AUTONOMOUS VEHICLE

 

A self-driving car, also known as an autonomous vehicle (AV), driverless 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. The future of this technology may have an impact on multiple industries and other circumstances.

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.

Possible implementations of the technology include personal self-driving vehicles, shared robotaxis, and connected vehicle platoons. Several projects to develop a fully self-driving commercial car are in various stages of development, but there are no self-driving cars available for everyday consumers.

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 December 2021, vehicles operating at Level 3 and above remain a marginal portion of the market. Waymo became the first service provider to offer driver-less taxi rides to the general public in a part of Phoenix, Arizona in 2020. However, while there is no driver in the car, the vehicles still have remote human overseers. In March 2021, Honda became the first manufacturer to provide a legally approved Level 3 vehicle, 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 complying with legal requirements.

Terminology and safety considerations

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."

Challenges:

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.

Other obstacles could include de-skilling and lower levels of driver experience for dealing with potentially dangerous situations and anomalies, ethical problems where an automated vehicle's software is forced during an unavoidable crash to choose between multiple harmful courses of action ('the trolley problem'), concerns about making large numbers of people currently employed as drivers unemployed, the potential for more intrusive mass surveillance of location, association and travel as a result of police and intelligence agency access to large data sets generated by sensors and pattern-recognition AI, and possibly insufficient understanding of verbal sounds, gestures and non-verbal cues by police, other drivers or pedestrians.

Possible technological obstacles for automated cars are:

• Artificial Intelligence is still not able to function properly in chaotic inner-city environments.
• A car's computer could potentially be compromised, as could a communication system between cars.
• Susceptibility of the car's sensing and navigation systems to different types of weather (such as snow) or deliberate interference, including jamming and spoofing.
• Avoidance of large animals requires recognition and tracking, and Volvo found that software suited to caribou, deer, and elk was ineffective with kangaroos.
• Autonomous cars may require 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 behaviors.
• Competition for the radio spectrum desired for the car's communication.
• Field programmability for the systems will require careful evaluation of product development and the component supply chain.
• Current road infrastructure may need changes for automated cars to function optimally.
• Validation challenge of Automated Driving and need for novel simulation-based approaches comprising digital twins and agent-based traffic simulation.

Social challenges include:

• Uncertainty about potential future regulation may delay deployment of automated cars on the road.
• Employment – Companies working on the technology have an increasing recruitment problem in that the available talent pool has not grown with demand. As such, education and training by third-party organizations such as providers of online courses and self-taught community-driven projects such as DIY Robo cars and Formula Pi have quickly grown in popularity, while university level extra-curricular programmes such as Formula Student Driverless  have bolstered graduate experience. Industry is steadily increasing freely available information sources, such as code, datasets and glossaries to widen the recruitment pool.