Engineering:Self-driving car

Short description: Vehicle operated with reduced human input

A self-driving car, also known as an autonomous car (AC), driverless car, or robotic car (robo-car), is a car that is capable of traveling without human input. Self-driving cars are responsible for perceiving the environment, monitoring important systems, and control, including navigation. Perception accepts visual and audio data from outside and inside the car and interpret the input to abstractly render the vehicle and its surroundings. The control system then takes actions to move the vehicle, considering the route, road conditions, traffic controls, and obstacles.

They have the potential to impact the Automotive industry , health, welfare, urban planning, traffic, insurance, labor market, and other domains. Appropriate regulations are necessary for deployment.

Autonomous ground vehicle capabilities can be categorized in six levels defined by SAE International (SAE J3016).

(As of August 2023), no system had reached the highest level, although multiple vendors are pursuing autonomy. Waymo was the first to offer robo taxi rides to the general public, and offers services in various US cities, followed by Cruise, in San Francisco . Honda was the first manufacturer to sell a Level 3 car, followed by Mercedes-Benz, BMW Group and Kia. Nuro offers autonomous commercial delivery operations in California. DeepRoute.ai launched a robotaxi service in Shenzhen. Palo Alto, California certified Nuro at Level 4.

Waymo undergoing testing in the San Francisco Bay Area

Roborace autonomous racing car on display at the 2017 New York City ePrix

History

Experiments have been conducted on automated driver assistance systems (ADAS) since at least the 1920s; trials began in the 1950s. The first semi-autonomous car was developed in 1977, by Japan's Tsukuba Mechanical Engineering Laboratory. It required specially marked streets that were interpreted by two cameras on the vehicle and an analog computer. The vehicle reached speeds of 30 km/h (19 mph) with the support of an elevated rail.

Carnegie Mellon University's Navlab and ALV semi-autonomous projects appeared in the 1980s, 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, ALV had reached 31 km/h (19 mph), on two-lane roads. Obstacle avoidance came in 1986, and day and night off-road by 1987. In 1995 Navlab 5 completed the first autonomous US coast-to-coast. Traveling from Pittsburgh, Pennsylvania and San Diego, California, 98.2% were autonomous, completed with an average speed of 63.8 mph (102.7 km/h). Until 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, controls, and sensor systems.

The US allocated US$650 million in 1991 for research on the National Automated Highway System, which demonstrated automated driving through a combination of highway-embedded automation with vehicle technology, and cooperative networking between the vehicles and highway infrastructure. The programme concluded with a successful demonstration in 1997. Partly funded by the National Automated Highway System and DARPA, Navlab drove 4,584 km (2,848 mi) across the US in 1995, 4,501 km (2,797 mi) or 98% autonomously. In 2015, Delphi improved piloted a Delphi technology-based Audi, over 5,472 km (3,400 mi) through 15 states, 99% autonomously. In 2015, Nevada, Florida, California, Virginia, Michigan, and Washington DC allowed autonomous car testing on public roads.

From 2016 to 2018, the European Commission funded development for connected and automated driving through Coordination Actions CARTRE and SCOUT programs. The Strategic Transport Research and Innovation Agenda (STRIA) Roadmap for Connected and Automated Transport was published in 2019.

In November 2017, Waymo announced testing of autonomous cars without a safety driver. However, an employee was in the car. An October 2017 report by the Brookings Institution found that $80 billion had been reported as invested in autonomous technology.

In December 2018, Waymo was the first to commercialize a robotaxi service, in Phoenix, Arizona. In October 2020, Waymo launched a geo-fenced robotaxi service in Phoenix. The cars were monitored in real-time, and remote engineers sometimes needed to intervene.

In March 2019, ahead of Roborace, Robocar set the Guinness World Record as the world's fastest autonomous car. Robocar reached 282.42 km/h (175.49 mph).

In March 2021, Honda began leasing in Japan a limited edition of 100 Legend Hybrid EX sedans equipped with the newly approved Level 3 automated driving equipment which had been granted the safety certification by Japanese government to their autonomous "Traffic Jam Pilot" driving technology, and legally allow drivers to take their eyes off the road.

(As of August 2023), vehicles operating at Level 3 and above are an insignificant market factor. In December 2020, Waymo became the first service provider to offer driverless taxi rides to the general public, in a part of Phoenix, Arizona. In March 2021, Honda was the first manufacturer to sell a legally approved Level 3 car. Nuro began autonomous commercial delivery operations in California in 2021. DeepRoute.ai launched robotaxi service in Shenzhen in July 2021. Nuro was approved for Level 4 in Palo Alto in August, 2023. In December 2021, Mercedes-Benz received approval for a Level 3 car. In February 2022, Cruise became the second service provider to offer driverless taxi rides to the general public, in San Francisco . In December 2022, several manufacturers had scaled back plans for self-driving technology, including Ford and Volkswagen.

Definitions

Various organizations have proposed terminology.

In 2014, SAE J3016 stated 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)."

Vendors do not consistently apply terminology, nor do products implement features in strict accord with definitions. Names such as AutonoDrive, PilotAssist, Full-Self Driving or DrivePilot are used even though the products offer an assortment of features that do not match the name.

ADS vs ADAS

ADAS means advanced driver-assistance system considered as level 1 and level 2.

ADS means automated driving system considered as level 3 and upper.

Automated driver assistance system

Features such as keeping the car within its lane, speed controls, and emergency braking are termed driver assistance and known as ADAS, because while they handle some driving tasks, they require a human driver.

Organizations such as AAA provide standardized naming conventions for features such as automated lane keeping support (ALKS). The Association of British Insurers stated that the usage of the word autonomous in marketing to be dangerous because car ads make motorists think "autonomous" and "autopilot" imply that the driver can rely on the car to control itself, even though they rely on the driver to ensure safety.

Despite offering something called Full Self-Driving, Tesla stated that its offering is not completely autonomous. In the United Kingdom, a fully self-driving car is defined as a car registered in a specific list, rather than a set of features. Proposals to adopt aviation automation terminology for cars have not prevailed.

According to SMMT, "There are two clear states – a vehicle is either assisted with a driver being supported by technology or automated where the technology is effectively and safely replacing the driver."

Autonomous vs. automated

Many projects have automated (made automatic) some aspect of driving. Some required aids in the environment, such as magnetic strips in roadways. Autonomous control implies performance under environmental uncertainty, along with the ability to compensate for errors without external intervention.

One approach is to pool information across multiple vehicles. This can be done locally, to e.g., form a convoy or more widely, e.g., to traffic-optimize a route.

Euro NCAP defined autonomous as "the system acts independently of the driver to avoid or mitigate the accident", which implies the autonomous system is not the driver.

In Europe, the words automated and autonomous might be used together. For instance, Regulation (EU) 2019/2144 supplied:

• "automated vehicle" means a motor vehicle designed and constructed to move autonomously for certain periods of time without continuous driver supervision but in respect of which driver intervention is still expected or required;
• "fully automated vehicle" means a motor vehicle that has been designed and constructed to move autonomously without driver supervision;

In British English, the word automated alone might have several meanings, such as in the sentence: "Thatcham also found that the automated lane keeping systems could only meet two out of the twelve principles required to guarantee safety, going on to say they cannot, therefore, be classed as 'automated driving', instead it claims the tech should be classed as "assisted driving".": The first occurrence of the "automated" word refers to an Unece automated system, while the second refers to the British legal definition of an automated vehicle. British law interprets the meaning of "automated vehicle" based on the interpretation section related to a vehicle "driving itself" and an insured vehicle.

On 8 November was introduced in the British Parliament a bill to "Regulate the use of automated vehicles on roads and in other public places; and to make other provision in relation to vehicle automation". The word "automated" appears in this definition.

This introduced bill considers a vehicle travels “autonomously” if "it is being controlled not by an individual but by equipment of the vehicle, and neither the vehicle nor its surroundings are being monitored by an individual with a view to immediate intervention in the driving of the vehicle". The word "autonomously" appears in this definition.

Autonomous versus cooperative

To enable a car to travel without a driver within the vehicle, some companies use a remote driver. A remote driver is a driver that is not physically inside the vehicle they are operating. Rather, they use a connection (typically an internet connection) to the vehicle to control it, and all its functions remotely.

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.

Self-driving car

PC Magazine defined a self-driving car as "a computer-controlled car that drives itself". The Union of Concerned Scientists used "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."

The British Automated and Electric Vehicles Act 2018 law defines a vehicle as "driving itself" if the vehicle "is operating in a mode in which it is not being controlled, and does not need to be monitored, by an individual".

Another British definition adopts "Self-driving vehicles are vehicles that can safely and lawfully drive themselves."

British definitions

On 8 November was introduced in the British Parliament a bill to "Regulate the use of automated vehicles on roads and in other public places; and to make other provision in relation to vehicle automation".

self-driving capability

Chapter 1 part1 of this automated vehicle bill starts by defining self-driving capability. It states that "A vehicle “satisfies the self-driving test” if it is designed or adapted with the intention that a feature of the vehicle will allow it to travel autonomously, and it is capable of doing so, by means of that feature, safely and legally." Those words are also defined: travel autonomously, safely and legally:

travels “autonomously”

A vehicle travels “autonomously” if it is being controlled not by an individual but by equipment of the vehicle, and neither the vehicle nor its surroundings are being monitored by an individual with a view to immediate intervention in the driving of the vehicle.

control

References to “control” of a vehicle are to control of the motion of the vehicle.

“safely”

if it travels to an acceptably safe standard, and

“legally”

if it travels with an acceptably low risk of committing a traffic infraction.

Terminology and communication offenses

On 8 November was introduced in the British Parliament a bill to "Regulate the use of automated vehicles on roads and in other public places; and to make other provision in relation to vehicle automation".

This bill define offenses so that an offense under those sections can be committed anywhere in the world.

For instance, a restricted term offense for a road vehicle or for a product may occur when

• (a) the person uses, or causes or permits the use of, a restricted term in connection with the promotion or supply of a road vehicle,
• (b) the person is acting in the course of business,
• (c) the use of the restricted term is directed at an end-user or potential end-user of the vehicle,
• (d) it is reasonable to anticipate that the use of the term will come to the attention of an end-user or potential end-user of the vehicle in Great Britain, and
• (e) the vehicle is not an appropriate vehicle.

or when

• (a) the person uses, or causes or permits the use of, a restricted term in connection with the promotion or supply of a product intended for use as equipment of a road vehicle,
• (b) the person is acting in the course of business,
• (c) the use of the restricted term is directed at an end-user or potential end-user of a road vehicle,
• (d) it is reasonable to anticipate that the use of the term will come to the attention of an end-user or potential end-user of a road vehicle in Great Britain, and
• (e) the restricted term is not used specifically in relation to the use of the product as equipment of an appropriate vehicle

For the purpose of communications likely to confuse as to autonomous capability, a person commits an offense when:

• (a) the person makes, or causes or permits the making of, a communication in connection with the promotion or supply of any product or service,
• (b) the person is acting in the course of business,
• (c) the communication is directed at an end-user or potential end-user of a road vehicle,
• (d) it is reasonable to anticipate that the communication will come to the attention of an end-user or potential end-user of a road vehicle in Great Britain, and
• (e) the communication would be likely to confuse end-users of road vehicles in Great Britain as to whether a vehicle that is not an authorized automated vehicle is capable of traveling autonomously, safely and legally on roads or other public places in Great Britain

SAE classification

Tesla Autopilot is classified as an SAE Level 2 system.

A classification system with six levels – ranging from fully manual to fully automated systems – was published in 2014 by SAE International as J3016, Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems; the details are revised periodically. This classification is based on the role of the driver, 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 its original formal classification system. After SAE updated its classification in 2016, called J3016_201609, NHTSA adopted the SAE standard. The classification is a topic of debate, with various approaches proposed for its expansion.

SAE levels

"Driving mode" is used as "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 vehicle share control. 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 (ACC)). The driver must always be ready to retake control. Lane keeping (LK) Type II is a further example of Level 1 self-driving. Automatic emergency braking, which alerts the driver to a potential crash and applies the brakes is a Level 1 feature, according to Autopilot Review magazine.
• 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. The shorthand "hands off" is not literal – contact between hand and wheel is often mandatory during SAE 2 driving, to confirm that the driver is ready to intervene. The driver's eyes may be monitored to confirm that the driver is attentive. True hands off driving is sometimes unofficially termed level 2.5. A common example is ACC combined with LK, such as "Super-Cruise" in the Cadillac CT6 or the F-150's BlueCruise.
• Level 3 ("eyes off"): The driver can safely turn their attention from driving. The driver must still be prepared to intervene within a time interval specified by the manufacturer, when called upon by the vehicle. This level can be thought of as a co-driver that alerts the driver in an orderly fashion when handing off control. An example would be a Traffic Jam Chauffeur (a car satisfying the Automated Lane Keeping Systems (ALKS) regulations).
• Level 4 ("mind off"): No driver attention is required for safety, allowing the driver to sleep or change seats. However, self-driving is supported only in specific areas (geofenced) or under specific circumstances. Outside of these areas/circumstances, the vehicle must be able to safely abort the trip, e.g. stop and park. An example would be a robotaxi or delivery service that covers specific locations, possibly also time-of-day-limited. Automated Valet Parking (AVP) is another example.
• Level 5 ("steering wheel optional"): No human intervention is required under any circumstances, such as long-distance trucking.

SAE (J3016) Automation Levels

SAE Level	Name	Narrative		Direction and speed control	Monitoring driving environment	Fallback responsibility	Driving modes
Driver monitors the driving environment
0	No Automation	Full-time performance by the driver of all aspects of driving, even when "enhanced by warning or intervention systems"		Driver	Driver	Driver	n/a
1	Driver Assistance	Driving mode-specific control by an ADAS of either steering or speed	Uses information about the driving environment and with the expectation that the driver performs all remaining aspects of the dynamic driving task	Driver and system			Some driving modes
2	Partial Automation	Driving mode-specific execution by one or more driver assistance systems of both steering and speed		System
ADAS monitors the driving environment
3	Conditional Automation	Driving mode-specific control by an ADAS of all aspects of driving	Driver must respond appropriately to a request to intervene	System	System	Driver	Some driving modes
4	High Automation		If a driver does not respond appropriately to a request to intervene the car can stop safely			System	Many driving modes
5	Full Automation		Control the vehicle under all conditions that can be managed by a driver				All driving modes

Criticism of SAE

The SAE Automation Levels have been criticized for their technological focus. It has been argued that the structure of the levels suggests that automation increases linearly and that more automation is better, which may not always be the case. The SAE Levels also do not account for changes that may be required to infrastructure and road user behavior.

Technology

General perspectives

Several classifications have been proposed to deal with ADAS technology. One such proposal is to adopt these categories: navigation, path planning, perception, and car control.

Even video games have been used as a platform to test autonomous vehicles.

Navigation

Navigation involves the use of maps to define a path between origin and destination. Hybrid navigation is the use of multiple navigation systems.

Sensing

Sensor technologies including combinations of cameras, LiDAR, radar, audio, and ultrasound have been applied to the task of understanding the environment surrounding a vehicle, GPS, and Inertial measurement. Deep neural networks are shown to be effective in analysing the combined stream of inputs from these sensors to detect and identify objects. Some systems use Bayesian simultaneous localization and mapping (SLAM) algorithms. Waymo at one point used SLAM, and added detection and tracking of other moving objects (DATMO), to handle potential obstacles. Other systems use roadside real-time locating system (RTLS) technologies to aid localization. Tesla uses eight cameras to create a bird's-eye view of the surroundings and categorize the objects within it.

Maps

Maps are necessary for navigation. Map sophistication varies from simple graphs that show which roads connect to each other, with details such as one-way vs two-way, to those that are highly detailed, with information about lanes, traffic controls, roadworks, and more. Researchers at the MITComputer Science and Artificial Intelligence Laboratory (CSAIL) developed a system called MapLite, which allowed self-driving cars to drive 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 road conditions. One issue with highly-detailed maps is keeping them updated as the world changes. Vehicles that can operate with less-detailed maps to some extent require less-frequent updates.

Sensor fusion

Control systems typically combine data from multiple sensors. Self-driving cars often combine cameras, LiDAR, and radar. Multiple sensors provide a more complete view of the surroundings and can be used to cross-check each other to correct errors.

Path planning

Path planning finds a sequence of segments that a vehicle can follow from origin to destination. Two techniques used for path planning are graph-based search and variational-based optimization techniques. Graph-based techniques can make harder decisions such as how to pass another vehicle/obstacle. Variational-based optimization techniques require a higher level of planning in setting restrictions on the vehicle's driving corridor to prevent collisions. The large scale path of the vehicle can be determined by using a voronoi diagram, an occupancy grid mapping, or with a driving corridors algorithm. The latter allows the vehicle to locate and drive within open space that is bounded by lanes or barriers.

Drive by wire

Drive by wire is the use of electrical or electro-mechanical systems for performing vehicle functions traditionally achieved by mechanical linkages.

Driver monitoring

Driver monitoring is used to assess the driver's attention and alertness. Techniques in use include eye monitoring, and requiring the driver to maintain torque on the steering wheel. Understand the driver's status and identify dangerous driving behaviors by using the driver monitoring system (DMS). Sounds an alert when the risk of an accident increases and allows the vehicle to take a calming approach to keep the driver safe.

Vehicle communication

Vehicles can potentially benefit from communicating with others to share information about traffic, road obstacles, to receive map and software updates, etc.

ISO/TC 22 specifies in-vehicle transport information and control systems, while ISO/TC 204 specifies information, communication and control systems in surface transport. International standards have been developed for ADAS functions, connectivity, human interaction, in-vehicle systems, management/engineering, dynamic map and positioning, privacy and security.

Software update

Software controls the vehicle, and can provide entertainment and other services. Some vehicles can acquire updates to that software over the internet. In March 2021, UNECE regulation on software update and software update management systems was published.

Safety model

Mobileye's mathematical model, "Responsibility-Sensitive Safety (RSS)", is undergoing standardization as "IEEE P2846: A Formal Model for Safety Considerations in Automated Vehicle Decision Making".

In 2022, a research group of National Institute of Informatics (NII, Japan) expanded RSS and developed "Goal-Aware RSS" to make RSS rules possible to deal with complex scenarios via program logic.

Turquoise lights

Turquoise lights will be used in the 2026 Mercedes-Benz EQS and S-Class sedans with Drive Pilot, the SAE Level 3 driving system. The turquoise lights are authorized so that EQS and S-Class Sedans that feature Drive Pilot can inform other drivers that the vehicle is driving in autonomous mode. As of 2024, the Turquoise light system has been approved nationwide in the US.

In 2023, this Turquoise light has not been standardized by the P.R.C or the UN-ECE.

Challenges

Autonomous delivery vehicles stuck in one place by attempting to avoid one another

Obstacles

The primary obstacle to ACs is the advanced software and mapping required to make them work safely across the wide variety of conditions that drivers experience. In addition to handling day/night driving in good and bad weather on roads of arbitrary quality, ACs must cope with other vehicles, road obstacles, poor/missing traffic controls, flawed maps, and handle endless edge cases, such as following the instructions of a police officer managing traffic at a crash site.

Other obstacles include cost, liability, consumer reluctance, potential ethical dilemmas, security, privacy, and legal/regulatory framework. Further, AVs could automate the work of professional drivers, eliminating many jobs, which could slow acceptance.

Concerns

Deceptive marketing

Tesla calls its Level 2 ADAS "Full Self-Driving (FSD) Beta". US Senators Richard Blumenthal and Edward Markey called on the Federal Trade Commission (FTC) to investigate this marketing in 2021. In December 2021 in Japan, Mercedes-Benz was punished by the Consumer Affairs Agency for misleading product descriptions.

Mercedes-Benz was criticized for a misleading US commercial advertising E-Class models. At that time, Mercedes-Benz rejected the claims and stopped its "self-driving car" ad campaign that had been running. In August 2022, the California Department of Motor Vehicles (DMV) accused Tesla of deceptive marketing practices.

With the Automated Vehicles Bill (AVB) self-driving car-makers could face prison for misleading adverts in the United-Kingdom.

Security

In the 2020s, concerns over ACs vulnerability to cyberattacks and data theft emerged.

In 2018 and 2019 former Apple engineers were charged with stealing information related to Apple's self-driving car project. In 2021 the United States Department of Justice (DOJ) accused Chinese security officials of coordinating a hacking campaign to steal information from government entities, including research related to autonomous vehicles. China has prepared "the Provisions on Management of Automotive Data Security (Trial) to protect its own data".

Cellular Vehicle-to-Everything technologies are based on 5G wireless networks. (As of November 2022), the US Congress was considering the possibility that imported Chinese AC technology could facilitate espionage.

Testing of Chinese automated cars in the US has raised concern over which US data are collected by Chinese vehicles to be stored in Chinese country and concern with any link with the Chinese communist party.

Real-time prediction

While predicting the behavior of ACs that do not use traditional communications such as hand signals, is a major challenge for human drivers, the real-time prediction of the behavior of other vehicles, pedestrians etc, some of which may be stationary when first noted, is even greater challenge for self-driving cars. Modelling human and vehicle behaviours using machine learning, particularity deep neural networks, has enabled better understanding of human-driver interaction. Raster-based methods have been replaced by vector-based methods in order to overcome the former's lossy rendering, limited receptive field, and prohibitively high cost. The remaining problem is high level of uncertainty that emerges in trajectory predictions as the prediction timeframe is extended. Also, if data re-normalization and re-encoding are used to update future trajectories each time a self-driving car changes its position, its action is often delayed by 8 milliseconds, potentially causing an accident. Several powerful trajectory prediction models have recently adopted Transformers with factorized attention as their encoders, but their scalability is still limited by the computational complexity of factorized attention. Most recently proposed QCNet model uses a query-centric instead of agent-centric modeling, taking advantage of both anchor-based and anchor-free solutions, with an anchor-free module generating adaptive anchors in a data-driven manner and an anchor-based module refining these anchors based on the scene context. The model injects the relative spatialtemporal positions into the key and value (both Transformer elements) when performing attention-based scene-context fusion.

Handover

For ACs that have not achieved L5, the ADAS has to be able to safely accept control from and return it to the driver.

Risk compensation

The second challenge is known as risk compensation: as a system is perceived to be safer, on average people engage in riskier behavior. (People who wear seat belts drive faster). ACs suffer from this problem: for example Tesla Autopilot users in some cases stop monitoring the vehicle while it is in control.

Trust

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. Automatic elevators were invented in 1900, but did not become common until operator strikes and trust was built with advertising and features such as an emergency stop button.

Ethical issues

Rationale for liability

Standards for liability have yet to be adopted to address crashes and other incidents. Does liability rest with the manufacturer or the driver/passenger and does it vary with, e.g., automation level or merely the specific circumstances?

Trolley Problem

The trolley problem is a thought experiment in ethics. Adapted for ACs, consider an AC carrying a passenger when suddenly a pedestrian steps in its way and the car has to choose between killing the pedestrian or swerving into a wall, killing the passenger. Ethical researchers have suggested deontology (formal rules) and utilitarianism (harm reduction) as applicable.

Public opinion has been reported to support harm reduction, except that they want the vehicle to prefer them when they are riding in it. However, utilitarian regulations are unpopular.

Privacy

Privacy-related issues arise mainly from the fact that ACs are connected to the internet. Any connected device offers the potential to be penetrated. This information includes destinations, routes, cabin recordings, media preferences, behavioral patterns, and others.

Road infrastructure

Whether existing road infrastructure can support higher levels of automation has not been finalized. The answer may vary across jurisdictions. In March 2023, the Japanese government unveiled a plan to set up a dedicated highway lane for ACs. In April 2023, JR East announced their challenge to raise their self-driving level of Kesennuma Line bus rapid transit (BRT) in rural area from the current Level 2 to Level 4 at 60 km/h.

Testing

Approaches

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 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.

Disengagements in the 2010s

A prototype of Waymo's self-driving car, navigating public streets in Mountain View, California in 2017

In California, self-driving car manufacturers are required to submit annual reports to share how often their vehicles disengaged from autonomous mode during tests. It has been believed that we would learn how reliable the vehicles are becoming based on how often they needed "disengagements".

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.

Distance between disengagement and total distance traveled autonomously in the 2010s

Car maker	California, 2016		California, 2018		California, 2019
	Distance between disengagements	Total distance traveled	Distance between disengagements	Total distance traveled	Distance between disengagements	Total distance traveled
Waymo	5,128 mi (8,253 km)	635,868 mi (1,023,330 km)	11,154 mi (17,951 km)	1,271,587 mi (2,046,421 km)	11,017 mi (17,730 km)	1,450,000 mi (2,330,000 km)
BMW	638 mi (1,027 km)	638 mi (1,027 km)
Nissan	263 mi (423 km)	6,056 mi (9,746 km)	210 mi (340 km)	5,473 mi (8,808 km)
Ford	197 mi (317 km)	590 mi (950 km)
General Motors	55 mi (89 km)	8,156 mi (13,126 km)	5,205 mi (8,377 km)	447,621 mi (720,376 km)	12,221 mi (19,668 km)	831,040 mi (1,337,430 km)
Aptiv	15 mi (24 km)	2,658 mi (4,278 km)
Tesla	3 mi (4.8 km)	550 mi (890 km)
Mercedes-Benz	2 mi (3.2 km)	673 mi (1,083 km)	1.5 mi (2.4 km)	1,749 mi (2,815 km)
Bosch	7 mi (11 km)	983 mi (1,582 km)
Zoox			1,923 mi (3,095 km)	30,764 mi (49,510 km)	1,595 mi (2,567 km)	67,015 mi (107,850 km)
Nuro			1,028 mi (1,654 km)	24,680 mi (39,720 km)	2,022 mi (3,254 km)	68,762 mi (110,662 km)
Pony.ai			1,022 mi (1,645 km)	16,356 mi (26,322 km)	6,476 mi (10,422 km)	174,845 mi (281,386 km)
Baidu (Apolong)			206 mi (332 km)	18,093 mi (29,118 km)	18,050 mi (29,050 km)	108,300 mi (174,300 km)
Aurora			100 mi (160 km)	32,858 mi (52,880 km)	280 mi (450 km)	39,729 mi (63,938 km)
Apple			1.1 mi (1.8 km)	79,745 mi (128,337 km)	118 mi (190 km)	7,544 mi (12,141 km)
Uber			0.4 mi (0.64 km)	26,899 mi (43,290 km)		0 mi (0 km)

In the 2020s

Disengagements
(As of 2022), "disengagements" are at the center of the controversy. The problem is that reporting companies have varying definitions of what qualifies as a disengagement, and that definition can change over time. Executives of self-driving car companies have criticized disengagements as a deceptive metric, because it does not take into account the higher degree of difficulty navigating urban streets compared with interstates highway.

Compliance
In April 2021, WP.29 GRVA issued the master document on "Test Method for Automated Driving (NATM)".

In October 2021, the Europe's comprehensive pilot test of automated driving on public roads, L3Pilot, demonstrated automated systems for cars in Hamburg, Germany, in conjunction with ITS World Congress 2021. SAE Level 3 and 4 functions were tested on ordinary roads. At the end of February 2022, the final results of the L3Pilot project were published.

In November 2022, an International Standard ISO 34502 on "Scenario based safety evaluation framework" was published.

Collision avoidance
In April 2022, collision avoidance testing was demonstrated by Nissan. Also, Waymo published a document about collision avoidance testing in December 2022.

Simulation and validation
In September 2022, Biprogy released a software system of "Driving Intelligence Validation Platform (DIVP)" as the achievement of Japanese national project "SIP-adus" led by Cabinet Office with the same name of its subproject which is interoperable with Open Simulation Interface (OSI) of ASAM.

Topics
In November 2021, the California Department of Motor Vehicles (DMV) notified Pony.ai that it was suspending its driverless testing permit following a reported collision in Fremont on 28 October. This incident stands out because the vehicle was in autonomous mode and didn't involve any other vehicle. In May 2022, DMV revoked Pony.ai's permit for failing to monitor the driving records of the safety drivers on its testing permit.

In April 2022, it is reported that Cruise's testing vehicle blocked fire engine on emergency call, and sparked questions about an autonomous vehicle's ability to handle unexpected roadway issues.

In November 2022, Toyota gave a demonstration of one of its GR Yaris test car equipped with AI, which had been trained on the skills and knowledge of professional rally drivers to enhance the safety of self-driving cars. Toyota has been using the learnings from the collaborative activities with Microsoft in FIA World Rally Championship since 2017 season.

Pedestrian reaction
In 2023 David R. Large, senior research fellow with the Human Factors Research Group at the University of Nottingham, disguised himself as a car seat in a study to test people's reactions to driverless cars. He said, "We wanted to explore how pedestrians would interact with a driverless car and developed this unique methodology to explore their reactions." The study found that, in the absence of someone in the driving seat, pedestrians trust certain visual prompts more than others when deciding whether to cross the road.

Incidents

Tesla Autopilot

(As of November 2021), Tesla's advanced driver-assistance system (ADAS) Autopilot is classified as a Level 2.

On 20 January 2016, the first of five known fatal crashes 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.

In 2021, NTSB Chair called on Tesla to change the design of its Autopilot to ensure it cannot be misused by drivers, according to a letter sent to the company's CEO.

Waymo

Google's in-house automated car

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), 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.

Uber's Advanced Technologies Group (ATG)

In March 2018, Elaine Herzberg died after being hit by a self-driving car being tested by Uber's Advanced Technologies Group (ATG) in the US state of Arizona. There was a safety driver in the car. Herzberg was crossing the road about 400 feet from an intersection. This marks the first time an individual is known to have been killed by an autonomous vehicle, and the incident raised questions about regulation of the self-driving car industry. Some experts said a human driver could have avoided the fatal crash. Arizona governor Doug Ducey 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 then stopped self-driving tests in California until it was issued a new permit in 2020.

In May 2018, the US National Transportation Safety Board (NTSB) issued a preliminary report. The final report 18 months later determined that the immediate cause of the accident was the safety driver's failure to monitor the road because she was distracted by her phone. However, Uber ATG's "inadequate safety culture" contributed to the crash. The report noted from the post-mortem that the victim had "a very high level" of methamphetamine in her body. The board also called on federal regulators to carry out a review before allowing automated test vehicles to operate on public roads.

In September 2020, the backup driver, Rafaela Vasquez, was charged with negligent homicide, because she did not look at the road for several seconds while her phone was streaming The Voice broadcast by Hulu. She pleaded not guilty and was released to await trial. Uber does not face any criminal charge because in the USA there is no basis for criminal liability for the corporation. The safety driver is assumed to be responsible for the accident, because she was in the driving seat in a capacity to avoid an accident (like in a Level 3). The trial was originally planned for February 2021 but is now scheduled to begin in June 2023.

Navya Arma driving system

On 9 November 2017, a Navya Arma 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."

NIO Navigate on Pilot

On 12 August 2021, a 31-year-old Chinese man was killed after his NIO ES8 collided with a construction vehicle. NIO's self-driving feature is still in beta and cannot yet deal with static obstacles. Though the vehicle's manual clearly states that the driver must take over when nearing construction sites, the issue is whether the feature was improperly marketed and unsafe. Lawyers of the deceased's family have also called into question NIO's private access to the vehicle, which they argue may lead to the data ending up forged.

Toyota e-Palette operation

On 26 August 2021, a Toyota e-Palette, a mobility vehicle used to support mobility within the Athletes' Village at the Olympic and Paralympic Games Tokyo 2020, collided with a visually impaired pedestrian about to cross a pedestrian crossing. The Toyota bus service was suspended after the accident, and resumed on 31 August 2021 with improved safety measures.

Public opinion surveys

In the 2010s

In a 2011 online survey of 2,006 US and UK consumers by Accenture, 49% said they would be comfortable using a "driverless car".

A 2012 survey of 17,400 vehicle owners by J.D. Power and Associates found 37% initially said they would be interested in purchasing a "fully autonomous car". However, that figure dropped to 20% if told the technology would cost 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% predicted that either Tesla or Daimler would be the first to the market with a fully autonomous vehicle, while (at 38%) Daimler was 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.

In 2017, Pew Research Center surveyed 4,135 US adults from 1–15 May and found 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.

In 2019, results from two opinion surveys of 54 and 187 US adults respectively were published. A new standardized 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.

In the 2020s

In 2022, research by safety charity Lloyd's Register Foundation uncovered that only a quarter (27%) of the world's population would feel safe in self-driving cars.

Regulation

Regulation of self-driving cars is an increasingly important issue which includes multiple subtopics. Among them are self-driving car liability, regulations regarding approval and international conventions.

In the 2010s, researchers openly worried about the potential of future regulation to delay deployment of automated cars on the road. In 2020, international regulation in the form of UNECE WP.29 GRVA was established, regulating Level 3 automated driving. (As of 2022), it is considered very challenging to be approved as Level 3.

Commercialization

Between manually driven vehicles (SAE Level 0) and fully autonomous vehicles (SAE Level 5), there are a variety of vehicle types that 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.

(As of 2023) nearly all commercially available vehicles with autonomous features are considered SAE Level 2. Development is ongoing at many car companies on further automation features that function at Level 2 and Level 3. Other companies offer services of autonomous Level 4 robotaxis in a few cities in the United States.

Achieving full self-driving remains a true challenge.

Level 2 commercialization

SAE Level 2 features are available as part of the advanced driver-assistance system (ADAS) abilities in many commercially available vehicles. These systems often require a subscription to an ongoing service or paid upgrade with the car purchase.

Ford started offering the "BlueCruise" service on certain electric and gas-powered vehicles in 2022; it is named "ActiveGlide" in Lincoln vehicles. The system provides features such as lane centering, street sign recognition and hands-free highway driving on more than 130,000 miles of divided highways in the US. The version 1.2 update of the service was released in September 2022, and added features like hands-free lane changing, in-lane repositioning, and predictive speed assist. In April 2023 BlueCruise technology was approved in the UK, for use on certain motorways. The technology will at first only be available for 2023 models of Ford's electric Mustang Mach-E SUV.

Tesla vehicles are equipped with hardware that Tesla claims will allow full self-driving in the future. The Tesla Autopilot suite of ADAS features are included in all Tesla vehicle models. More advanced driving features are available at an extra cost, under the "Enhanced Autopilot" and "Full Self-Driving" names. The marketing names have been criticized as misleading, as all Tesla ADAS features provide only Level 2 capabilities.

Level 2 development

General Motors is developing the "Ultra Cruise" ADAS system, that will be a dramatic improvement over their current "Super Cruise" system. Ultra Cruise will cover "95 percent" of driving scenarios on 2 million miles of roads in the US, according to the company. The system hardware in and around the car includes multiple cameras, short- and long-range radar, and a LiDAR sensor, and will be powered by the Qualcomm Snapdragon Ride Platform. The luxury Cadillac Celestiq electric vehicle will be one of the first vehicles to feature Ultra Cruise.

Level 3 commercialization

Level 3 development

As of 2023, three car manufacturers have registered Level 3 conditionally automated cars: Honda in Japan, Mercedes in Germany, Nevada and California and BMW in Germany. Mass production is still not available.

Honda announced in December 2022, that it will enhance its Level 3 technology to function at any speed below legal limits on highways by 2029. Only 80 vehicles with Level 3 Honda Sending elite have been sold in Japan. Mass production start date for vehicles with the Level 3 driving system is not known as of 2022.

Mercedes-Benz received in early 2023 authorization for its Level 3 Drive Pilot in Nevada, and plans to apply for approval in California by mid-2023. Drive Pilot is planned to be available in the US market as an option for some models in the second half of 2023. California also registered Drive Pilot conditionally automated driving technology in 2023. Drive Pilot subscription service is introduces at a rate of $2500 per year in California and Nevada

BMW had been trying to make 7 Series as an automated car in 2017, in public urban motorways of the United States, Germany and Israel before commercializing them in 2021. Although it was not realized, BMW is still preparing 7 Series to become the next manufacturer to reach Level 3 in the second half of 2022. In 2023 BMW states that its level-3 technology will be available in Germany in the spring 2024. It will be the second manufacturer to deliver both level-2 and level-3 technology, but the only one with a level 3 technology which works in the dark.

In 2023, in China, three companies IM Motors, Mercedes and BMW have obtained authorization to test vehicles to be produced with level 3 conditional automated driving systems on Chinese motorways.

Other manufacturers have not yet registered Level 3 conditionally automated cars, while some of them have plans to do so.

In September 2021, Stellantis has presented its findings from a pilot programme testing Level 3 autonomous vehicles on public Italian highways. Stellantis's Highway Chauffeur claims Level 3 capabilities, which was tested on the Maserati Ghibli and Fiat 500X prototypes. Stellantis is going to roll out Level 3 capability within its cars in 2024.

Polestar, a Volvo Cars' brand, indicated in January 2022 its plan to offer Level 3 autonomous driving system in the Polestar 3 SUV, Volvo XC90 successor, with technologies from Luminar Technologies, Nvidia, and Zenseact. Polestar plan to make Level 3 Chauffeur available in Polestar 4, and Polestar 4 sold in China in 2023 and in 2024 in the rest of the world.

In January 2022, Bosch and the Volkswagen Group subsidiary CARIAD released a collaboration for autonomous driving up to level 3. This Joint development targets to be explored and evalauted for Level 4.

Hyundai Motor Company is (As of February 2022), in the stage of enhancing cybersecurity of connected cars to put Level 3 self-driving Genesis G90 on Korean roads. In 2023, Kia and Hyundai Korean car makers delayed their Level 3 plans, and will not deliver Level 3 vehicles in 2023.

Level 4 commercialization

Cruise and Waymo offer limited robotaxi services in a handful of American cities, as fully autonomous vehicles without any human safety drivers in the vehicles.

On 1 April 2023 in Japan, Level 4 legal scheme of the amended "Road Traffic Act" was nation-wide enforced, and one service level-upped to the Level 4 service. The approved self-driving shuttle is "ZEN drive Pilot Level 4" custom-made by AIST.

Level 4 development

In July 2020, Toyota started testing with public demonstration rides on Lexus LS (fifth generation) based TRI-P4 with Level 4 capability. In August 2021, Toyota operated potentially Level 4 service using e-Palette around the Tokyo 2020 Olympic Village.

In September 2020, Mercedes-Benz introduced world's first commercial Level 4 Automated Valet Parking (AVP) system named Intelligent Park Pilot for its new S-Class. The system can be pre-installed but is conditional on future national legal approval.

In September 2021, Honda started testing programme toward launch of Level 4 mobility service business in Japan under collaboration with Cruise and General Motors, using Cruise AV. In October 2021 at World Congress on Intelligent Transport Systems, Honda presented that they are already testing Level 4 technology on modified Legend Hybrid EX. At the end of the month, Honda explained that they are conducting verification project on Level 4 technology on a test course in Tochigi prefecture. Honda plans to test on public roads in early 2022.

In February 2022, General Motors and Cruise have petitioned NHTSA for permission to build and deploy a self-driving vehicle, the Cruise Origin, which is without human controls like steering wheels or brake pedals. The car was developed with GM and Cruise investor Honda, and its production is expected to begin in late 2022 in Detroit at GM's Factory Zero. (As of April 2022), the petition is pending.

In April 2022, Honda unveiled its Level 4 mobility service partners to roll out in central Tokyo in the mid-2020s using the Cruise Origin. By September 2022, Japan version prototype of Cruise Origin for Tokyo was completed and started testing.

In January 2023, Holon, the new brand from the Benteler Group, unvield its self-driving shuttle autonomous during the Consumer Electronics Show (CES) 2023 in Las Vegas. The company claims the vehicle is the world's first Level 4 shuttle built to automotive standard. Production of the Holon mover is scheduled to start in the US at the end of 2025.

Robotaxi

This section is an excerpt from Robotaxi[ edit ]

See also

Self-driving vehicles

• History of self-driving cars
• Self-driving car liability
• Self-driving truck
• Dutch Automated Vehicle Initiative
• Driverless tractor
• List of self-driving system suppliers
• Mobility as a service
• Platoon (automobile)
• Unmanned ground vehicle
• Unmanned aerial vehicle

Connected vehicles

• Connected car
• Intelligent transportation system
• Remote-control vehicle

Other vehicle technologies

• Automotive navigation system
• Advanced driver-assistance system
• Computer vision
• Death by GPS
• Hybrid navigation
• Machine vision
• Personal rapid transit
• Retrofitting
• Smart camera
• Technological unemployment
• Vehicle infrastructure integration
• Vehicle safety technology
• Vision processing unit
• Lane centering
• Measurement of Assured Clear Distance Ahead
• Electronic stability control
• Collision avoidance system

Further reading

• 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. https://books.google.com/books?id=DW2QAAAAQBAJ. 
• Macdonald, Iain David Graham (2011). A Simulated Autonomous Car (thesis). The University of Edinburgh. http://www.inf.ed.ac.uk/publications/thesis/online/IM110982.pdf. Retrieved 17 April 2013. 
• Knight, Will (22 October 2013). "The Future of Self-driving Cars". https://www.technologyreview.com/s/520431/driverless-cars-are-further-away-than-you-think/. 
• Taiebat, Morteza; Brown, Austin; Safford, Hannah; Qu, Shen; Xu, Ming (2019). "A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles.". Environmental Science & Technology 52 (20): 11449–11465. doi:10.1021/acs.est.8b00127. PMID 30192527. Bibcode: 2019arXiv190110581T. 
• Glancy, Dorothy (2016). A Look at the Legal Environment for Driverless Vehicles (Report). National Cooperative Highway Research Program Legal Research Digest. 69. Washington, DC: Transportation Research Board. ISBN 978-0-309-37501-6. http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_lrd_069.pdf. Retrieved 22 July 2016. 
• Newbold, Richard (17 June 2015). "The driving forces behind what would be the next revolution in the haulage sector". http://theloadstar.co.uk/the-driving-forces-behind-what-would-be-the-next-revolution-in-the-haulage-sector/. 
• Bergen, Mark (27 October 2015). "Meet the Companies Building Self-Driving Cars for Google and Tesla (And Maybe Apple)". http://recode.net/2015/10/27/meet-the-companies-building-self-driving-cars-for-google-and-tesla-and-maybe-apple/. 
• 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". US Department of Transportation. http://ntl.bts.gov/lib/57000/57000/57076/Review_FMVSS_AV_Scan.pdf. 
• Slone, Sean (August 2016). "State Laws on Autonomous Vehicles". Council of State Governments. http://knowledgecenter.csg.org/kc/system/files/CR_automomous.pdf. 
• Henn, Steve (31 July 2015). "Remembering When Driverless Elevators Drew Skepticism". https://www.npr.org/2015/07/31/427990392/remembering-when-driverless-elevators-drew-skepticism. 
• Anderson, James M. (2016). "Autonomous Vehicle Technology: A Guide for Policymakers". RAND Corporation. http://www.rand.org/content/dam/rand/pubs/research_reports/RR400/RR443-2/RAND_RR443-2.pdf. 
• 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.
• Kemp, Roger (2018). "Autonomous vehicles – who will be liable for accidents?". [15 Digital Evidence and Electronic Signature Law Review (2018) 33 – 47]. https://journals.sas.ac.uk/deeslr/issue/view/528. 

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