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Debate

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/DISCLAIMER\ This is my opinion about the test, and it doesn't reflect my thoughts about the self-driving car in itself.

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WOULD YOU LIKE DRIVERLESS VEHICLES TO BE TESTED IN YOUR CITY ?

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Please take a look at the attached slideshow

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A self-driving car, also known as an autonomous car, driver-less car, or robotic car ( robo-car), is a car incorporating vehicular automation, that is, a ground 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 thermographic cameras, 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.

Autonomy in vehicles is often categorized in six levels, according to a system developed by SAE International (SAE J3016, revised periodically). The SAE levels can be roughly understood as Level 0 - no automation; Level 1 - hands on/shared control; Level 2 - hands off; Level 3 - eyes off; Level 4 - mind off, and Level 5 - steering wheel optional.

As of March 2022, vehicles operating at Level 3 and above remain a marginal portion of the market. In December 2020, Waymo became the first service provider to offer driver-less taxi rides to the general public, in a part of Phoenix, Arizona. In March 2021, Honda became the first manufacturer to provide a legally approved Level 3 car, and Toyota operated a potentially Level 4 service around the Tokyo 2020 Olympic Village. Nuro has been allowed to start autonomous commercial delivery operations in California in 2021. In December 2021, Mercedes-Benz became the second manufacturer to receive legal approval for a Level 3 car complying with legal requirements. In February 2022, Cruise became the second service provider to offer driver-less taxi rides to the general public, in San Francisco in the US.

In China, two publicly accessible trials of robotaxis have been launched, in 2020 in Shenzhen's Pingshan District by Chinese firm AutoX and in 2021 at Shougang Park in Beijing by Baidu, a venue for the 2022 Winter Olympics.

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 night time 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 programme 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.

In November 2017, Waymo announced that it had begun testing driver-less cars without a safety driver in the driver position; however, there was still an employee in the car. An October 2017 report by the Brookings Institution found that the $80 billion had been reported as invested in all facets of self driving technology up to that point, but that it was "reasonable to presume that total global investment in autonomous vehicle technology is significantly more than this."

In October 2018, Waymo announced that its test vehicles had travelled 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, in Phoenix, Arizona. In October 2020, Waymo launched a geo-fenced driver-less ride hailing service in Phoenix. The cars are being monitored in real-time by a team of remote engineers, and there are cases where the remote engineers need to intervene.

In 2020, a National Transportation Safety Board chairman stated that no self-driving cars ( SAE level 3+) were available for consumers to purchase in the US in 2020:

There is not a vehicle currently available to US consumers that is self-driving. Period. Every vehicle sold to US consumers still requires the driver to be actively engaged in the driving task, even when advanced driver assistance systems are activated. If you are selling a car with an advanced driver assistance system, you're not selling a self-driving car. If you are driving a car with an advanced driver assistance system, you don't own a self-driving car.

On 5 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.

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.

In 2014, 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)."

Modern vehicles provide features such as keeping the car within its lane, speed controls, or emergency braking. Those features alone are just considered as driver assistance technologies because they still require a human driver control while fully automated vehicles drive themselves without human driver input.

According to Fortune, some newer vehicles' technology names-such as AutonoDrive, PilotAssist, Full-Self Driving or DrivePilot-might confuse the driver, who may believe no driver input is expected when in fact the driver needs to remain involved in the driving task. According to the BBC, confusion between those concepts leads to deaths.

For this reason, some organizations such as the AAA try to provide standardized naming conventions for features such as ALKS which aim to have capacity to manage the driving task, but which are not yet approved to be an automated vehicles in any countries. 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' mean a vehicle can drive itself when they still rely on the driver to ensure safety. Technology able to drive a car is still in its beta stage.

Some car makers suggest or claim vehicles are self-driving when they are not able to manage some driving situations. Despite being called Full Self-Driving, Tesla stated that its offering should not be considered as a fully autonomous driving system. This makes drivers risk becoming excessively confident, taking distracted driving behaviour, leading to crashes. While in Great-Britain, a fully self-driving car is only a car registered in a specific list. There have also been proposals to adopt the aviation automation safety knowledge into the discussions of safe implementation of autonomous vehicles, due to the experience that has been gained over the decades by the aviation sector on safety topics.

According to the 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 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.

In Europe, the words automated and autonomous might be used together. For instance, Regulation (EU) 2019/2144 of the European Parliament and of the Council of 27 November 2019 on type-approval requirements for motor vehicles (...) defines "automated vehicle" and "fully automated vehicle" based on their autonomous capacity:

"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 any driver supervision;

In British English, the word automated alone might have several meaning, such 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 occurrence refers to the British legal definition of an automated vehicle. The British law interprets the meaning of "automated vehicle" based on the interpretation section related to a vehicle "driving itself" and an insured vehicle.

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 'driver-less' 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 assumes: "Self-driving vehicles are vehicles that can safely and lawfully drive themselves."

A classification system with six levels - ranging from fully manual to fully automated systems - was published in 2014 by standardization body 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 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) had released its original formal classification system. After SAE updated its classification in 2016, called J3016_201609, NHTSA adopted the SAE standard, and SAE classification became widely accepted.

: The automated system issues warnings and may momentarily intervene but has no sustained vehicle control.

("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. Automatic emergency braking which alerts the driver to a crash and permits full braking capacity is also a Level 1 feature, according to Autopilot Review magazine.

("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. The eyes of the driver might be monitored by cameras to confirm that the driver is keeping their attention to traffic. Literal hands off driving is considered level 2.5, although there are no half levels officially. A common example is adaptive cruise control which also utilizes lane keeping assist technology so that the driver simply monitors the vehicle, such as "Super-Cruise" in the Cadillac CT6 by General Motors or Ford's F-150 BlueCruise.

("eyes off"): The driver can safely turn their attention away from the driving tasks, e.g. the driver can text or watch a film. 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. This level of automation can be thought of as a co-driver or co-pilot that's ready to alert the driver in an orderly fashion when swapping their turn to drive. An example would be a Traffic Jam Chauffeur, another example would be a car satisfying the international Automated Lane Keeping Systems (ALKS) regulations.

("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. However, 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. slow down and park the car, if the driver does not retake control. An example would be a robotic taxi or a robotic delivery service that covers selected locations in an area, at a specific time and quantities. Automated valet parking is another example.

("steering wheel optional"): No human intervention is required at all. An example would be a robotic vehicle that works on all kinds of surfaces, all over the world, all year around, in all weather conditions.

In the formal SAE definition below, an important transition is from SAE Level 2 to SAE Level 3 in which the human driver is no longer expected to monitor the environment continuously. 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 always 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
steering and
acceleration/
deceleration

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 the car can pull over safely by guiding system

System

Many driving modes

5

Full Automation

Under all roadway and environmental conditions that can be managed by a human driver

All driving modes

Hybrid navigation is the simultaneous use of more than one navigation system for location data determination, needed for navigation.

Sensing
To reliably and safely operate an autonomous vehicle, usually a mixture of sensors is utilized. Typical sensors include lidar (Light Detection and Ranging), stereo vision, GPS and IMU. 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.

Maps
Self-driving cars require a new class of high-definition maps (HD maps) that represent the world at up to two orders of magnitude more detail. In May 2018, researchers from the Massachusetts Institute of Technology (MIT) 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.

Path planning
Path planning is a computational problem to find a sequence of valid configurations that moves the object from the source to destination. 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. However, these traditional approaches are not sufficient for a vehicle that is interacting with other moving objects, and several advanced approaches applying machine learning are under developments.

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

Driver monitoring system is a vehicle safety system to assess the driver's alertness and warn the driver if needed. It is recognized in developer side that the role of the systems will increase as SAE Level 2 systems become more common-place, and becomes more challenging at Level 3 and above to predict the driver's readiness for handover.

Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Vehicular communication systems use vehicles and roadside units as the communicating nodes in a peer-to-peer network, providing each other with information. This connectivity enables autonomous vehicles to interact with non-autonomous traffic and pedestrians to increase safety. And autonomous vehicles will need to connect to the cloud to update their software and maps, and feedback information to improve the used maps and software of their manufacturer.

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 re-programmable 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.

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.;

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).

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). Homogenization requires that the digital information from these different sources is transmitted and stored in the same form. This means their differences are decoupled, and digital information can be transmitted, stored, and computed in a way that the vehicles and their operating system can better understand and act upon it.

In international standardization field, ISO/TC 22 is in charge of in-vehicle transport information and control systems, and ISO/TC 204 is in charge of information, communication and control systems in the field of urban and rural surface transportation. International standards have been actively developed in the domains of AD/ADAS functions, connectivity, human interaction, in-vehicle systems, management/engineering, dynamic map and positioning, privacy and security.

In 2017, Mobileye published a mathematical model for automated vehicle safety which is called "Responsibility-Sensitive Safety (RSS)". It is under standardization at IEEE Standards Association as "IEEE P2846: A Formal Model for Safety Considerations in Automated Vehicle Decision Making".

The 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 forfeiting control of their cars, concerns about safety, and the implementation of a legal framework and consistent global government regulations for self-driving cars. In addition, cyberattacks could be a potential threat to autonomous driving in the future.

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:[needs update ]

Artificial Intelligence is still not able to function properly in chaotic inner-city environments.

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 high-definition maps to operate properly. Where these maps may be out of date, they would need to be able to fall back to reasonable behaviours.

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.

Validation challenge of Automated Driving and need for novel simulation-based approaches comprising digital twins and agent-based traffic simulation.

Regulation
In 2010s, researchers openly worried about the potential of future regulation to delay deployment of automated cars on the road. However, as written in UNECE WP.29 GRVA, international regulation for Level 3 was smoothly established in 2020, and the uncertainty was resolved. As of 2022, in practice, it is actually very difficult to be approved as Level 3, with the Mercedes-Benz Drive Pilot being one of the few commercially available options to receive such approval.

Deceptive marketing
As Tesla's "Full Self-Driving (FSD)" actually corresponds to Level 2, senators called for investigation to the Federal Trade Commission (FTC) about their marketing claims in August 2021. And in December 2021 in Japan, Mercedes-Benz Japan Co., Ltd. was punished by the Consumer Affairs Agency for the descriptions in their handouts that are different from the fact.

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 organizations 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 programmed such as Formula Student Driver-less have bolstered graduate experience. Industry is steadily increasing freely available information sources, such as code, datasets and glossaries to widen the recruitment pool.

National security
In 2020s, from the importance of the automotive sector to the nation, self-driving car has become a topic of national security. The concerns regarding cybersecurity and data protection are not only important for user protection, but also in the context of national security. The trove of data collected by self-driving cars, paired with cybersecurity vulnerabilities, creates an appealing target for intelligence collection. Self-driving cars are required to be considered in a new way when it comes to espionage risk.

It was in July 2018 that a former Apple engineer was arrested by Federal Bureau of Investigation (FBI) at San Jose International Airport (SJC) while preparing to board a flight to China and charged with stealing proprietary information related to Apple's self-driving car project. And in January 2019, another Apple employee was charged with stealing self-driving car project secrets. In July 2021, United States Department of Justice (DOJ) accused Chinese security officials of a hacking attack seeking data on of coordinating a vast hacking campaign to steal sensitive and secret information from government entities including research related to autonomous vehicles. On the China side, they have already prepared "the Provisions on Management of Automotive Data Security (Trial)".

It is concerned that leapfrogging ability can be applied to autonomous car technology. Also, emerging Cellular V2X (Cellular Vehicle-to-Everything) technologies are based on 5G wireless networks.

Moving obstacles
Self-driving cars are already exploring the difficulties of determining the intentions of pedestrians, bicyclists, and animals, and models of behaviour 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".

Handover and risk compensation
Two human-factor challenges are important for safety. One is the handover from automated driving to manual driving. Human factors research on automated systems has shown that people are slow to detect a problem with automation and slow to understand the problem after it is detected. When automation failures occur, unexpected transitions that require a driver to take over will occur suddenly and the driver may not be ready to take over.

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 behaviour 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.

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. 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. There are three types of trust between human and automation. There is dispositional trust, the trust between the driver and the company's product; there is situational trust, or the trust from different scenarios; and there is learned trust where the trust is built between similar events.

Rationale for liability
There are different opinions on who should be held liable in case of a crash, especially with people being hurt. One study suggests requesting the owners of self-driving cars to sign end-user license agreements (EULAs), assigning to them accountability for any accidents. Other studies 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.

Implications from the Trolley Problem
A moral dilemma that a software engineer or car manufacturer might face in programming the operating software of a self-driving vehicle is captured in a variation of the traditional ethical thought experiment, the trolley problem: An AV is driving with passengers when suddenly a person appears in its way and the car has to commit between one of two options, either to run the person over or to avoid hitting the person by swerving into a wall, killing the passengers. Researchers have suggested, in particular, two ethical theories to be applicable to the behaviour of automated vehicles in cases of emergency: deontology and utilitarianism. Deontological theory suggests that an automated car needs to follow strict written-out rules that it needs to follow in any situation. Utilitarianism, on the other hand, promotes 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. Recently, some specific ethical frameworks i.e., utilitarianism, deontology, relativism, absolutism (monism), and pluralism, are investigated empirically with respect to the acceptance of self-driving cars in unavoidable accidents.

Privacy
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 (see data mining). This information gathering ranges from tracking of the routes taken, voice recording, video recording, preferences in media that is consumed in the car, behavioural 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 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.

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 October 28. 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.

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. Autonomous vans are being developed for use by online grocers such as Ocado.

China trailed the first automated public bus in Henan province in 2015, on a highway linking Zhengzhou and Kaifeng. 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.

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.

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 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 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.

In 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 is known to have been killed by an autonomous vehicle, and considered to raise questions about regulations surrounding the burgeoning self-driving car industry. 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.

In September 2020, the backup driver has been charged of negligent homicide, because she did not look to the road for several seconds while her television was streaming broadcast by Hulu. Uber does not face any criminal charge because in the USA there is no basis for criminal liability for the corporation. The driver is assumed to be responsible of the accident, because she was in the driver seat in capacity to avoid an accident (like in a Level 3). Trial is planned for February 2021.[needs update ]

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.

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 sceptical and 24% were hostile.

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 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 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.

The Geneva Convention on Road Traffic subscribed to by over 101 countries worldwide, requires the driver to be 18 years old.

In March 2021, the following UNECE regulations were published:

Regulation 155: Cyber security and cyber security management system

Regulation 156: Software update and software update management system

Regulation 157: Automated Lane Keeping Systems (ALKS)

On 22 January 2022, these regulations come into effect in some countries.[citation needed ]

Japan is a non-signatory country to the Vienna Convention. In 2019, Japan amended two laws, "Road Traffic Act" and "Road Transport Vehicle Act", and they came into effect in April 2020. In the former act, Level 3 self driving cars became allowed on public roads. In the latter act, process to designate types for safety certification on Level 3 self driving function of Autonomous Driving System (ADS) and the certification process for the asserted type were legally defined. Through the amendment process, the achievements from the national project "SIP-adus" led by Cabinet Office since 2014 were fully considered and accepted.

In May 2020, "The Road Act" was also amended to include definition of automatic operation control equipment in infrastructure, and came into effect. In July 2020, the next stage national level roadmap plan was officially issued which had considered social deployment and acceptability of Level 4. At the end of 2020, Ministry of Land, Infrastructure, Transport and Tourism (MLIT) amended its "Safety Regulation for Road Transport Vehicle" to reflect the finalized UNECE WP.29 GRVA's regulations consistently without delay.

In April 2021, National Police Agency (NPA) published its expert committee's report of FY 2020 on summary of issues in research to realize Level 4 mobility services, including required legal amendment issues. During the summer of 2021, Ministry of Economy, Trade and Industry (METI) prepared with MLIT to launch a project "RoAD to the L4" to cover R&D with social deployment to realize acceptable Level 4 mobility service, and updated its public information in September. As a part of this project, civil law liability problem reflecting changed roles will be clarified.

At the end of 2021, NPA proposed an amendment bill on "Road Traffic Act" to include approving scheme for Level 4 services. In March 2022, the Japanese government adopted the bill to amend the act. In April 2022, the bill was deliberated at the ordinary National Diet session and passed. Under the amended act, a license system will be introduced for operators of transport services using unmanned Level 4 vehicles, which requires no driver in the remotely monitored vehicle within a limited area. Such vehicles are expected to be used for residents in depopulated areas.

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. 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 2017, the Republican-controlled House of Representatives unanimously passed "SELF DRIVE Act" which would speed the adoption of self-driving cars and bar states from setting performance standards. However, a complementary bill in the Senate, "AV START", failed to pass after Democrats raised objections that it didn't do enough to address safety and liability concerns. A comprehensive regulatory structure has not yet emerged at either the federal or state level in the United States.

Federal policies

In September 2016, the US National Economic Council and US Department of Transportation (USDOT) released the Federal Automated Vehicles Policy, which are 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. Since then, USDOT has released multiple updates:

Automated Driving Systems: A Vision for Safety 2.0 (12 September 2017)

Preparing for the Future of Transportation: Automated Vehicles 3.0 (4 October 2018)

Ensuring American Leadership in Automated Vehicle Technologies: Automated Vehicles 4.0 (8 January 2020)

The National Highway Traffic Safety Administration (NHTSA) released for public comment the Occupant Protection for Automated Driving System on 30 March 2020, followed by the Framework for Automated Driving System Safety on 3 December 2020. Occupant Protection is intended to modernize the Federal Motor Vehicle Safety Standards considering the removal of manual controls with automated driving systems, while the Framework document is intended to provide an objective way to define and assess automated driving system competence to ensure motor vehicle safety while also remaining flexible to accommodate the development of features to improve safety.

Historically, a vehicle without driving controls such as a steering wheel, accelerator pedal, and brake pedal would not be in compliance with the Federal Motor Vehicle Safety Standards (FMVSS), the minimum safety equipment needed to legally sell a vehicle to the public. On 10 March 2022, NHTSA updated and finalized the rule on safety requirements for the Occupant Protection which now allows a vehicle without driving controls to comply with US regulations.

State policies

Overview
As of August 2022, 38 states have laws or executive orders related to autonomous vehicles.

Nevada
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 driver-less 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.

Florida
In April 2012, Florida became the second state to allow the testing of automated cars on public roads.

California
California became the third state to allow automated car testing when Governor Jerry Brown signed SB 1298 into law in September 2012 at Google Headquarters in Mountain View.

In Washington, DC's district code:

"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.

Michigan and others
In December 2013, Michigan became the fourth state to allow testing of driver-less 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.

In March 2019, the UK became a signatory country to the Vienna Convention.

In 2021, the UK worked on a bill to allow self-driving automated lane keeping systems (ALKS) up to 37 mph (or 60 km/h) after a mixed reaction of experts during the consultation launched in summer 2020. This system would be allowed to give back control to the driver when "unplanned events" such as road construction or inclement weather occurs. The Centre for Connected and Autonomous Vehicles (CCAV) has asked the Law Commission of England and Wales and the Scottish Law Commission to undertake a far-reaching review of the legal framework for "automated" vehicles, and their use as part of public transport networks and on-demand passenger services. The teams developed policy and the full analysis report was published in January 2022.

About misleading representation in marketing, the Society of Motor Manufacturers and Traders (SMMT) published guiding principles as followings:

1. An automated driving feature must be described sufficiently clearly so as not to mislead, including setting out the circumstances in which that feature can function.

2. An automated driving feature must be described sufficiently clearly so that it is distinguished from an assisted driving feature.

3. Where both automated driving and assisted driving features are described, they must be clearly distinguished from each other.

4. An assisted driving feature should not be described in a way that could convey the impression that it is an automated driving feature.

5. The name of an automated or assisted driving feature must not mislead by conveying that it is the other - ancillary words may be necessary to avoid confusion - for example for an assisted driving feature, by making it clear that the driver must be in control at all times.

In July 2022, the new "Vehicle General Safety Regulation" come into effect which establishes the legal framework for the approval of automated and fully driverless vehicles (Level 3 and above) in the EU. And the EU introduced regulations which require all new cars from 6 July 2022 to be fitted with intelligent speed assistance (ISA) (Level 2).

EU Commission delegated regulation contains specific requirements for specific vehicles with regulatory differences between:

Fully automated vehicles of categories N1, N2 and N3 without driver seat and without occupants

Fully automated vehicles of categories N1, N2, N3, M1, M2, M3 without driver seat, with occupants

Dual mode vehicles: vehicles with a driver seat designed and constructed to be driven by the driver in the "manual driving mode" and to be driven by the automated driving system (ADS) without any driver supervision in the "fully automated driving mode"

In December 2020, the government updated the strategy to make France the preferred location in Europe for the deployment of automated road mobility services.

Germany
Germany is a signatory country to the Vienna Convention. In July 2021, the "Federal Act Amending the Road Traffic Act and the Compulsory Insurance Act" came into effect. The Act allows motor vehicles with autonomous driving capabilities, meaning vehicles that can perform driving tasks independently without a person driving, in specified operating areas on public roads. Provisions about autonomous driving in appropriate operating areas correspond to Level 4. Moreover, the new German legislation has major implications for dilemmatic situations. This includes for example the non-discrimination principle that applies to unavoidable crash situations. Moreover, the act elaborates on the technical requirements of autonomous vehicles, including a software system that can operate without permanent supervision of the technical oversight or driver, contains an accident mitigation and reduction system and can initiate a "minimal-risk state."

Canada
Canada is a non-signatory country to the Vienna Convention. At the federal level, "The Motor Vehicle Safety Act" regulates about motor vehicles which was last amended in February 2020.

China
For historical reason, China is not a signatory country to 1949 Geneva Convention, although it is a signatory country to 1968 Vienna Convention. Legislations are conducted at National People's Congress and its Standing Committee which are under control of Chinese Communist Party.

In March 2021, Ministry of Public Security (MPS) published draft proposed amendments on the "Road Traffic Safety Law". In August 2021, The Cyberspace Administration of China (CAC) and MIIT issued "the Provisions on Management of Automotive Data Security (Trial)". In September 2021, " Data Security Law" came into effect, which broadly expands the extraterritorial reach of China's existing data rules.

In February 2022, MIIT issued the second draft of the "Administrative Measures for Data Security in the Industry and Information Technology Fields". And in March 2022, MIIT issued "the Guidelines for the Construction of the Internet of Vehicles Cybersecurity and Data Security Standard System".

Australia
Australia is a non-signatory country to the Vienna Convention. National Transport Commission (NTC) is in charge of reforming current laws with still achieving national level consistency. In February 2022, NTC published a policy paper to present proposals on the end-to-end regulatory framework for the commercial deployment of automated vehicles.

New Zealand
New Zealand is a non-signatory country to the Vienna Convention. New Zealand legislation does not specifically require a driver to be present for a vehicle to be legally operated on a public road. However, most regulations and relevant international frameworks strongly imply the presence of a driver in the vehicle given that 'automation' was not a consideration at the time of drafting the legislation.

Israel
Israelis a signatory country to the Vienna Convention. As of April 2022, Israel Innovation Authority (OCS) is working on forming regulatory framework for trials and use of autonomous vehicles with Ministry of Transport and Road Safety (MOT) and Ministry of Justice.

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.

In January 2022, Polestar, a Volvo Cars' brand, indicated 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.

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 April 2022, Honda unveiled its Level 4 mobility service partners to roll out in central Tokyo in the mid-2020s using the Cruse Origin.

Also in April 2022, Volkswagen started testing of its autonomous ID. Buzz AD prototype with Argo AI on public roads. And in May 2022, Argo AI started testing on public roads in Austin and Miami using modified Ford Escape Hybrid.

Media related to Self-driving car at Wikimedia Commons

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A self-driving car, also known as an autonomous car, driver-less car, or robotic car ( robo-car), is a car incorporating vehicular automation, that is, a ground vehicle that is capable of sensing its environment and moving safely with little or no human input.