Artificial Intelligence - Intelligent Transportation.

  

The use of advanced technology, artificial intelligence, and control systems to manage highways, cars, and traffic is known as intelligent transportation.

Traditional American highway engineering disciplines such as driver routing, junction management, traffic distribution, and system-wide command and control inspired the notion.

Because it attempts to embed monitoring equipment in pavements, signaling systems, and individual automobiles in order to decrease congestion and increase safety, intelligent transportation has significant privacy and security considerations.

Highway engineers of the 1950s and 1960s were commonly referred to as "communications engineers," since they used information in the form of signs, signals, and statistics to govern vehicle and highway interactions and traffic flow.

During these decades, computers were primarily employed to simulate crossings and calculate highway capacity.

S. Y. Wong's Traffic Simulator, which used the facilities of the Institute for Advanced Investigate (IAS) computer in Princeton, New Jersey, to study traffic engineering, is one of the early applications of computing technology in this respect.

To show road systems, traffic regulations, driver behavior, and weather conditions, Wong's mid-1950s simulator used computational tools previously established to investigate electrical networks.

Dijkstra's Algorithm, named for computer scientist Edsger Dijkstra, was a pioneering use of information technology to automatically construct and map least distance routes.

In 1959, Dijkstra created an algorithm that finds the shortest path between a starting point and a destination point on a map.

Online mapping systems still use Dijsktra's routing method, and it has significant economic utility in traffic management planning.

Other algorithms and automated devices for guidance control, traffic signaling, and ramp metering were developed by the automotive industry during the 1960s.

Many of these devices, such as traffic right-of-way signals coupled to transistorized fixed-time control boxes, synchronized signals, and traffic-actuated vehicle pressure detectors, became commonplace among the public.

Despite this, traffic control system simulation in experimental labs has remained an essential use of information technology in transportation.

Despite the engineers' best efforts, the rising popularity of vehicles and long-distance travel stressed the national highway system in the 1960s, resulting in a "crisis" in surface transportation network operations.

By the mid-1970s, engineers were considering information technology as a viable alternative to traditional signaling, road expansion, and grade separation approaches for decreasing traffic congestion and increasing safety.

Much of this work was focused on the individual driver, who was supposed to be able to utilize data to make real-time choices that would make driving more enjoyable.

With onboard instrument panels and diagnostics, computing technology promised to make navigation simpler and maximize safety while lowering journey times, particularly when combined with other technologies like as radar, the telephone, and television cameras.

Automobiling became more informed as computer chip prices dropped in the 1980s.

Electronic fuel gauges and oil level indicators, as well as digital speedometer readouts and other warnings, were added to high-end automobile models.

Most states' television broadcasts started delivering pre-trip travel information and weather briefings in the 1990s, based on data and video collected automatically by roadside sensing stations and video cameras.

These summaries were made accessible at roadside way stations, where passengers could get live text-based weather forecasts and radar pictures on public computer displays, as well as as text on pagers and mobile phones.

Few of these innovations have a significant influence on personal privacy or liberty.

However, in 1991, Congress approved the Intermodal Surface Transportation Efficiency Act (ISTEA or "Ice Tea"), which allowed $660 million for the creation of the country's Intelligent Vehicle Highway System (IVHS), which was coauthored by Secretary of Transportation Norman Mineta.

Improved safety, decreased congestion, greater mobility, energy efficiency, economic productivity, increased usage of public transit, and environmental cleanup are among the aims set out in the act.

All of these objectives would be achieved via the effective use of information technology to facilitate transportation in the aggregate as well as on a vehicle-by-vehicle basis.

Hundreds of projects were funded to provide new infrastructure and possibilities for travel and traffic management, public transit management, electronic toll payment, commercial fleet management, emergency management, and vehicle safety, among other things.

While some applications of intelligent transportation technology remained underutilized in the 1990s—for example, carpool matching—other applications became virtually standard on American highways: for example, onboard safety monitoring and precrash deployment of airbags in cars, or automated weigh stations, roadside safety inspections, and satellite Global Positioning System (GPS) tracking for tractor-trailers.

Private enterprise had joined the government in augmenting several of these services by the mid-1990s.

OnStar, a factory-installed telematics system that utilizes GPS and cell phone communications to give route guidance, summon emergency and roadside assistance services, track stolen cars, remotely diagnose mechanical faults, and access locked doors, is included in all General Motors vehicles.

Automobile manufacturers also started experimenting with infrared sensors coupled to expert systems for autonomous collision avoidance, as well as developing technology that allows automobiles to be "platooned" into huge groups of closely spaced vehicles to optimize highway lane capacity.

The computerized toll and traffic management system, which was launched in the 1990s, was perhaps the most widely used implementation of intelligent transportation technology (ETTM).

By placing a radio transponder on their cars, ETTM enabled drivers to pay their tolls on highways without having to slow down.

Florida, New York State, New Jersey, Michigan, Illinois, and California were all using ETTM systems by 1995.

Since then, ETTM has expanded to a number of additional states as well as internationally.

Because of its potential for government intrusion, intelligent transportation initiatives have sparked debate.

Hong Kong's government deployed electronic road pricing (ERP) in the mid-1980s, with radar transmitter-receivers triggered when cars went through tolled tunnels or highway checkpoints.

This system's billing bills supplied drivers with a full record of where they had gone and when they had been there.

The system was put on hold before the British turned Hong Kong over to the Chinese in 1997, due to concerns about potential human rights violations.

On the other hand, the basic purpose of political surveillance is sometimes broadened to include transportation goals.

The UK government, for example, placed street-based closed-circuit television cameras (CCTV) in a "Ring of Steel" around London's financial sector in 1993 to defend against Irish Republican Army terror bombs.

Extreme CCTV enhanced monitoring of downtown London ten years later, in 2003, to incorporate several infrared illuminators enabling the "capture" of license plate numbers on autos.

A daily usage tax was imposed on drivers entering the crowded downtown area.

Vehicles with a unique identification like the Vehicle Identification Number (VIN) or an electronic tag may be tracked using technologies like GPS and electronic payment tollbooth software, for example.

This opens the door to continuous monitoring and tracking of driving choices, as well as the prospect of permanent movement records.

Individual toll crossing locations and timings, the car's average speed, and photos of all passengers are routinely obtained by intelligent transportation surveillance.

In the early 2000s, state transportation bureaus in Florida and California utilized comparable data to send out surveys to individual drivers who used certain roads.

Several state motor vehicle agencies have also contemplated establishing "dual-use" intelligent transportation databanks to supply or sell traffic and driver-related data to law enforcement and marketers.

Artificial intelligence methods are becoming an increasingly important part of intelligent transportation planning, especially as large amounts of data from actual driving experiences are now being collected.

They're being used more and more to control vehicles, predict traffic congestion, and meter traffic, as well as reduce accident rates and fatalities.

Artificial neural networks, genetic algorithms, fuzzy logic, and expert systems are among the AI techniques already in use in various intelligent transportation applications, both singly and in combination.

These methods are being used to develop new vehicle control systems for autonomous and semiautonomous driving, automatic braking control, and real-time energy consumption and emissions monitoring.

Surtrac, for example, is a scalable, adaptive traffic control system created by Carnegie Mellon University, which uses theoretical modeling and artificial intelligence algorithms.

The amount of traffic on particular roadways and intersections may change dramatically throughout the day.

Traditional automated traffic control technology adapts to established patterns on a set timetable or depends on traffic control observations from a central location.

Intersections can communicate with one another, and automobiles may possibly exchange their user-programmed travel paths, thanks to adaptive traffic management.

Vivacity Labs in the United Kingdom employs video sensors at junctions and AI technology to monitor and anticipate traffic conditions in real time during an individual motorist's trip, as well as perform mobility assessments for enterprises and local government bodies at the city scale.

Future paths in intelligent transportation research and development may be determined by fuel costs and climate change consequences.

When oil costs are high, rules may encourage sophisticated traveler information systems that advise drivers to the best routes and departure times, as well as expected (and costly) idling and wait periods.

If traffic grows more crowded, cities may use smart city technologies like real-time traffic and parking warnings, automated incident detection and vehicle recovery, and linked surroundings to govern human-piloted vehicles, autonomous automobiles, and mass transit systems.

More cities throughout the globe are going to use dynamic cordon pricing, which entails calculating and collecting fees to enter or drive in crowded regions.

Vehicle occupancy detection monitors and vehicle categorization detectors are examples of artificial intelligence systems that enable congestion charging.

 

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Driverless Cars and Trucks; Trolley Problem.

Further Reading:

Alpert, Sheri. 1995. “Privacy and Intelligent Highway: Finding the Right of Way.” Santa Clara Computer and High Technology Law Journal 11: 97–118.

Blum, A. M. 1970. “A General-Purpose Digital Traffic Simulator.” Simulation 14, no. 1: 9–25.

Diebold, John. 1995. Transportation Infostructures: The Development of Intelligent Transportation Systems. Westport, CT: Greenwood Publishing Group.

Garfinkel, Simson L. 1996. “Why Driver Privacy Must Be a Part of ITS.” In Converging Infrastructures: Intelligent Transportation and the National Information Infrastructure, edited by Lewis M. Branscomb and James H. Keller, 324–40. Cambridge, MA: MIT Press.

High-Tech Highways: Intelligent Transportation Systems and Policy. 1995. Washington, DC: Congressional Budget Office.

Machin, Mirialys, Julio A. Sanguesa, Piedad Garrido, and Francisco J. Martinez. 2018. “On the Use of Artificial Intelligence Techniques in Intelligent Transportation Systems.” In IEEE Wireless Communications and Networking Conference Worshops (WCNCW), 332–37. Piscataway, NJ: IEEE.

Rodgers, Lionel M., and Leo G. Sands. 1969. Automobile Traffic Signal Control Systems. Philadelphia: Chilton Book Company.

Wong, S. Y. 1956. “Traffic Simulator with a Digital Computer.” In Proceedings of the Western Joint Computer Conference, 92–94. New York: American Institute of Electrical Engineers.

Artificial Intelligence - What Is The Liability Of Self-Driving Vehicles?

 

Driverless cars may function completely or partly without the assistance of a human driver.

Driverless automobiles, like other AI products, confront difficulties with liability, responsibility, data protection, and customer privacy.

Driverless cars have the potential to eliminate human carelessness while also providing safe transportation for passengers.

They have been engaged in mishaps despite their potential.

The Autopilot software on a Tesla SUV may have failed to notice a huge vehicle crossing the highway in a well-publicized 2016 accident.

A Tesla Autopilot may have been involved in the death of a 49-year-old woman in 2018.

A class action lawsuit was filed against Tesla as a result of these occurrences, which the corporation resolved out of court.

Additional worries about autonomous cars have arisen as a result of bias and racial prejudice in machine vision and face recognition.

Current driverless cars may be better at spotting people with lighter skin, according to Georgia Institute of Technology researchers.

Product liability provides some much-needed solutions to such problems.

The Consumer Protection Act of 1987 governs product liability claims in the United Kingdom (CPA).

This act enacts the European Union's (EU) Product Liability Directive 85/374/EEC, which holds manufacturers liable for product malfunctions, i.e., items that are not as safe as they should be when bought.

This contrasts with U.S. law addressing product liability, which is fragmented and largely controlled by common law and a succession of state acts.

The Uniform Commercial Code (UCC) offers remedies where a product fails to fulfill stated statements, is not merchantable, or is inappropriate for its specific use.

In general, manufacturers are held accountable for injuries caused by their faulty goods, and this responsibility may be handled in terms of negligence or strict liability.

A defect in this situation could be a manufacturer defect, where the driverless vehicle does not satisfy the manufacturer’s specifications and standards; a design defect, which can result when an alternative design would have prevented an acci dent; or a warning defect, where there is a failure to provide enough warning as regards to a driverless car’s operations.

To evaluate product responsibility, the five stages of automation specified by the Society of Automotive Engineers (SAE) International should be taken into account: Level 0, full control of a vehicle by a driver; Level 1, a human driver assisted by an automated system; Level 2, an automated system partially conduct ing the driving while a human driver monitors the environment and performs most of the driving; Level 3, an automated system does the driving and monitor ing of the environment, but the human driver takes back control when signaled; Level 4, the driverless vehicle conducts driving and monitors the environment but is restricted in certain environment; and Level 5, a driverless vehicle without any restrictions does everything a human driver would.

In Levels 1–3 that involve human-machine interaction, where it is discovered that the driverless vehicle did not communicate or send out a signal to the human driver or that the autopilot software did not work, the manufacturer will be liable based on product liability.

At Level 4 and Level 5, liability for defective product will fully apply.

Manufacturers have a duty of care to ensure that any driverless vehicle they manufacture is safe when used in any foreseeable manner.

Failure to exercise this duty will make them liable for negligence.

In some other cases, even when manufacturers have exercised all reasonable care, they will still be liable for unintended defects as per the strict liability principle.

The liability for the driver, especially in Levels 1–3, could be based on tort principles, too.

The requirement of article 8 of the 1949 Vienna Convention on Road Traffic, which states that “[e]very vehicle or combination of vehicles proceeding as a unit shall have a driver,” may not be fulfilled in cases where a vehicle is fully automated.

In some U.S. states, namely, Nevada and Florida, the word driver has been changed to controller, and the latter means any person who causes the autonomous technology to engage; the person must not necessarily be present in the vehicle.

A driver or controller becomes responsible if it is proved that the obligation of reasonable care was not performed by the driver or controller or they were negligent in the observance of this duty.

In certain other cases, victims will only be reimbursed by their own insurance companies under no-fault responsibility.

Victims may also base their claims for damages on the strict responsibility concept without having to present proof of the driver’s fault.

In this situation, the driver may demand that the manufacturer be joined in a lawsuit for damages if the driver or the controller feels that the accident was the consequence of a flaw in the product.

In any case, proof of the driver's or controller's negligence will reduce the manufacturer's liability.

Third parties may sue manufacturers directly for injuries caused by faulty items under product liability.

According to MacPherson v. Buick Motor Co. (1916), where the court found that an automobile manufacturer's duty for a faulty product goes beyond the initial consumer, there is no privity of contract between the victim and the maker.

The question of product liability for self-driving vehicles is complex.

The transition from manual to smart automated control transfers responsibility from the driver to the manufacturer.

The complexity of driving modes, as well as the interaction between the human operator and the artificial agent, is one of the primary challenges concerning accident responsibility.

In the United States, the law of motor vehicle product liability relating to flaws in self-driving cars is still in its infancy.

While the Department of Transportation and, especially, the National Highway Traffic Safety Administration give some basic recommendations on automation in driverless vehicles, Congress has yet to adopt self-driving car law.

In the United Kingdom, the Automated and Electric Cars Act of 2018 makes insurers accountable by default for accidents using automated vehicles that result in death, bodily injury, or property damage, providing the vehicles were in self-driving mode and insured at the time of the accident.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Accidents and Risk Assessment; Product Liability and AI; Trolley Problem.

Further Reading:

Geistfeld. Mark A. 2017. “A Roadmap for Autonomous Vehicles: State Tort Liability, Automobile Insurance, and Federal Safety Regulation.” California Law Review 105: 1611–94.

Hevelke, Alexander, and Julian Nida-Rümelin. 2015. “Responsibility for Crashes of Autonomous Vehicles: An Ethical Analysis.” Science and Engineering Ethics 21, no. 3 (June): 619–30.

Karanasiou, Argyro P., and Dimitris A. Pinotsis. 2017. “Towards a Legal Definition of Machine Intelligence: The Argument for Artificial Personhood in the Age of Deep Learning.” In ICAIL ’17: Proceedings of the 16th edition of the International Conference on Artificial Intelligence and Law, edited by Jeroen Keppens and Guido Governatori, 119–28. New York: Association for Computing Machinery.

Luetge, Christoph. 2017. “The German Ethics Code for Automated and Connected Driving.” Philosophy & Technology 30 (September): 547–58.

Rabin, Robert L., and Kenneth S. Abraham. 2019. “Automated Vehicles and Manufacturer Responsibility for Accidents: A New Legal Regime for a New Era.” Virginia Law Review 105, no. 1 (March): 127–71.

Wilson, Benjamin, Judy Hoffman, and Jamie Morgenstern. 2019. “Predictive Inequity in Object Detection.” https://arxiv.org/abs/1902.11097.

Artificial Intelligence - How Do Autonomous Vehicles Leverage AI?

Using a virtual driver system, driverless automobiles and trucks, also known as self-driving or autonomous vehicles, are capable of moving through settings with little or no human control.

A virtual driver system is a set of characteristics and capabilities that augment or replicate the actions of an absent driver to the point that, at the maximum degree of autonomy, the driver may not even be present.

Diverse technology uses, restricting circumstances, and categorization methods make reaching an agreement on what defines a driverless car difficult.

A semiautonomous system, in general, is one in which the human performs certain driving functions (such as lane maintaining) while others are performed autonomously (such as acceleration and deceleration).

All driving activities are autonomous only under certain circumstances in a conditionally autonomous system.

All driving duties are automated in a fully autonomous system.

Automobile manufacturers, technology businesses, automotive suppliers, and universities are all testing and developing applications.

Each builder's car or system, as well as the technical road that led to it, demonstrates a diverse range of technological answers to the challenge of developing a virtual driving system.

Ambiguities exist at the level of defining circumstances, so that a same technological system may be characterized in a variety of ways depending on factors such as location, speed, weather, traffic density, human attention, and infrastructure.

When individual driving duties are operationalized for feature development and context plays a role in developing solutions, more complexity is generated (such as connected vehicles, smart cities, and regulatory environment).

Because of this complication, producing driverless cars often necessitates collaboration across several roles and disciplines of study, such as hardware and software engineering, ergonomics, user experience, legal and regulatory, city planning, and ethics.

The development of self-driving automobiles is both a technical and a socio-cultural enterprise.

The distribution of mobility tasks across an array of equipment to collectively perform a variety of activities such as assessing driver intent, sensing the environment, distinguishing objects, mapping and wayfinding, and safety management are among the technical problems of engineering a virtual driver system.

LIDAR, radar, computer vision, global positioning, odometry, and sonar are among the hardware and software components of a virtual driving system.

There are many approaches to solving discrete autonomous movement problems.

With cameras, maps, and sensors, sensing and processing can be centralized in the vehicle, or it can be distributed throughout the environment and across other vehicles, as with intelligent infrastructure and V2X (vehicle to everything) capability.

The burden and scope of this processing—and the scale of the problems to be solved—are closely related to the expected level of human attention and intervention, and as a result, the most frequently referenced classification of driverless capability is formally structured along the lines of human attentional demands and intervention requirements by the Society of Automotive Engineers, and has been adopted in 2 years.

These companies use various levels of driver automation, ranging from Level 0 to Level 5.

Level 0 refers to no automation, which means the human driver is solely responsible for longitudinal and latitudinal control (steering) (acceleration and deceleration).

On Level 0, the human driver is in charge of keeping an eye on the environment and reacting to any unexpected safety hazards.

Automated systems that take control of longitudinal or latitudinal control are classified as Level 1, or driver aid.

The driver is in charge of observation and intervention.

Level 2 denotes partial automation, in which the virtual driver system is in charge of both lateral and longitudinal control.

The human driver is deemed to be in the loop, which means that they are in charge of monitoring the environment and acting in the event of a safety-related emergency.

Level 2 capability has not yet been achieved by commercially available systems.

The monitoring capabilities of the virtual driving system distinguishes Level 3 conditional autonomy from Level 2.

At this stage, the human driver may be disconnected from the surroundings and depend on the autonomous system to keep track of it.

The person is required to react to calls for assistance in a range of situations, such as during severe weather or in construction zones.

A navigation system (e.g., GPS) is not required at this level.

To operate at Level 2 or Level 3, a vehicle does not need a map or a specific destination.

A human driver is not needed to react to a request for intervention at Level 4, often known as high automation.

The virtual driving system is in charge of navigation, locomotion, and monitoring.

When a specific condition cannot be satisfied, such as when a navigation destination is obstructed, it may request that a driver intervene.

If the human driver does not choose to interfere, the system may safely stop or redirect based on the engineering approach.

The classification of this situation is based on standards of safe driving, which are established not only by technical competence and environmental circumstances, but also by legal and regulatory agreements and lawsuit tolerance.

Level 5 autonomy, often known as complete automation, refers to a vehicle that is capable of doing all driving activities in any situation that a human driver could handle.

Although Level 4 and Level 5 systems do not need the presence of a person, they still necessitate substantial technological and social cooperation.

While efforts to construct autonomous vehicles date back to the 1920s, Leonardo Da Vinci is credited with the concept of a self-propelled cart.

In his 1939 New York World's Fair Futurama display, Norman Bel Geddes envisaged a smart metropolis of the future inhabited by self-driving automobiles.

Automobiles, according to Bel Geddes, will be outfitted with "technology that would rectify the mistakes of human drivers" by 1960.

General Motors popularized the concept of smart infrastructure in the 1950s by building a "automated highway" with steering-assist circuits.

In 1960, the business tested a working prototype car, but owing to the expensive expense of infrastructure, it quickly moved its focus from smart cities to smart autos.

A team lead by Sadayuki Tsugawa of Tsukuba Mechanical Engineering Laboratory in Japan created an early prototype of an autonomous car.

Their 1977 vehicle operated under predefined environmental conditions dictated by lateral guiding rails.

The truck used cameras to track the rails, and most of the processing equipment was aboard.

The EUREKA (European Research Organization) pooled money and experience in the 1980s to enhance the state-of-the-art in cameras and processing for autonomous cars.

Simultaneously, Carnegie Mellon University in Pittsburgh, Pennsylvania pooled their resources for research on autonomous navigation utilizing GPS data.

Since then, automakers including General Motors, Tesla, and Ford Motor Company, as well as technology firms like ARGO AI and Waymo, have been working on autonomous cars or critical components.

The technology is becoming less dependent on very limited circumstances and more adaptable to real-world scenarios.

Manufacturers are currently producing Level 4 autonomous test cars, and testing are being undertaken in real-world traffic and weather situations.

Commercially accessible Level 4 self-driving cars are still a long way off.

There are supporters and opponents of autonomous driving.

Supporters point to a number of benefits that address social problems, environmental concerns, efficiency, and safety.

The provision of mobility services and a degree of autonomy to those who do not already have access, such as those with disabilities (e.g., blindness or motor function impairment) or those who are unable to drive, such as the elderly and children, is one such social benefit.

The capacity to decrease fuel economy by managing acceleration and braking has environmental benefits.

Because networked cars may go bumper to bumper and are routed according to traffic optimization algorithms, congestion is expected to be reduced.

Finally, self-driving vehicles have the potential to be safer.

They may be able to handle complicated information more quickly and thoroughly than human drivers, resulting in fewer collisions.

Self-driving car negative repercussions may be included in any of these areas.

In terms of society, driverless cars may limit access to mobility and municipal services.

Autonomous mobility may be heavily regulated, costly, or limited to places that are inaccessible to low-income commuters.

Non-networked or manually operated cars might be kept out of intelligent geo-fenced municipal infrastructure.

Furthermore, if no adult or responsible human party is present during transportation, autonomous automobiles may pose a safety concern for some susceptible passengers, such as children.

Greater convenience may have environmental consequences.

Drivers may sleep or work while driving autonomously, which may have the unintended consequence of extending commutes and worsening traffic congestion.

Another security issue is widespread vehicle hacking, which could bring individual automobiles and trucks, or even a whole city, to a halt. 

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Accidents and Risk Assessment; Autonomous and Semiautonomous Systems; Autonomy and Complacency; Intelligent Transportation; Trolley Problem.

Further Reading:

Antsaklis, Panos J., Kevin M. Passino, and Shyh J. Wang. 1991. “An Introduction to Autonomous Control Systems.” IEEE Control Systems Magazine 11, no. 4: 5–13.

Bel Geddes, Norman. 1940. Magic Motorways. New York: Random House.

Bimbraw, Keshav. 2015. “Autonomous Cars: Past, Present, and Future—A Review of the Developments in the Last Century, the Present Scenario, and the Expected Future of Autonomous Vehicle Technology.” In ICINCO: 2015—12th International Conference on Informatics in Control, Automation and Robotics, vol. 1, 191–98. Piscataway, NJ: IEEE.

Kröger, Fabian. 2016. “Automated Driving in Its Social, Historical and Cultural Contexts.” In Autonomous Driving, edited by Markus Maurer, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, 41–68. Berlin: Springer.

Lin, Patrick. 2016. “Why Ethics Matters for Autonomous Cars.” In Autonomous Driving, edited by Markus Maurer, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, 69–85. Berlin: Springer.

Weber, Marc. 2014. “Where To? A History of Autonomous Vehicles.” Computer History Museum. https://www.computerhistory.org/atchm/where-to-a-history-of-autonomous-vehicles/.