Autonomous cars are self driving cars ,also known as Robotic cars or driver less cars that are capable
to drive with human input.
They uses various types of sensors to perceive their surrounding like optical and thermographic cameras, radar, lidar, ultrasound/sonar, GPS, odometry and inertial measurement units.
Control systems interpret sensory information to create a three-dimensional model of the vehicle’s surroundings. Based on the model, the car then identifies an appropriate navigation path and strategies for managing traffic controls (stop signs, etc.) and obstacles.
According to the level divided by SAE the level is divided into 6 levels: 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
The first semi-automated car was developed in 1977, by Japan’s Tsukuba Mechanical Engineering Laboratory, which required specially marked streets that were interpreted by two cameras on the vehicle and an analog computer. The vehicle reached speeds up to 30 kilometers per hour (19 mph) with the support of an elevated rail
Roborace Autonomous cars is displayed at New York city 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.
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.
Autonomous Vs Automated
Autonomous means self-governing. Many historical projects related to vehicle automation have been automated (made automatic) subject to a heavy reliance on artificial aids in their environment, such as magnetic strips. Autonomous control implies satisfactory performance under significant uncertainties in the environment, and the ability to compensate for system failures without external intervention.
Level Of Driving Autonomous:
Level 0: The automated system issues warnings and may momentarily intervene but has no sustained vehicle control.
Level 1: (“hands on”): The driver and the automated system share control of the vehicle. Examples are systems where the driver controls steering and the automated system controls engine power to maintain a set speed (Cruise control) or engine and brake power to maintain and vary speed (Adaptive cruise control or ACC); and Parking Assistance, where steering is automated while speed is under manual control.
Level 2: (“hands off”): The automated system takes full control of the vehicle: accelerating, braking, and steering. The driver must monitor the driving and be prepared to intervene immediately at any time if the automated system fails to respond properly.
Level 3 (“eyes off”): The driver can safely turn their attention away from the driving tasks, e.g. the driver can text or watch a 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.
Level 4 (“mind off“): As level 3, but no driver attention is ever required for safety, e.g. the driver may safely go to sleep or leave the driver’s seat. However, self-driving is supported only in limited spatial areas (geofenced) or under special circumstances.
Level 5 (“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.
Several classifications have been proposed to deal with the broad range of technological discussions pertaining to self-driving cars. One such proposal is to classify based on the following categories; car navigation, path planning, environment perception and car control. In the 2020s, it became apparent that these technologies are far more complex than initially thought. Even video games have been used as a platform to test autonomous vehicles.
Hybrid navigation is the simultaneous use of more than one navigation system for location data
determination, needed for navigation.
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.
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.
Control systems on automated cars may use sensor fusion, which is an approach that integrates
information from a variety of sensors on the car to produce a more consistent, accurate, and
useful view of the environment. Self-driving cars tend to use a combination of cameras, LiDAR
sensors, and radar sensors in order to enhance performance and ensure the safety of the
passenger and other drivers on the road. An increased consistency in self-driving performance
prevents accidents that may occur because of one faulty sensor.
Path planning is a computational problem to find a sequence of valid configurations that moves the object from the source to destination. Self-driving cars rely on path planning technology in order to follow the rules of traffic and prevent accidents from occurring. 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.
A driving corridors algorithm allows the vehicle to locate and drive within open free space that is bounded by lanes or barriers. While these algorithms work in a simple situation, path planning has not been proven to be effective in a complex scenario.
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.
Main article: Drive by wire
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
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.
Re-programmable See also: Over-the-air programming
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.
In March 2021, UNECE regulation on software update and software update management system
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.
- 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.
- 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.
- The service layer contains the applications and their functionalities that serve the autonomous vehicle (and its owners) as they extract, create, store and consume content regarding their driving history, traffic congestion, roads or parking abilities, for example.
- 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.
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. Mathematical safety model 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”.
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
1.OBSTACLE: 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.
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.
In the 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.
Self-driving cars are already exploring the difficulties of determining the intentions of
pedestrians, bicyclists, and animals, and models of behavior must be programmed into
driving algorithms. Human road users also have the challenge of determining the
intentions of autonomous vehicles, where there is no driver with which to make eye
contact or exchange hand signals.
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.
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.
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.
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.
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
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 suspension was made after the accident, and restarted on 31 with improved safety measures.
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. ublic opinion surveysIn 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.