Self-driving car Outline A self-driving car (also known as an autonomous car or a driverless car) is a vehicle that is capable of sensing its environment and moving with little or no human input. Autonomous cars combine a variety of sensors to perceive their surroundings, such as radar, computer vision, 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. Potential benefits include reduced costs, increased safety, increased mobility, increased customer satisfaction and reduced crime. Safety benefits include a reduction in traffic collisions, resulting injuries and related costs, including for insurance. Automated cars are predicted to increase traffic flow; provide enhanced mobility for children, the elderly, disabled, and the poor; relieve travelers from driving and navigation chores; lower fuel consumption; significantly reduce needs for parking space; reduce crime; and facilitate business models for transportation as a service, especially via the sharing economy. Problems include safety, technology, liability, desire by individuals to control their cars, legal framework and government regulations; risk of loss of privacy and security concerns, such as hackers or terrorism; concern about the resulting loss of driving-related jobs in the road transport industry; and risk of increased suburbanization as travel becomes more convenient. History General Motors’ Firebird II of the 1950s was described as having an “electronic brain” that allowed it to move into a lane with a metal conductor and follow it along.
Technology
Waymo Chrysler Pacifica Hybrid undergoing testing in the San Francisco Bay Area.
Experiments have been conducted on automating driving since at least the 1920s; trials began in the 1950s. The first truly automated car was developed in 1977, by Japan’s Tsukuba Mechanical Engineering Laboratory. The vehicle tracked white street markers, which were interpreted by two cameras on the vehicle, using an analog computer for signal processing. The vehicle reached speeds up to 30 kilometres per hour (19 mph), with the support of an elevated rail. Autonomous prototype cars appeared in the 1980s, with Carnegie Mellon University’s Navlab and ALV projects funded by 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 kilometers per hour (19 mph) with obstacle avoidance added in 1986 and off-road driving in day and nighttime conditions by 1987. From the 1960s through the second DARPA Grand Challenge in 2005, automated vehicle research in the U.S. was primarily funded by DARPA, the US Army and the U.S. Navy yielding incremental advances in speeds, driving competence in more complex conditions, controls and sensor systems. Companies and research organizations have developed prototypes. The U.S. allocated $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 program 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 kilometers (2,848 mi) across America in 1995, 4,501 kilometers (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 kilometers (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, D.C., allowed the testing of automated cars on public roads. In 2017, Audi stated that its latest A8 would be automated at speeds of up to 60 kilometres per hour (37 mph) using its “Audi AI.” The driver would not have to do safety checks such as frequently gripping the steering wheel. The Audi A8 was claimed to be the first production car to reach level 3 automated driving, and Audi would be the first manufacturer to use laser scanners in addition to cameras and ultrasonic sensors for their system. In November 2017, Waymo announced that it had begun testing driverless cars without a safety driver in the driver position; however, there is still an employee in the car. In July 2018, Waymo announced that its test vehicles had traveled in automated mode for over 8,000,000 miles (13,000,000 km), increasing by 1,000,000 miles (1,600,000 kilometers) per month. Terminology There is some inconsistency in terminology used in the self-driving car industry. Various organizations have proposed to define an accurate and consistent vocabulary. 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 at or above level 3 (or to any vehicle equipped with such an ADS).” Words definition and safety considerations Modern vehicles provide partly automated features such as keeping the car within its lane, speed controls or emergency braking. Nonetheless, differences remain between a fully autonomous self-driving car on one hand and driver assistance technologies on the other hand. According to the BBC, confusion between those concepts leads to deaths. 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’ means a vehicle can drive itself, when they still rely on the driver to ensure safety. Technology alone still is not able to drive the car. When some car makers suggest or claim vehicles are self-driving, when they are only partly automated, drivers risk becoming excessively confident, leading to crashes, while fully self-driving cars are still a long way off in the UK. 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. 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. Wood et al. (2012) wrote, “This Article generally uses the term ‘autonomous,’ instead of the term ‘automated.’ ” The term “autonomous” was chosen “because it is the term that is currently in more widespread use (and thus is more familiar to the general public). However, the latter term is arguably more accurate. ‘Automated’ connotes control or operation by a machine, while ‘autonomous’ connotes acting alone or independently. Most of the vehicle concepts (that we are currently aware of) have a person in the driver’s seat, utilize a communication connection to the Cloud or other vehicles, and do not independently select either destinations or routes for reaching them. Thus, the term ‘automated’ would more accurately describe these vehicle concepts.” As of 2017, most commercial projects focused on automated vehicles that did not communicate with other vehicles or with an enveloping management regime. Put in the words of one Nissan engineer, “A truly autonomous car would be one where you request it to take you to work and it decides to go to the beach instead.” EuroNCAP defines autonomous in “Autonomous Emergency Braking” as: “the system acts independently of the driver to avoid or mitigate the accident.” which implies the autonomous system is not the driver. Autonomous versus cooperative To make a car travel without any driver embedded within the vehicle some system makers used a remote driver. But according to SAE J3016, some driving automation systems may indeed be autonomous if they perform all of their functions independently and self-sufficiently, but if they depend on communication and/or cooperation with outside entities, they should be considered cooperative rather than autonomous. Self-driving car Techemergence says. “Self-driving” is a rather vague term with a vague meaning —?Techemergence PC mag definition is: A computer-controlled car that drives itself. Also called an “autonomous vehicle” and “driverless car,” self-driving cars date back to the 1939 New York World’s Fair when General Motors predicted the development of self-driving, radio-controlled electric cars.

UC SUSA

Definition is: Self-driving vehicles are cars or trucks in which human drivers are never required to take control to safely operate the vehicle. Also known as autonomous or “driverless” cars, they combine sensors and software to control, navigate, and drive the vehicle. Currently, there are no legally operating, fully-autonomous vehicles in the United States.

NHTSA

definition is: These self-driving vehicles ultimately will integrate onto U.S. roadways by progressing through six levels of driver assistance technology advancements in the coming years. This includes everything from no automation (where a fully engaged driver is required at all times), to full autonomy (where an automated vehicle operates independently, without a human driver). Classification A classification system based on six different levels (ranging from fully manual to fully automated systems) was published in 2014 by SAE International, an automotive standardization body, as J3016, Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. This classification system is based on the amount of driver intervention and attentiveness required, rather than the vehicle capabilities, although these are very loosely related. In the United States in 2013,

National Highway

Traffic Safety Administration (NHTSA) released a formal classification system, but abandoned this system in favor of the SAE standard in 2016. Also in 2016, SAE updated its classification, called J3016_201609. Levels of driving automation In SAE’s automation level definitions, “driving mode” means “a type of driving scenario with characteristic dynamic driving task requirements (e.g., expressway merging, high speed cruising, low speed traffic jam, closed-campus operations, etc.)” Level 0: 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 Adaptive Cruise Control (ACC)

where the driver controls steering and the automated system controls speed; 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. 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. The shorthand “hands off” is not meant to be taken literally. In fact, contact between hand and wheel is often mandatory during SAE 2 driving, to confirm that the driver is ready to intervene. 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 movie. 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. As an example, the 2018

Audi A8 Luxury

Sedan was the first commercial car to claim to be capable of level 3 self-driving. This particular car has a so-called Traffic Jam Pilot. When activated by the human driver, the car takes full control of all aspects of driving in slow-moving traffic at up to 60 kilometers per hour (37 mph). The function works only on highways with a physical barrier separating one stream of traffic from oncoming traffic. Level 4 (“mind off”): As level 3, but no driver attention is ever required for safety, i.e. the driver may safely go to sleep or leave the driver’s seat. Self-driving is supported only in limited spatial areas (geofenced) or under special circumstances, like traffic jams. Outside of these areas or circumstances, the vehicle must be able to safely abort the trip, i.e. park the car, if the driver does not retake control. Level 5 (“steering wheel optional”): No human intervention is required at all. An example would be a robotic taxi. In the formal SAE definition below, note in particular what happens in the shift from SAE 2 to SAE 3: the human driver no longer has to monitor the environment. This is the final aspect of the “dynamic driving task” that is now passed over from the human to the automated system. At SAE 3, the human driver still has the responsibility to intervene when asked to do so by the automated system. At SAE 4 the human driver is relieved of that responsibility and at SAE 5 the automated system will never need to ask for an intervention. Legal definition In the district of Columbia (DC) code, “Autonomous vehicle” means a vehicle capable of navigating

District roadways

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.

Technical challenges

The challenge for driverless car designers is to produce control systems capable of analyzing sensory data in order to provide accurate detection of other vehicles and the road ahead. 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. Typical sensors include Lidar, stereo vision, GPS and IMU. Udacity is developing an open-source software stack. 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. Driverless vehicles require some form of machine vision for the purpose of visual object recognition. Automated cars are being developed with deep neural networks, a type of deep learning architecture with many computational stages, or levels, in which neurons are simulated from the environment that activate the network. The neural network depends on an extensive amount of data extracted from real-life driving scenarios, enabling the neural network to “learn” how to execute the best course of action. In May 2018, researchers from 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. Human factor challenges Alongside the many technical challenges that autonomous cars face, there exist many human and social factors that may impede upon the wider uptake of the technology. As things become more automated, the human users need to have trust in the automation, which can be a challenge in itself.

Testing

Testing vehicles with varying degrees of automation can be done physically, in closed environments, on public roads (where permitted, typically with a license or permit or adhering to a specific set of operating principles) or virtually, i.e. in computer simulations. When driven on public roads, automated vehicles require a person to monitor their proper operation and “take over” when needed. Apple is currently testing self-driven cars, and has increased the number of test vehicles from 3 to 27 in January 2018, and to 45 in March 2018. One way to assess the progress of automated vehicles is to compute the average distance driven between “disengagements”, when the automated system is turned off, typically by a human driver. In 2017,

Waymo

reported 63 disengagements over 352,545 miles (567,366 km) of testing, or 5,596 miles (9,006 km) on average, the highest among companies reporting such figures. Waymo also traveled more distance in total than any other. Their 2017 rate of 0.18 disengagements per 1,000 miles (1,600 km) was an improvement from 0.2 disengagements per 1,000 miles (1,600 km) in 2016 and 0.8 in 2015. In March, 2017, Uber reported an average of 0.67 miles (1.08 km) per disengagement. In the final three months of 2017, Cruise Automation (now owned by GM) averaged 5,224 miles (8,407 km) per disruption over 62,689 miles (100,888 km). In July 2018, the first electric driverless racing car “Robocar” completed 1.8 kilometers track, using its navigation system and artificial intelligence.
Self-driving cartesting results. Source: Wang, Brian (25 March 2018). “Uber’ self-driving system was still 400 times worse [than] Waymo in 2018 on key distance intervention metric”. NextBigFuture.com. Retrieved 25 March 2018.
Fields of application Automated trucks Several companies are said to be testing automated technology in semi trucks. Otto, a self-driving trucking company that was acquired by Uber in August 2016, demonstrated their trucks on the highway before being acquired. In May 2017, San Francisco-based startup Embark announced a partnership with truck manufacturer Peterbilt to test and deploy automated technology in Peterbilt’s vehicles. Waymo has also said to be testing automated technology in trucks, however no timeline has been given for the project. In March 2018, Starsky Robotics, the San Francisco-based automated truck company, completed a 7-mile (11 km) fully driverless trip in Florida without a single human in the truck. Starsky Robotics became the first player in the self-driving truck game to drive in fully automated mode on a public road without a person in the cab. In Europe, the truck Platooning is considered with the Safe Road Trains for the Environment approach.Vehicular automation also covers other kinds of vehicles such as Buses, Trains, Trucks. Lockheed Martin with funding from the U.S. Army developed an automated truck convoying system that uses a lead truck operated by a human driver with a number of trucks following autonomously. Developed as part of the Army’s Autonomous

Mobility Applique System (AMAS),

the system consists of an automated driving package that has been installed on more than nine types of vehicles and has completed more than 55,000 hours of driving at speeds up to 64 kilometres per hour (40 mph) as of 2014. As of 2017 the Army was planning to field 100-200 trucks as part of a rapid-fielding program. Transport systems 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

Pprovider 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. In China, 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. Those minibuses should be at level 4, that is driverless in closed roads. Potential advantages Safety Driving safety experts predict that once driverless technology has been fully developed, traffic collisions (and resulting deaths and injuries and costs), caused by human error, such as delayed reaction time, tailgating, rubbernecking, and other forms of distracted or aggressive driving should be substantially reduced.

Consulting firm McKinsey & Company

estimated that widespread use of autonomous vehicles could “eliminate 90% of all auto accidents in the United States, prevent up to US$190 billion in damages and health-costs annually and save thousands of lives.” According to motorist website “TheDrive.com” operated by Time magazine, none of the driving safety experts they were able to contact were able to rank driving under an autopilot system at the time (2017) as having achieved a greater level of safety than traditional fully hands-on driving, so the degree to which these benefits asserted by proponents will manifest in practice cannot be assessed. Confounding factors that could reduce the net effect on safety may include unexpected interactions between humans and partly or fully automated vehicles, or between different types of vehicle system; complications at the boundaries of functionality at each automation level (such as handover when the vehicle reaches the limit of its capacity); the effect of the bugs and flaws that inevitably occur in complex interdependent software systems; sensor or data shortcomings; and successful compromise by malicious interveners. Welfare Automated cars could reduce labor costs; relieve travelers from driving and navigation chores, thereby replacing behind-the-wheel commuting hours with more time for leisure or work; and also would lift constraints on occupant ability to drive, distracted and texting while driving, intoxicated, prone to seizures, or otherwise impaired. For the young, the elderly, people with disabilities, and low-income citizens, automated cars could provide enhanced mobility. The removal of the steering wheel—along with the remaining driver interface and the requirement for any occupant to assume a forward-facing position—would give the interior of the cabin greater ergonomic flexibility. Large vehicles, such as motorhomes, would attain appreciably enhanced ease of use. Traffic Additional advantages could include higher speed limits; smoother rides; and increased roadway capacity; and minimized traffic congestion, due to decreased need for safety gaps and higher speeds.

Currently, maximum controlled-access

highway throughput or capacity according to the U.S. Highway Capacity Manual is about 2,200 passenger vehicles per hour per lane, with about 5% of the available road space is taken up by cars. One study estimated that automated cars could increase capacity by 273% (~8,200 cars per hour per lane). The study also estimated that with 100% connected vehicles using vehicle-to-vehicle communication, capacity could reach 12,000 passenger vehicles per hour (up 445% from 2,200 pc/h per lane) traveling safely at 120 km/h (75 mph) with a following gap of about 6 m (20 ft) of each other. Currently, at highway speeds drivers keep between 40 to 50 m (130 to 160 ft) away from the car in front. These increases in highway capacity could have a significant impact in traffic congestion, particularly in urban areas, and even effectively end highway congestion in some places. The ability for authorities to manage traffic flow would increase, given the extra data and driving behavior predictability combined with less need for traffic police and even road signage. Lower costs Safer driving is expected to reduce the costs of vehicle insurance. Reduced traffic congestion and the improvements in traffic flow due to widespread use of automated cars will also translate into better fuel efficiency. Additionally, self-driving cars will be able to accelerate and brake more efficiently, meaning higher fuel economy from reducing wasted energy typically associated with inefficient changes to speed (energy typically lost due to friction, in the form of heat and sound). Parking space Manually driven vehicles are reported to be used only 4-5% time, and being parked and unused for the remaining 95-96% of the time. Autonomous vehicles could, on the other hand, be used continuously after it has reached its destination. This could dramatically reduce the need for parking space.

For example, in Los Angeles,

14% of the land is used for parking alone, equivalent to some 17,020,594 square meters. This combined with the potential reduced need for road space due to improved traffic flow, could free up tremendous amounts of land in urban areas, which could then be used for parks, recreational areas, buildings, among other uses; making cities more livable. Related effects By reducing the (labor and other) cost of mobility as a service, automated cars could reduce the number of cars that are individually owned, replaced by taxi/pooling and other car sharing services. This would also dramatically reduce the size of the automotive production industry, with corresponding environmental and economic effects. Assuming the increased efficiency is not fully offset by increases in demand, more efficient traffic flow could free roadway space for other uses such as better support for pedestrians and cyclists. The vehicles’ increased awareness could aid the police by reporting on illegal passenger behavior, while possibly enabling other crimes, such as deliberately crashing into another vehicle or a pedestrian.
However, this may also lead to much expanded mass surveillance if there is wide access granted to third parties to the large data sets generated. The future of passenger rail transport in the era of automated cars is not clear.

Potential limits or obstacles

The sort of hoped-for potential benefits from increased vehicle automation described may be limited by foreseeable challenges, such as disputes over liability (will each company operating a vehicle accept that they are its “driver” and thus responsible for what their car does, or will some try to project this liability onto others who are not in control?), 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 having to forfeit control of their cars, concerns about the safety of driverless in practice, 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 (at the same time as many other alternate blue collar occupations may be undermined by automation), 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 (making anonymous travel difficult), 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 very high-quality specialized 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, discrepancy between people’s beliefs of the necessary government intervention may cause a delay in accepting automated cars on the road. Whether the public desires no change in existing laws, federal regulation, or another solution; the framework of regulation will likely result in differences of opinion, 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 programmers 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. Potential disadvantages A direct impact of widespread adoption of automated vehicles is the loss of driving-related jobs in the road transport industry. There could be resistance from professional drivers and unions who are threatened by job losses. In addition, there could be job losses in public transit services and crash repair shops. The automobile insurance industry might suffer as the technology makes certain aspects of these occupations obsolete. A frequently cited paper by Michael

Osborne and Carl Benedikt

Frey found that automated cars would make many jobs redundant. Privacy could be an issue when having the vehicle’s location and position integrated into an interface in which other people have access to. In addition, there is the risk of automotive hacking through the sharing of information through V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols. There is also the risk of terrorist attacks. Self-driving cars could potentially be loaded with explosives and used as bombs. The lack of stressful driving, more productive time during the trip, and the potential savings in travel time and cost could become an incentive to live far away from cities, where land is cheaper, and work in the city’s core, thus increasing travel distances and inducing more urban sprawl, more fuel consumption and an increase in the carbon footprint of urban travel. There is also the risk that traffic congestion might increase, rather than decrease. Appropriate public policies and regulations, such as zoning, pricing, and urban design are required to avoid the negative impacts of increased suburbanization and longer distance travel. Some believe that once automation in vehicles reaches higher levels and becomes reliable, drivers will pay less attention to the road. Research shows that drivers in automated cars react later when they have to intervene in a critical situation, compared to if they were driving manually. Depending on the capabilities of automated vehicles and the frequency with which human intervention is needed, this may counteract any increase in safety, as compared to all-human driving, that may be delivered by other factors. Ethical and moral reasoning come into consideration when programming the software that decides what action the car takes in an unavoidable crash; whether the automated car will crash into a bus, potentially killing people inside; or swerve elsewhere, potentially killing its own passengers or nearby pedestrians. A question that programmers of AI systems find difficult to answer (as do ordinary people, and ethicists) is “what decision should the car make that causes the ‘smallest’ damage to people’s lives?” The ethics of automated vehicles are still being articulated, and may lead to controversy. They may also require closer consideration of the variability, context-dependency, complexity and non-deterministic nature of human ethics. Different human drivers make various ethical decisions when driving, such as avoiding harm to themselves, or putting themselves at risk to protect others. These decisions range from rare extremes such as self-sacrifice or criminal negligence, to routine decisions good enough to keep the traffic flowing but bad enough to cause accidents, road rage and stress. Human thought and reaction time may sometimes be too slow to detect the risk of an upcoming fatal crash, think through the ethical implications of the available options, or take an action to implement an ethical choice. Whether a particular automated vehicle’s capacity to correctly detect an upcoming risk, analyze the options or choose a ‘good’ option from among bad choices would be as good or better than a particular human’s may be difficult to predict or assess.
This difficulty may be in part because the level of automated vehicle system understanding of the ethical issues at play in a given road scenario, sensed for an instant from out of a continuous stream of synthetic physical predictions of the near future, and dependent on layers of pattern recognition and situational intelligence, may be opaque to human inspection because of its origins in probabilistic machine learning rather than a simple, plain English ‘human values’ logic of parsable rules. The depth of understanding, predictive power and ethical sophistication needed will be hard to implement, and even harder to test or assess. The scale of this challenge may have other effects. There may be few entities able to marshal the resources and AI capacity necessary to meet it, as well as the capital necessary to take an automated vehicle system to market and sustain it operationally for the life of a vehicle, and the legal and ‘government affairs’ capacity to deal with the potential for liability for a significant proportion of traffic accidents. This may have the effect of narrowing the number of different system operators, and eroding the presently quite diverse global vehicle market down to a small number of system suppliers.
Introduction Technology Readiness Levels (TRLs) are a systematic metric/measurement system that supports assessments of the maturity of a particular technology and the consistent comparison of maturity between different types of technology. The TRL approach has been used on-and-off in NASA space technology planning for many years and was recently incorporated in the NASA Management Instruction (NMI 7100) addressing integrated technology planning at NASA. Figure 1 (attached) provides a summary view of the technology maturation process model for

NASA space activities

for which the TRL’s were originally conceived; other process models may be used. However, to be most useful the general model must include: (a) ‘basic’ research in new technologies and concepts (targeting identified goals, but not necessary specific systems), (b) focused technology development addressing specific technologies for one or more potential identified applications, (c) technology development and demonstration for each specific application before the beginning of full system development of that application, (d) system development (through first unit fabrication), and (e) system ‘launch’ and operations. Technology Readiness Levels Summary TRL 1 Basic principles observed and reported TRL 2 Technology concept and/or application formulated TRL 3 Analytical and experimental critical function and/or characteristic proof-ofconcept TRL 4 Component and/or breadboard validation in laboratory environment TRL 5 Component and/or breadboard validation in relevant environment TRL 6 System/subsystem model or prototype demonstration in a relevant environment (ground or space) TRL 7 System prototype demonstration in a space environment TRL 8 Actual system completed and “flight qualified” through test and demonstration (ground or space) TRL 9 Actual system “flight proven” through successful mission operations 028 Discussion of Each Level The following paragraphs provide a descriptive discussion of each technology readiness level, including an example of the type of activities that would characterize each TRL. TRL 1 Basic principles observed and reported This is the lowest “level” of technology maturation. At this level, scientific research begins to be translated into applied research and development. Examples might include studies of basic properties of materials (e.g., tensile strength as a function of temperature for a new fiber). Cost to Achieve: Very Low ‘Unique’ Cost (investment cost is borne by scientific research programs) TRL 2 Technology concept and/or application formulated Once basic physical principles are observed, then at the next level of maturation, practical applications of those characteristics can be ‘invented’ or identified. For example, following the observation of high critical temperature (Htc) superconductivity, potential applications of the new material for thin film devices (e.g., SIS mixers) and in instrument systems (e.g., telescope sensors) can be defined. At this level, the application is still speculative: there is not experimental proof or detailed analysis to support the conjecture. Cost to Achieve: Very Low ‘Unique’ Cost (investment cost is borne by scientific research programs) TRL 3 Analytical and experimental critical function and/or characteristic proof-of-concept At this step in the maturation process, active research and development (R&D) is initiated. This must include both analytical studies to set the technology into an appropriate context and laboratory-based studies to physically validate that the analytical predictions are correct. These studies and experiments should constitute “proof-of-concept” validation of the applications/concepts formulated at TRL 2. For example, a concept for High

Energy Density Matter (HEDM)

propulsion might depend on slush or super-cooled hydrogen as a propellant: TRL 3 might be attained when the concept-enabling phase/temperature/pressure for the fluid was achieved in a laboratory. Cost to Achieve: Low ‘Unique’ Cost (technology specific) 028 TRL 4 Component and/or breadboard validation in laboratory environment Following successful “proof-of-concept” work, basic technological elements must be integrated to establish that the “pieces” will work together to achieve concept-enabling levels of performance for a component and/or breadboard. This validation must devised to support the concept that was formulated earlier, and should also be consistent with the requirements of potential system applications. The validation is relatively “low-fidelity” compared to the eventual system: it could be composed of ad hoc discrete components in a laboratory. For example, a TRL 4 demonstration of a new ‘fuzzy logic’ approach to avionics might consist of testing the algorithms in a partially computer-based, partially bench-top component (e.g., fiber optic gyros) demonstration in a controls lab using simulated vehicle inputs. Cost to Achieve: Low-to-moderate ‘Unique’ Cost (investment will be technology specific, but probably several factors greater than investment required for TRL 3) TRL 5 Component and/or breadboard validation in relevant environment At this, the fidelity of the component and/or breadboard being tested has to increase significantly. The basic technological elements must be integrated with reasonably realistic supporting elements so that the total applications (component-level, sub-system level, or system-level) can be tested in a ‘simulated’ or somewhat realistic environment. From oneto-several new technologies might be involved in the demonstration. For example, a new type of solar photovoltaic material promising higher efficiencies would at this level be used in an actual fabricated solar array ‘blanket’ that would be integrated with power supplies, supporting structure, etc., and tested in a thermal vacuum chamber with solar simulation capability. Cost to Achieve: Moderate ‘Unique’ Cost (investment cost will be technology dependent, but likely to be several factors greater that cost to achieve TRL 4) TRL 6 System/subsystem model or prototype demonstration in a relevant environment (ground or space) A major step in the level of fidelity of the technology demonstration follows the completion of TRL 5. At TRL 6, a representative model or prototype system or system — which would go well beyond ad hoc, ‘patch-cord’ or discrete component level breadboarding — would be tested in a relevant environment. At this level, if the only ‘relevant environment’ is the environment of space, then the model/prototype must be demonstrated in space. Of 028 course, the demonstration should be successful to represent a true TRL 6. Not all technologies will undergo a TRL 6 demonstration: at this point the maturation step is driven more by assuring management confidence than by R&D requirements. The demonstration might represent an actual system application, or it might only be similar to the planned application, but using the same technologies. At this level, several-to-many new technologies might be integrated into the demonstration. For example, a innovative approach to high temperature/low mass radiators, involving liquid droplets and composite materials, would be demonstrated to TRL 6 by actually flying a working, sub-scale (but scaleable) model of the system on a Space Shuttle or International Space Station ‘pallet’. In this example, the reason space is the ‘relevant’ environment is that microgravity plus vacuum plus thermal environment effects will dictate the success/failure of the system — and the only way to validate the technology is in space. Cost to Achieve: Technology and demonstration specific; a fraction of TRL 7 if on ground; nearly the same if space is required TRL 7 System prototype demonstration in a space environment TRL 7 is a significant step beyond TRL 6, requiring an actual system prototype demonstration in a space environment. It has not always been implemented in the past. In this case, the prototype should be near or at the scale of the planned operational system and the demonstration must take place in space. The driving purposes for achieving this level of maturity are to assure system engineering and development management confidence (more than for purposes of technology R&D). Therefore, the demonstration must be of a prototype of that application. Not all technologies in all systems will go to this level. TRL 7 would normally only be performed in cases where the technology and/or subsystem application is mission critical and relatively high risk. Example: the Mars Pathfinder Rover is a TRL 7 technology demonstration for future Mars micro-rovers based on that system design. Example: X-vehicles are TRL 7, as are the demonstration projects planned in the New Millennium spacecraft program. Cost to Achieve: Technology and demonstration specific, but a significant fraction of the cost of TRL 8 (investment = “Phase C/D to TFU” for demonstration system) TRL 8 Actual system completed and “flight qualified” through test and demonstration (ground or space) By definition, all technologies being applied in actual systems go through TRL 8. In almost all cases, this level is the end of true ‘system development’ for most technology elements. Example: this would include DDT&E through Theoretical First Unit (TFU) for a new reusable launch vehicle. This might include integration of new technology into an existing system. Example: loading and testing successfully a new control algorithm into the onboard computer on Hubble Space Telescope while in orbit. Cost to Achieve: Mission specific; typically highest unique cost for a new technology (investment = “Phase C/D to TFU” for actual system) 028 TRL 9 Actual system “flight proven” through successful mission operations By definition, all technologies being applied in actual systems go through TRL 9. In almost all cases, the end of last ‘bug fixing’ aspects of true ‘system development’. For example, small fixes/changes to address problems found following launch (through ‘30 days’ or some related date). This might include integration of new technology into an existing system (such operating a new artificial intelligence tool into operational mission control at JSC). This TRL does not include planned product improvement of ongoing or reusable systems. For example, a new engine for an existing RLV would not start at TRL 9: such
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