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2021 | OriginalPaper | Buchkapitel

Different Liability Regimes for Autonomous Vehicles: One Preferable Above the Other?

verfasst von : Steven Van Uytsel

Erschienen in: Autonomous Vehicles

Verlag: Springer Nature Singapore

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Abstract

Autonomous vehicles are said to bring safety to the roads. Machines are expected not to make the same driving mistakes as humans. Indeed, machines will not drive intoxicated or get too tired to drive. However, the application of adversarial machine learning to autonomous vehicles has shown that the reaction of these vehicles to altered traffic signs may be the cause of unpredictable reactions. Rather than stopping in front of a vandalized stop sign, the autonomous vehicle may speed. This may lead to accidents. Therefore, scholars have developed various liability and compensation schemes to deal with accidents by autonomous vehicles. The following liability and compensation schemes have been suggested to deal with the civil liability of accidents of autonomous vehicles: operator liability, product liability, strict liability, no-fault compensation, and negligence. Each of these schemes are judged against victim and innovation friendliness. The former is being framed as easiness to obtain compensation, while the latter is understood as a burden on the industry. Operator liability, strict liability and no-fault compensation are considered as victim friendly. Product liability and negligence put a burden on the victim to prove either a defect of the product or a fault of the manufacturer. Only by shifting the burden of proof to the manufacturer would these systems be made victim friendly. In terms of innovation, the situation is not obvious. Operator liability, product liability and negligence make it difficult for a manufacturer to anticipate the size of the financial burden in case of an accident. This would be different with strict liability and no-fault compensation. Much of the discussion above is framed in relation to vehicles that are operating autonomously on their own. There is, however, more and more research on infrastructure enabled autonomy. In system, autonomous vehicles will be operating in connection with road side units, cloud services, and other traffic participants. As this will bring together products, services, and behavior, a mix of different liability regimes will make it difficult for the victim to obtain compensation. Therefore, a one-stop window may facilitate obtaining compensation. No-fault compensation could be ideal.

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Vorheriges Kapitel Testing Autonomous Vehicles on Public Roads: Facilitated by a Series of Alternative, Often Soft, Legal Instruments

Nächstes Kapitel Who is Liable for the UBER Self-Driving Crash? Analysis of the Liability Allocation and the Regulatory Model for Autonomous Vehicles

Woods (2019), p. 80.

Kalra and Groves (2017), p. 1.

Carp (2018), p. 88.

Woods (2019), p. 81.

See, e.g., Channon et al. (2019); Evas (2018); Lim (2018); Pearl (2018); Schellekens (2018); Crane, Logue and Pilz (2017); Geistfeld (2017); Maurer et al. (2015); Schroll (2015); Ravid (2014).

See, e.g., Pearl (2017), Karner (2019), p. 121.

See, e.g., Abbott (2018); Chagal-Feferkorn (2018a).

See, e.g., Amato (2019); Borghetti (2019); Koch (2019).

See, e.g., Spindler (2019), pp. 136–138.

See, e.g., Borges (2019); Schellekens (2018); Pearl (2018), Schroll (2015).

Society of Automative Engineers (2018); See also Channon et al. (2019), pp. 4–5. Level 3, which is characterized as conditional automation, enables the vehicle to monitor the driving environment under normal conditions. The driver is required, however, to control the system and take over whenever the system requests to do so. Lim (2018), pp. 4–5. The control by the driver is not required anymore for Level 4 vehicles. Under this high automation mode, the vehicle is able to interpret the driving environment and take the decisions necessary for driving under certain conditions. The driver can still take control when he desires or when the system fails due to, for example road works, road diversions, or off-road driving. Pearl (2017), pp. 28–29. Level 5 will offer full automation. Fuchs (2019). This means that the system can drive by itself under all circumstances. The system will even be able to drive in “unpredictable or changing physical environments.” Lim (2018), p. 5. Many predictions have been made on when autonomous vehicles will hit the road (for a summary of these predictions, see Gurney (2013), pp. 248–251 (especially footnotes 8–15); Geistfeld (2017), p. 1615. However, among the vehicles currently marketed to the public, Tesla is offering the most advanced autonomous vehicles, see Lim (2018). For an overview of the historical development, see Kellerman (2018), pp. 106–109. Audi is said to start commercialize a Level 3 vehicle in 2019, see Audi MediaCenter (2017); See also Abe (2018), p. 3. Hannah YeeFen Lim purports that in early 2018 the race for a Level 5 vehicle is being dominated by Google’s Waymo subsidiary and General Motors’ Cruise Automation subsidiary, see Lim (2018), p. 5. Ryosuke Abe reports that Nissan will test autonomously driving taxis in the high-density city of Yokohama and that Toyota would like to provide autonomous vehicles at the sites built for the Tokyo Olympics in 2020. No statement is made on the level of these vehicles, see Abe (2018), p. 3.

Pearl (2017), p. 49.

Goto (2019), p. 2.

Goto (2019), p. 3.

Pearl (2017), p. 49.

Pearl (2017), p. 57.

See Lhosse et al. (2019). See also, e.g., Channon et al. (2019), pp. 34–46; Evas (2018); Lim (2018), pp. 106–109; Crane, Logue and Pilz (2017); Geistfeld (2017), p. 1619 and pp. 1634–1647. Maurer et al. (2015), pp. 553–570.

McCormick (2019), p. 37.

Wagner (2019), p. 41. Koch (2019), p. 105.

Wagner (2019), p. 47.

Koch (2019), p. 106.

Van Uytsel and Vargas (2020), p. 193.

Lengthy court proceedings may be the consequence of using product liability law for the traffic accidents. See, e.g., Chagal-Feferkorn (2018a), pp. 17–22; Pearl (2018), p. 19; Ravid (2014), p. 200.

Wagner (2019), 43 (also noting that the German Federal Court of Justice favors the so-called risk-utility test).

Lim (2018), pp. 82–98.

Lim (2018), pp. 92–98.

Wagner (2019), p. 44; Geistfeld (2017), pp. 1645–1647.

Wagner (2019), p. 45.

Surden (2019).

Lohmann (2016), pp. 337–338.

See, e.g., Chagal-Feferkorn (2018a), pp. 17–22; Pearl (2018), p. 19; Ravid (2014), p. 200.

Shavell (2019), p. 2.

Davola (2018), p. 603 (This liability scheme creates a system in “in which they [car manufacturers] are always responsible for harms caused by the vehicles they produce. This form of liability departs from any notion of fault” and defect.).

Spindler (2019), p. 136.

Spindler (2019), p. 136. In a somewhat different framing, see Zech (2019), p. 197 arguing that strict liability incentivizes only the marketing of products of which one is sure that they are safe.

See chapter Learning systems under attack—Adversarial attacks, defenses and beyond [this volume].

This is sometimes equaled with how law deals with animals and their unpredictable behavior. Dufy and Hopkins (2013).

Spindler (2019), p. 139. On the complexity of causality, Martin-Casals (2019).

Schellekens (2018), p. 319.

See, e.g., Engelhard and de Bruin (2017), pp. 111–115; Schellekens (2018). We read that this can be realized by giving the self-learning robot an electronic legal personality, see, e.g., Herrmann et al. (2018), p. 238. In the United Kingdom, an insurance for the vehicle driving autonomously is elaborated. See Channon (2019), pp. 22–33. Similar to this no-fault compensation scheme is the victim compensation fund. See, e.g., Pearl (2018); Schroll (2015). Victim compensation funds have also been tested in the United States, mainly in the aftermath of big disasters such as the September 11 attacks and the Deep Water Horizon oil spill. Pearl (2018), p. 4. This kind of fund also does not decide on a fault. However, unlike the non-fault compensation scheme, the existence of a fund would not exclude the application of normal tort law. Pearl (2018), p. 22. If the victim does not opt for the application of the fund, tort law will be by default the basis to judge the liability.

Schellekens (2018), p. 320.

Schellekens (2018), p. 319.

Schellekens (2018), pp. 324–327. Compare Pearl (2018), pp. 29–36 for a discussion on the different forms of contribution.

Schellekens (2018), pp. 324–325.

Pearl (2018), pp. 31–32 and p. 35.

Schellekens (2018), pp. 325. According to Pearl, this would also apply to the victim compensation fund. See Pearl (2018), pp. 28–37.

On the insurers, see Schellekens (2018), pp. 325–326.

Pearl (2018), p. 33.

Pearl (2018), p. 33; Schroll suggests one run by the Federal Government. State-run and private insurance are alternatives. See Schroll (2015), pp. 822–827.

Pearl (2018), p. 35.

Spindler (2019), p. 137.

Pearl (2018), p. 35.

Spindler (2019), p. 136; Pearl (2018), p. 35.

Spindler (2019), p. 130.

Lim (2018), p. 23.

Marchant and Lindor (2012), p. 1323.

See chapter Sensors for automated driving [this volume].

There are various types of machine learning. Supervised machine learning teaches the system, through labels attached to the correct response, what it has to do in a given situation. For example, applying the brakes is labelled as the correct action when a traffic light is red. Unsupervised machine learning lets the system detect a pattern in a given set of data. For example, if the data shows a pattern of stopping in front of a red light, the algorithm will learn that the rule is to stop at a red light. Reinforced machine learning teaches the machine through trial and error which is the best action, which is called policy, to take. A machine will learn this through rewards. Lastly, deep learning is machine learning that is structured in layers that are connected to each other (resembling a neural network) and that processes the calculations.

Lim (2018), pp. 92–98.

See Abbott (2018); Chagal-Feferkorn (2018a).

European Parliament (2017), Section AB.

European Parliament (2017), Section AD.

Abbott (2018), pp. 23–26.

Chagal-Feferkorn (2018b), pp. 17–25.

Chagal-Feferkorn (2018a, 2018b).

European Parliament (2017), Sect. 59 (f).

See, e.g., European Parliament (2017), Section AA (explaining autonomy as “ability to take decisions and implement them in the outside world, independently of external control or influence; whereas this autonomy is of a purely technological nature and its degree depends on how sophisticated a robot’s interaction with its environment has been designed to be”) and Sect. 56 (indicating the level of autonomy should determine the level of liability); Millar and Kerr (2016), p. 123 (arguing that outperforming human experts render robots autonomous).

Abbott (2018), p. 23 (ability to replace a human actor, which is expressed in the idea that a robot is given a task which it can complete by itself).

Chagal-Feferkorn (2018a), pp. 90–107.

Abbott (2018), p. 31.

Abbott (2018), p. 41.

Chagal-Feferkorn (2020), p. 43.

Abbott (2018), p. 30.

Abbott (2018), p. 31.

European Parliament (2017).

Schellekens (2018), p. 315.

Some jurisdictions have created absolute liability of the operator towards weaker participants in traffic.

Woods (2019), p. 80.

Schellekens (2018), p. 316.

Carp (2018), p. 88.

Van Uytsel and Vargas (2020), p. 179.

Schellekens (2018), p. 315.

Wagner (2019), pp. 30–31.

It is held, though, that proving only a defect should still be less burdensome as careless behavior of a manufacturer. See Abbott (2018), p. 21.

Davola (2018), p. 604.

Schellekens (2018), p. 326.

Geistfeld (2017), p. 1629.

Schellekens (2018), pp. 325–326.

100

Pearl (2018), p. 23; Schellekens (2018), pp. 325.

101

Schellekens (2018), p. 328. The idea of a fund is supported by Davola (2018), and Abraham and Rabin (2019). The latter two disagree on the funding. The former contemplates that the government should intervene with funding, while the latter impose manufacturer enterprise responsibility and thus require funding from the manufacturers.

102

Pearl states that a contribution to the fund by all manufacturers in an industry is unprecedented in the United States. Therefore, it may not be a viable solution. Pearl (2018), pp. 36–37.

103

The need to make it predictable has been stressed by several scholars. See, e.g., Pearl (2018), pp. 20–21; Schellekens (2018), p. 329; Smith (2016), p. 6.

104

Lim (2018), p. 23.

105

Lim (2018), p. 23.

106

Lim (2018), pp. 94–98.

107

Wagner (2019), p. 45.

108

Gopalswamy and Rathinam (2018), p. 1.

109

Available at: https://mcity.umich.edu/#. Accessed 30 June 2020.

110

Available at: https://www.abdynamics.com/ja/blog/2019/case-study-autonomous-vehicle-testing-in-china. Accessed 30 June 2020.

111

Available at: https://cast.tamu.edu/. Accessed 30 June 2020.

112

Available at: https://eng.unimelb.edu.au/industry/aimes. Accessed 30 June 2020.

113

Glancy (2015), p. 642.

114

Glancy (2014), pp. 1627–1640.

115

Duplechin (2018), pp. 817–830; Glancy (2014), pp. 1643–1647.

116

Glancy (2014), p. 1644.

117

Glancy (2015), p. 660 (making an indication that negligence will complement product liability in case of connected vehicles).

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Titel: Different Liability Regimes for Autonomous Vehicles: One Preferable Above the Other?
verfasst von: Steven Van Uytsel
Verlag: Springer Nature Singapore
Buch: Autonomous Vehicles
Print ISBN: 978-981-15-9254-6

Electronic ISBN: 978-981-15-9255-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-15-9255-3_4

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