbank's SB Drive, King Long to Bring Apollo-Powered Autonomous Buses to Japan". GlobeNewswire News Room.
- Larson, William; Zhao, Weihua (23 December 2019). "Self-driving cars and the city: Effects on sprawl, energy consumption, and housing affordability". Regional Science and Urban Economics: 103484. doi:10.1016/j.regsciurbeco.2019.103484. ISSN 0166-0462.
- "[INFOGRAPHIC] Autonomous Cars Could Save The US $1.3 Trillion Dollars A Year". businessinsider.com. 12 September 2014. Retrieved 3 October 2014.
- Miller, John (19 August 2014). "Self-Driving Car Technology's Benefits, Potential Risks, and Solutions". theenergycollective.com. Archived from the original on 8 May 2015. Retrieved 4 June 2015.
- Whitwam, Ryan (8 September 2014). "How Google's self-driving cars detect and avoid obstacles". ExtremeTech. Retrieved 4 June 2015.
- Cowen, Tyler (28 May 2011). "Can I See Your License, Registration and C.P.U.?". The New York Times.
- Ramsey, Mike (3 May 2015). "Self-Driving Cars Could Cut Down on Accidents, Study Says". The Wall Street Journal. Retrieved 29 October 2016.
- Ramsey, Jonathon (8 March 2017). "The Way We Talk About Autonomy Is a Lie, and That's Dangerous". thedrive.com. Retrieved 19 March 2018.
- "An Open Source Self-Driving Car". Udacity. Retrieved 12 July 2017.
- Fazzini, Kate (13 August 2018). "Elon Musk: Tesla to open-source some self-driving software for safety". cnbc.com.
- Staff, Ars (24 April 2018). "This startup's CEO wants to open-source self-driving car safety testing". Ars Technica.
- Light, Donald (8 May 2012). A Scenario" The End of Auto Insurance (Technical report). Celent.
- Mui, Chunka (19 December 2013). "Will The Google Car Force A Choice Between Lives And Jobs?". Forbes. Retrieved 19 December 2013.
- Gosman, Tim (24 July 2016). "Along for the ride: How driverless cars can become commonplace". Brand Union. Retrieved 29 October 2016.
- Dudley, David (January 2015). "The Driverless Car Is (Almost) Here; The self-driving car — a godsend for older Americans — is now on the horizon". AARP the Magazine. Retrieved 30 November 2015.
- "Driver licensing system for older drivers in New South Wales, Australia". NSW Government. 30 June 2016. Retrieved 16 May 2018.
- Stenquist, Paul (7 November 2014). "In Self-Driving Cars, a Potential Lifeline for the Disable". The New York Times. Retrieved 29 October 2016.
- Curry, David (22 April 2016). "Will elderly and disabled gain most from autonomous cars?". ReadWrite. Retrieved 29 October 2016.
- James M. Anderson; Nidhi Kalra; Karlyn D. Stanley; Paul Sorensen; Constantine Samaras; Oluwatobi A. Oluwatola (2016). "Autonomous Vehicle Technology: A Guide for Policymakers". RAND Corporation. Retrieved 30 October 2016.
- Simonite, Tom (1 November 2014). "Self-Driving Motorhome: RV Of the Future?". Archived from the original on 5 January 2016. Retrieved 1 November 2015. Cite journal requires
- "Get ready for automated cars". Houston Chronicle. 11 September 2012. Retrieved 5 December 2012.
- Simonite, Tom (25 October 2013). "Data Shows Google's Robot Cars Are Smoother, Safer Drivers Than You or I". MIT Technology Review. Retrieved 15 November 2013.
- O'Toole, Randal (18 January 2010). Gridlock: Why We're Stuck in Traffic and What To Do About It. Cato Institute. p. 192. ISBN 978-1-935308-24-9.
- "Future Car Focus: Robot Cars". MSN Autos. 2013. Archived from the original on 12 January 2013. Retrieved 27 January 2013.
- Ackerman, Evan (4 September 2012). "Study: Intelligent Cars Could Boost Highway Capacity by 273%". Institute of Electrical and Electronics Engineers (IEEE). IEEE Spectrum. Retrieved 29 October 2016.
- Gibson, David K. (28 April 2016). "Can we banish the phantom traffic jam?". BBC.
- "Autonomous Intersection Management – FCFS policy with 6 lanes in all directions". The University of Texas at Austin Computer Science Department. 12 June 2009. Retrieved 28 April 2012.
- Taiebat, Morteza; Brown, Austin; Safford, Hannah; Qu, Shen; Xu, Ming (2018). "A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles". Environmental Science & Technology. 52 (20): 11449–11465. arXiv:1901.10581. doi:10.1021/acs.est.8b00127. PMID 30192527.
- Pyper, Julia (15 September 2015). "Self-Driving Cars Could Cut Greenhouse Gas Pollution". Scientific American. Retrieved 25 December 2018.
- "Spaced Out parking report". racfoundation.org. Retrieved 3 September 2018.
- ""Cars are parked 95% of the time". Let's check!". reinventingparking.org. Retrieved 3 September 2018.
- Chester, Mikhail; Fraser, Andrew; Matute, Juan; Flower, Carolyn; Pendyala, Ram (2 October 2015). "Parking Infrastructure: A Constraint on or Opportunity for Urban Redevelopment? A Study of Los Angeles County Parking Supply and Growth". Journal of the American Planning Association. 81 (4): 268–286. doi:10.1080/01944363.2015.1092879. ISSN 0194-4363.
- "See Just How Much Of A City's Land Is Used For Parking Spaces". Fast Company. 20 July 2017. Retrieved 3 September 2018.
- Stewart, Jack (25 March 2018). "Forget Self Driving. The Future is in Self Parking". Wired.
- Woodyard, Chris (5 March 2015). "McKinsey study: Self-driving cars yield big benefits". USA Today. Retrieved 4 June 2015.
- "Self-driving cars: The next revolution" (PDF). kpmg.com. 5 September 2013. Retrieved 6 September 2013.
- Miller, Owen. "Robotic Cars and Their New Crime Paradigms". Retrieved 4 September 2014.
- "Mass unemployment fears over Google artificial intelligence plans". London. 29 December 2013. Retrieved 29 December 2013.
- Dvorak, John C. (30 September 2015). "There's a Bumpy Road Ahead for Driverless Cars". PCMag. Retrieved 30 September 2015.
- Benedikt Frey, Carl; Osborne, Michael A. (1 January 2017). "The future of employment: How susceptible are jobs to computerisation?". Technological Forecasting and Social Change. 114: 254–280. CiteSeerX 10.1.1.395.416. doi:10.1016/j.techfore.2016.08.019. ISSN 0040-1625.
- Neumann, Peter G. (September 2016). "Risks of Automation: A Cautionary Total-system Perspective of Our Cyberfuture". Commun. ACM. 59 (10): 26–30. doi:10.1145/2988445. ISSN 0001-0782.
- Acharya, Anish (16 December 2014). "Are We Ready for Driver-less Vehicles? Security vs. Privacy – A Social Perspective". arXiv:1412.5207 [cs.CY].
- Patrick Lin (22 January 2014). "What If Your Autonomous Car Keeps Routing You Past Krispy Kreme?". The Atlantic. Retrieved 22 January 2014.
- Glielmo, Luigi. "Vehicle-to-Vehicle/Vehicle-to-Infrastructure Control" (PDF).
- Mark Harris (16 July 2014). "FBI warns driverless cars could be used as 'lethal weapons'". The Guardian.
- Smith, Noah (5 November 2015). "The downside of driverless cars". The Sydney Morning Herald. Retrieved 30 October 2016.
- Ufberg, Max (15 October 2015). "Whoops: The Self-Driving Tesla May Make Us Love Urban Sprawl Again". Wired. Retrieved 28 October 2016.
- Sparrow, Robert; Howard, Mark (2017). "When human beings are like drunk robots: Driverless vehicles, ethics, and the future of transport". Transportation Research Part C: Emerging Technologies. 80: 206–215. doi:10.1016/j.trc.2017.04.014.
- Merat, Natasha; Jamson, A. Hamish (June 2009). "How do drivers behave in a highly automated car?" (PDF). Drive Assessment 2009 Proceedings. pp. 514–521. ISBN 9-78087414162-7.
Drivers' response to all critical events was found to be much later in the automated driving condition, compared to manual driving.
- Spangler, Todd. "Self-driving cars programmed to decide who dies in a crash". USA Today. Detroit Free Press. Retrieved 29 November 2017.
- Maxmen, Amy. "Self-driving Car Dilemmas Reveal That Moral Choices Are Not Universal." Nature 562.7728 (2018): 469-470. Web.
- Goodall, Noah (June 2016). "Can you program ethics into a self-driving car?". IEEE Spectrum. 53 (6): 25–28. doi:10.1109/MSPEC.2016.7473149.
- Nicholas, Negroponte (1 January 2000). Being digital. Vintage Books. ISBN 978-0679762904. OCLC 68020226.
- Adhikari, Richard (11 February 2016). "Feds Put AI in the Driver's Seat". Technewsworld. Retrieved 12 February 2016.
- Nichols, Greg (13 February 2016). "NHTSA chief takes conservative view on autonomous vehicles: "If you had perfect, connected autonomous vehicles on the road tomorrow, it would still take 20 to 30 years to turn over the fleet."". ZDNet. Retrieved 17 February 2016.
- "New Allstate Survey Shows Americans Think They Are Great Drivers – Habits Tell a Different Story". PR Newswire. 2 August 2011. Retrieved 7 September 2013.
- Henn, Steve (31 July 2015). "Remembering When Driverless Elevators Drew Skepticism". NPR. Retrieved 14 August 2016.
- "Will Regulators Allow Self-Driving Cars In A Few Years?". Forbes. 24 September 2013. Retrieved 5 January 2014.
- "Reliance on autopilot is now the biggest threat to flight safety, study says". 18 November 2013. Retrieved 19 November 2013.
- Patrick Lin (8 October 2013). "The Ethics of Autonomous Cars". The Atlantic.
- Tim Worstall (18 June 2014). "When Should Your Driverless Car From Google Be Allowed To Kill You?". Forbes.
- Alexander Skulmowski; Andreas Bunge; Kai Kaspar; Gordon Pipa (16 December 2014). "Forced-choice decision-making in modified trolley dilemma situations: a virtual reality and eye tracking study". Frontiers in Behavioral Neuroscience. 8: 426. doi:10.3389/fnbeh.2014.00426. PMC 4267265. PMID 25565997.
- Gomes, Lee (28 August 2014). "Hidden Obstacles for Google's Self-Driving Cars". MIT Technology Review. Retrieved 22 January 2015.
- SingularityU The Netherlands (1 September 2016), Carlo van de Weijer on real intelligence, retrieved 21 November 2016
- "Hackers find ways to hijack car computers and take control". 3 September 2013. Retrieved 7 September 2013.
- Philip E. Ross (11 April 2014). "A Cloud-Connected Car Is a Hackable Car, Worries Microsoft". IEEE Spectrum. Retrieved 23 April 2014.
- Moore-Colyer, Roland (12 February 2015). "Driverless cars face cyber security, skills and safety challenges". v3.co.uk. Retrieved 24 April 2015.
- Petit, J.; Shladover, S.E. (1 April 2015). "Potential Cyberattacks on Automated Vehicles". IEEE Transactions on Intelligent Transportation Systems. 16 (2): 546–556. doi:10.1109/TITS.2014.2342271. ISSN 1524-9050.
- Ron Tussy (29 April 2016). "Challenges facing Autonomous Vehicle Development". AutoSens. Retrieved 5 May 2016.
- Zhou, Naaman (1 July 2017). "Volvo admits its self-driving cars are confused by kangaroos". The Guardian. Retrieved 1 July 2017.
- Glenn Garvin (21 March 2014). "Automakers say self-driving cars are on the horizon". Miami Herald. Retrieved 22 March 2014.
- Badger, Emily (15 January 2015). "5 confounding questions that hold the key to the future of driverless cars". Wonk Blog. The Washington Post. Retrieved 22 January 2015.
- Brodsky, Jessica (2016). "Autonomous Vehicle Regulation: How an Uncertain Legal Landscape May Hit the Brakes on Self-Driving Cars". Berkeley Technology Law Journal. 31 (Annual Review 2016): 851–878. Retrieved 29 November 2017.
- Silver, David (20 January 2018). "Limited talent pool is standing in the way of driverless cars". The Next Web.
- "DIY Robocars first year in review".
- Laursen, Lucas (28 August 2017). "The Tech That Won the First Formula Student Driverless Race". IEEE Spectrum.
- "udacity/self-driving-car". GitHub. 31 December 2018.
- "Berkeley Deep Drive". bdd-data.berkeley.edu.
- "Glossary – Level Five Jobs". levelfivejobs.com. 27 July 2018.
- "You can take a ride in a self-driving Lyft during CES". The Verge. Retrieved 26 November 2018.
- Adams, Ian (30 December 2016). "Self-Driving Cars Will Make Organ Shortages Even Worse". Slate. Retrieved 9 November 2018.
- Snow, Shawn (29 August 2017). "The US Army is developing autonomous armored vehicles". Army Times. Retrieved 26 November 2018.
- "Driver-less car design: Sleep-walking into the future?". 5 April 2016. Archived from the original on 5 April 2016. Retrieved 26 November 2018.
- Company, Ford Motor (7 January 2019). "How 'Talking' and 'Listening' Vehicles Could Make Roads Safer, Cities Better". Medium. Retrieved 8 June 2019.
- "Volvo's Fully Autonomous 360c Concept Vehicle Even Lets You Sleep In It". 6 September 2018. Retrieved 26 November 2018.
- Ashley Jalsey III, Driverless cars promise far greater mobility for the elderly and people with disabilities, Washington Post (November 23, 2017).
- Henry Claypool, Amitai Bin-Nun & Jeffrey Gerlach, Self-Driving Cars: The Impact on People with Disabilities (January 2017), Ruderman Family Foundation/Securing America's Future Energy.
- McParland, Tom. "Why Autonomous Cars Could Be The Change Disabled People Need". Jalopnik. Retrieved 26 November 2018.
- Nelson, Gabe (14 October 2015). "Tesla beams down 'autopilot' mode to Model S". Automotive News. Retrieved 19 October 2015.
- Zhang, Benjamin (10 January 2016). "ELON MUSK: In 2 years your Tesla will be able to drive from New York to LA and find you". Automotive News. Retrieved 12 January 2016.
- Charlton, Alistair (13 June 2016). "Tesla Autopilot is 'trying to kill me', says Volvo R&D chief". International Business Times. Retrieved 1 July 2016.
- Golson, Jordan (27 April 2016). "Volvo autonomous car engineer calls Tesla's Autopilot a 'wannabe'". The Verge. Retrieved 1 July 2016.
- Korosec, Kirsten (15 December 2015). "Elon Musk Says Tesla Vehicles Will Drive Themselves in Two Years". Fortune. Retrieved 1 July 2016.
- "Path to Autonomy: Self-Driving Car Levels 0 to 5 Explained". Car and Driver. 3 October 2017. Retrieved 1 January 2019.
- Abuelsamid, Sam (1 July 2016). "Tesla Autopilot Fatality Shows Why Lidar And V2V Will Be Necessary For Autonomous Cars". Forbes. Retrieved 1 July 2016.
- Horwitz, Josh; Timmons, Heather (20 September 2016). "There are some scary similarities between Tesla's deadly crashes linked to Autopilot". Quartz. Retrieved 19 March 2018.
- "China's first accidental death due to Tesla's automatic driving: not hitting the front bumper". China State Media (in Chinese). 14 September 2016. Retrieved 18 March 2018.
- Felton, Ryan (27 February 2018). "Two Years On, A Father Is Still Fighting Tesla Over Autopilot And His Son's Fatal Crash". jalopnik.com. Retrieved 18 March 2018.
- Yadron, Danny; Tynan, Dan (1 July 2016). "Tesla driver dies in first fatal crash while using autopilot mode". The Guardian. San Francisco. Retrieved 1 July 2016.
- Vlasic, Bill; Boudette, Neal E. (30 June 2016). "Self-Driving Tesla Involved in Fatal Crash". The New York Times. Retrieved 1 July 2016.
- Office of Defects Investigations, NHTSA (28 June 2016). "ODI Resume – Investigation: PE 16-007" (PDF). National Highway Traffic Safety Administration (NHTSA). Retrieved 2 July 2016.
- Shepardson, David (12 July 2016). "NHTSA seeks answers on fatal Tesla Autopilot crash". Automotive News. Retrieved 13 July 2016.
- "A Tragic Loss" (Press release). Tesla Motors. 30 June 2016. Retrieved 1 July 2016.
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.
- Abuelsamid, Sam. "Adding Some Statistical Perspective To Tesla Autopilot Safety Claims".
- Administration, National Highway Traffic Safety. "FARS Encyclopedia".
- Alan Levin; Jeff Plungis (8 July 2016). "NTSB to scrutinize driver automation with probe of Tesla crash". Automotive News. Retrieved 11 July 2016.
- "Fatal Tesla Autopilot accident investigation ends with no recall ordered". The Verge. 19 January 2016. Retrieved 19 January 2017.
- "All Tesla Cars Being Produced Now Have Full Self-Driving Hardware". 19 October 2016.
- "Autopilot: Full Self-Driving Hardware on All Cars". Tesla Motors. Retrieved 21 October 2016.
- Guess, Megan (20 October 2016). "Teslas will now be sold with enhanced hardware suite for full autonomy". Ars Technica. Retrieved 20 October 2016.
- Self-driving Car Logs More Miles, googleblog
- A First Drive. YouTube. 27 May 2014.
- "Google Self-Driving Car Project, Monthly Report, March 2016" (PDF). Retrieved 23 March 2016.
- "Waymo". Waymo.
- Davies, Alex (13 December 2016). "Meet the Blind Man Who Convinced Google Its Self-Driving Car Is Finally Ready". Wired.
- "For the first time, Google's self-driving car takes some blame for a crash". Washington Post. 29 February 2016.
- "Google founder defends accident records of self-driving cars". Associated Press. Los Angeles Times. 3 June 2015. Retrieved 1 July 2016.
- VISHAL MATHUR (17 July 2015). "Google Autonomous Car Experiences Another Crash". Government Technology. Retrieved 18 July 2015.
- "Google's Self-Driving Car Caused Its First Crash". Wired. February 2016.
- "Passenger bus teaches Google robot car a lesson". Los Angeles Times. 29 February 2016.
- "Uber to Suspend Autonomous Tests After Arizona Accident". 25 March 2017 – via www.bloomberg.com.
- "Uber's Self-Driving Cars Hit 2 Million Miles As Program Regains Momentum". 22 December 2017 – via www.forbes.com.
- Bensinger, Greg; Higgins, Tim (22 March 2018). "Video Shows Moments Before Uber Robot Car Rammed Into Pedestrian". Wall Street Journal. Retrieved 25 March 2018.
- Lubben, Alex (19 March 2018). "Self-driving Uber killed a pedestrian as human safety driver watched". Vice News. Retrieved 19 March 2018.
- "Human Driver Could Have Avoided Fatal Uber Crash, Experts Say". 22 March 2018 – via www.bloomberg.com.
- The Associated Press; abc15.com staff (27 March 2018). "Governor Ducey suspends Uber from automated vehicle testing". KNXV-TV. Retrieved 27 March 2018.
- Said, Carolyn (27 March 2018). "Uber puts the brakes on testing robot cars in California after Arizona fatality". San Francisco Chronicle. Retrieved 8 April 2018.
- "Preliminary Report Released for Crash Involving Pedestrian, Uber Technologies, Inc., Test Vehicle" (PDF). 24 May 2018.
- Gibbs, Samuel (9 November 2017). "Self-driving bus involved in crash less than two hours after Las Vegas launch". The Guardian. Retrieved 9 November 2017.
- Lim, Hazel Si Min; Taeihagh, Araz (2018). "Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications". Energies. 11 (5): 1062. doi:10.3390/en11051062.
- Lee, Timothy (31 January 2015). "Driverless cars will mean the end of mass car ownership". Vox. Retrieved 31 January 2015.
- O'Toole, Randal, Policy Implications of Autonomous Vehicles (18 September 2014). Cato Institute Policy Analysis No. 758. Available at SSRN: https://ssrn.com/abstract=2549392
- Pinto, Cyrus (2012). "How autonomous vehicle policy in California and Nevada addresses technological and non-technological liabilities". Intersect: The Stanford Journal of Science, Technology and Society. 5.
- Badger, Emily (15 January 2015). "5 confounding questions that hold the key to the future of driverless cars". Washington Post. ISSN 0190-8286. Retrieved 27 November 2017.
- Guerra, Erick (1 June 2016). "Planning for Cars That Drive Themselves: Metropolitan Planning Organizations, Regional Transportation Plans, and Autonomous Vehicles". Journal of Planning Education and Research. 36 (2): 210–224. doi:10.1177/0739456X15613591. ISSN 0739-456X.
- Litman, Todd. "Autonomous vehicle implementation predictions." Victoria Transport Policy Institute 28 (2014).
- Humphreys, Pat (19 August 2016). "Retail Revolution". Transport and Travel. Retrieved 24 August 2016.
- JafariNaimi, Nassim (2018). "Our Bodies in the Trolley's Path, or Why Self-driving Cars Must *Not* Be Programmed to Kill". Science, Technology, & Human Values. 43 (2): 302–323. doi:10.1177/0162243917718942.
- Jain, Lochlann (2004). ""Dangerous instrumentality": the bystander as subject in automobility". Cultural Anthropology. 19 (1): 61–94. doi:10.1525/can.2004.19.1.61.
- "GAR – 1968 Vienna Convention". 1 December 2017. Archived from the original on 1 December 2017.
- Bryant Walker Smith (1 November 2012). "Automated Vehicles Are Probably Legal in The United States". The Center for Internet and Society (CIS) at Stanford Law School. Retrieved 31 January 2013.
- Canis, Bill (19 September 2017). Issues in Autonomous Vehicle Deployment (PDF). Washington, DC: Congressional Research Service. Retrieved 16 October 2017.
- Bryant Walker Smith. "Automated Driving: Legislative and Regulatory Action". The Center for Internet and Society (CIS) at Stanford Law School. Retrieved 31 January 2013.
- Kang, Cecilia (19 September 2016). "Self-Driving Cars Gain Powerful Ally: The Government". The New York Times. ISSN 0362-4331. Retrieved 28 September 2016.
- "Nevada enacts law authorizing autonomous (driverless) vehicles". Green Car Congress. 25 June 2011. Retrieved 25 June 2011.
- Alex Knapp (22 June 2011). "Nevada Passes Law Authorizing Driverless Cars". Forbes. Archived from the original on 28 June 2011. Retrieved 25 June 2011.
- Christine Dobby (24 June 2011). "Nevada state law paves the way for driverless cars". Financial Post. Retrieved 25 June 2011.
- John Markoff (10 May 2011). "Google Lobbies Nevada To Allow Self-Driving Cars". The New York Times. Retrieved 11 May 2011.
- "Bill AB511 Nevada Legislature" (PDF). Nevada Legislature. Retrieved 25 June 2011.
- Tim Healey (24 June 2011). "Nevada Passes Law Allowing Self-Driving Cars". Motor Trend. Retrieved 25 June 2011.
- Cy Ryan (7 May 2012). "Nevada issues Google first license for self-driving car". Las Vegas Sun. Retrieved 12 May 2012.
- Valdes, Ana M. (5 July 2012). "Florida embraces self-driving cars, as engineers and lawmakers prepare for the new technology". WPTV. Archived from the original on 12 April 2013.
- Oram, John (27 September 2012). "Governor Brown Signs California Driverless Car Law at Google HQ". Archived from the original on 30 September 2012.
- "New Law Allows Driverless Cars On Michigan Roads". CBS Detroit. 28 December 2013. Retrieved 2 November 2014.
- Selle, Jeff (7 August 2014). "Aye, Robot: Cd'A City Council approves robot ordinance". Coeur d'Alene Press.
- "Bill Text – AB-2866 Autonomous vehicles". leginfo.legislature.ca.gov. Retrieved 2 November 2019.
- "Federal Automated Vehicles Policy". Department of Transportation. 14 September 2016. Retrieved 20 October 2016.
- "Public Workshop Autonomous Vehicles" (PDF). 19 October 2016. Retrieved 20 September 2017.
- Levin, Sam (15 December 2016). "Uber blames humans for self-driving car traffic offenses as California orders a halt". The Guardian. Retrieved 15 December 2016.
- "UK to road test driverless cars". BBC. 16 July 2013. Retrieved 17 July 2013.
- "Des véhicules autonomes sur route ouverte à Bordeaux en octobre 2015". usine-digitale.fr.
- Greenblatt, Nathan (19 January 2016). "Self-Driving Cars Will Be Ready Before Our Laws Are". IEEE Spectrum.
- "Swisscom reeals the first driverless car on Swiss roads". Swisscom. 12 May 2015. Archived from the original on 28 September 2015. Retrieved 1 August 2015.
- "Zalazone home page". zalazone.hu. Retrieved 24 January 2018.
- "Hungary as one of the European hubs for automated and connected driving" (PDF). ZalaZone. Retrieved 23 January 2018.
- Maierbrugger, Arno (1 August 2016). "Singapore to launch self-driving taxis next year | Investvine". Retrieved 9 August 2016.
- Ramirez, Elaine (7 February 2017). "How South Korea Plans To Put Driverless Cars On The Road By 2020". Forbes. Retrieved 23 November 2019.
- Slone, Sean. "State Laws on Autonomous Vehicles". Retrieved 11 December 2016.
- "Ten ways autonomous driving could redefine the automotive world". Retrieved 11 December 2016.
- "Marketplace of change: Automobile insurance in the era of autonomous vehicles". Archived from the original on 13 April 2018. Retrieved 1 January 2019.
- "Frequency of Target Crashes for IntelliDrive Safety Systems" (PDF).
- "No lights, no signs, no accidents – future intersections for driverless cars (video)". Reuters.com. 22 March 2012. Retrieved 28 April 2012.
- Andert, Edward; Khayatian, Mohammad; Shrivastava, Aviral (18 June 2017). "Crossroads". Crossroads: Time-Sensitive Autonomous Intersection Management Technique. Institute of Electrical and Electronics Engineers Inc. pp. 1–6. doi:10.1145/3061639.3062221. ISBN 9781450349277.
- Khayatian, Mohammad; Mehrabian, Mohammadreza; Shrivastava, Aviral (2018). "RIM". RIM: Robust Intersection Management for Connected Autonomous Vehicles. Institute of Electrical and Electronics Engineers Inc. pp. 35–44.
- "Mobility 2020". Nordic Communications Corporation. 8 January 2016.
- "Consumers in US and UK Frustrated with Intelligent Devices That Frequently Crash or Freeze, New Accenture Survey Finds". Accenture. 10 October 2011. Retrieved 30 June 2013.
- Yvkoff, Liane (27 April 2012). "Many car buyers show interest in autonomous car tech". CNET. Retrieved 30 June 2013.
- "Große Akzeptanz für selbstfahrende Autos in Deutschland". motorvision.de. 9 October 2012. Archived from the original on 15 May 2016. Retrieved 6 September 2013.
- "Autonomous Cars Found Trustworthy in Global Study". autosphere.ca. 22 May 2013. Retrieved 6 September 2013.
- "Autonomous cars: Bring 'em on, drivers say in Insurance.com survey". Insurance.com. 28 July 2014. Retrieved 29 July 2014.
- "Autonomous Vehicle Predictions: Auto Experts Offer Insights on the Future of Self-Driving Cars". PartCatalog.com. 16 March 2015. Retrieved 18 March 2015.
- Kyriakidis, M.; Happee, R.; De Winter, J. C. F. (2015). "Public opinion on automated driving: Results of an international questionnaire among 5,000 respondents". Transportation Research Part F: Traffic Psychology and Behaviour. 32: 127–140. doi:10.1016/j.trf.2015.04.014.
- Hohenberger, C.; Spörrle, M.; Welpe, I. M. (2016). "How and why do men and women differ in their willingness to use automated cars? The influence of emotions across different age groups". Transportation Research Part A: Policy and Practice. 94: 374–385. doi:10.1016/j.tra.2016.09.022.
- Hall-Geisler, Kristen (22 December 2016). "Autonomous cars seen as smarter than human drivers". TechCrunch. Retrieved 26 December 2016.
- Smith, Aaaron; Anderson, Monica (4 October 2017). "Automation in Everyday Life".
- Hewitt, Charlie; Politis, Ioannis; Amanatidis, Theocharis; Sarkar, Advait (2019). "Assessing public perception of self-driving cars: the autonomous vehicle acceptance model". Proceedings of the 24th International Conference on Intelligent User Interfaces. ACM Press: 518–527. doi:10.1145/3301275.3302268.
- "Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations". Transportation Research Part A: Policy and Practice. 77.
- "Responsibility for Crashes of Autonomous Vehicles: An Ethical Analysis". Sci Eng Ethics. 21.
- "The Coming Collision Between Autonomous Vehicles and the Liability System". Santa Clara Law Review. 52.
- "The Trolley Problem". The Yale Law Journal. 94 (6).
- Himmelreich, Johannes (17 May 2018). "Never Mind the Trolley: The Ethics of Autonomous Vehicles in Mundane Situations". Ethical Theory and Moral Practice. 21 (3): 669–684. doi:10.1007/s10677-018-9896-4. ISSN 1386-2820.
- Meyer, G.; Beiker, S (2014). Road vehicle automation. Springer International Publishing. pp. 93–102.
- Himmelreich, Johannes (2018). "Never Mind the Trolley: The Ethics of Autonomous Vehicles in Mundane Situations". Ethical Theory and Moral Practice. 3: 669.
- Lafrance, Adrienne (21 March 2016). "How Self-Driving Cars Will Threaten Privacy". Retrieved 4 November 2016.
- Jack, Boeglin (1 January 2015). "The Costs of Self-Driving Cars: Reconciling Freedom and Privacy with Tort Liability in Autonomous Vehicle Regulation". Yale Journal of Law and Technology. 17 (1).
- Greenhouse, Steven. "Autonomous vehicles could cost America 5 million jobs. What should we do about it?". latimes.com. Retrieved 7 December 2016.
- Bertoncello, M.; Wee, D. "Ten ways autonomous driving could redefine the automotive world". McKinsey & Company. Retrieved 7 December 2016.
- "Employment by detailed occupation". bls.gov. United States Department of Labor. Retrieved 7 December 2016.
- Fagnant, D. J.; Kockelman, K. (2015). "Preparing a nation for autonomous vehicles: Opportunities, barriers, and policy recommendations". Transportation Research Part A: Policy and Practice. 77: 167–181. doi:10.1016/j.tra.2015.04.003.
- Edmond Awad, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Jenrich, Azim Shariff, & Jean-François Bonnefon, & Iyan Rahwan (2018). "The Moral Machine Experiment". Nature. 563 (7729): 59–64. Bibcode:2018Natur.563...59A. doi:10.1038/s41586-018-0637-6. hdl:10871/39187. PMID 30356211.CS1 maint: multiple names: authors list (link)
- Hornigold, Thomas. "Building a Moral Machine: Who Decides the Ethics of Self Driving Cars?". Singularity Hub.
- Jean-François Bonnefon, Azim Shariff, & Iyad Rahwan (2016). "The Social Dilemma of Autonomous Vehicles". Science. 352 (6293): 1573–6. arXiv:1510.03346. Bibcode:2016Sci...352.1573B. doi:10.1126/science.aaf2654. PMID 27339987.CS1 maint: multiple names: authors list (link)
- Rawhwan, Iyad. "The Social Dilemma of Driverless Cars". Youtube. TedXCambridge.
- Lambert, Fred (21 December 2015). "Tesla CEO Elon Musk drops his prediction of full autonomous driving from 3 years to just 2". electrek.co. Retrieved 23 May 2018.
- Lambert, Fred (8 December 2017). "Elon Musk updates timeline for a self-driving car, but how does Tesla play into it?". electrek.co. Retrieved 23 May 2018.
- Britt, Ryan. "The 5 Best (and Worst) Autonomous Cars in All of Sci-Fi".
- "3D-Drucker: Warum die Industrie wieder einen Trend verschläft" (in German). t3n News. Retrieved 22 January 2017.
- "'Bull' episode 10 preview: The self-driving car case and Ginny Bretton". 3 January 2017.
|Wikimedia Commons has media related to Unmanned automobiles.|
- O'Toole, Randal (18 January 2010). Gridlock: Why We're Stuck in Traffic and What To Do About It. Cato Institute. ISBN 978-1-935308-24-9.
- Macdonald, Iain David Graham (2011). A Simulated Autonomous Car (PDF) (thesis). The University of Edinburgh. Retrieved 17 April 2013.
- Knight, Will (22 October 2013). "The Future of Self-driving Cars". MIT Technology Review. Retrieved 22 July 2016.
- Taiebat, Morteza; Brown, Austin; Safford, Hannah; Qu, Shen; Xu, Ming (2018). "A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles". Environmental Science & Technology. 52 (20): 11449–11465. arXiv:1901.10581. doi:10.1021/acs.est.8b00127. PMID 30192527.
- Glancy, Dorothy (2016). A Look at the Legal Environment for Driverless Vehicles (PDF) (Report). National Cooperative Highway Research Program Legal Research Digest. 69. Washington, DC: Transportation Research Board. ISBN 978-0-309-37501-6. Retrieved 22 July 2016.
- Newbold, Richard (17 June 2015). "The driving forces behind what would be the next revolution in the haulage sector". The Loadstar. Retrieved 22 July 2016.
- Bergen, Mark (27 October 2015). "Meet the Companies Building Self-Driving Cars for Google and Tesla (And Maybe Apple)". re/code.
- John A. Volpe National Transportation Systems Center (March 2016). "Review of Federal Motor Vehicle Safety Standards (FMVSS) for Automated Vehicles: Identifying potential barriers and challenges for the certification of automated vehicles using existing FMVSS" (PDF). National Transportation Library. US Department of Transportation.
- Slone, Sean (August 2016). "State Laws on Autonomous Vehicles" (PDF). Capitol Research – Transportation Policy. Council of State Governments. Retrieved 28 September 2016.
- Steve Henn (31 July 2015). "Remembering When Driverless Elevators Drew Skepticism".
- James M. Anderson; et al. (2016). "Autonomous Vehicle Technology: A Guide for Policymakers" (PDF). RAND Corporation.
- Gereon Meyer, Sven Beiker (Eds.), Road Vehicle Automation, Springer International Publishing 2014, ISBN 978-3-319-05990-7, and following issues: Road Vehicle Automation 2 (2015), Road Vehicle Automation 3 (2016), Road Vehicle Automation 4 (2017), Road Vehicle Automation 5 (2018), Road Vehicle Automation 6 (2019). These books are based on presentations and discussions at the Automated Vehicles Symposium organized annually by TRB and AUVSI.
- Roger Kemp (2018). "Autonomous vehicles – who will be liable for accidents?". [15 Digital Evidence and Electronic Signature Law Review (2018) 33 – 47].