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Self-Driving Vehicles-an Ethical Overview

Abstract

The introduction of self-driving vehicles gives rise to a large number of ethical issues that go beyond the common, extremely narrow, focus on improbable dilemma-like scenarios. This article provides a broad overview of realistic ethical issues related to self-driving vehicles. Some of the major topics covered are as follows: Strong opinions for and against driverless cars may give rise to severe social and political conflicts. A low tolerance for accidents caused by driverless vehicles may delay the introduction of driverless systems that would substantially reduce the risks. Trade-offs will arise between safety and other requirement on the road traffic system. Over-reliance on the swift collision-avoiding reactions of self-driving vehicles can induce people to take dangerous actions, such as stepping out in front of a car, relying on its fast braking. Children travelling alone can violate safety instructions such as the use of seatbelts. Digital information about routes and destinations can be used to convey commercial and political messages to car users. If fast passage can be bought, then socio-economic segregation of road traffic may result. Terrorists and other criminals can hack into a vehicle and make it crash. They can also use self-driving vehicles for instance to carry bombs to their designed places of detonation or to wreak havoc on a country's road system.

1 Introduction

This will be a momentous technological transformation. It calls for major efforts to anticipate and evaluate social changes that may potentially accompany the introduction of the new technology. As part of these endeavours, ethical and public policy aspects of the technology itself and of various scenarios for its introduction need to be explored (Palm & Hansson, 2006). This article presents an overview of plausible challenges and opportunities that can potentially result from the introduction of self-driving (driverless, autonomous) road vehicles. Our purpose is to broaden the discussion from a focus on the crash behaviour of vehicles to the many types of social change that the new technology can be involved in. We have studied the ethical literature on the topic, and reflected on the social and ethical implications of topics brought up in the technical and policy-oriented literature. This search resulted in a fairly extensive (but of necessity not exhaustive) list of issues, many of which do not seem to have been discussed previously in the ethical literature.1 In what follows we begin by discussing the changes in responsibility ascriptions that can be expected ("Sect. 2"), since such changes will determine much of the ethical framework for the new technology. After that we discuss potential positive and negative reactions to automated vehicles ("Sect. 3") and the trade-offs between safety and other requirements on a new road traffic system ("Sect. 4"). We then turn to the important ethical issues that arise from the possibility of external control of autonomous vehicles ("Sect. 5") and from the large amounts of person-related data that will be collected in vehicles and road management systems ("Sect. 6"). This is followed by chapters on human health and the environment ("Sect. 7"), social and labour market relations ("Sect. 8"), and criminality ("Sect. 9"). Our conclusions are summarized in "Sect. 10 ".

2 Responsibility for Safety

Much of the discussion on self-driving vehicles has been concerned with issues of responsibility. In the currently on-going tests on public roads, there is always a person on the driver's seat, called a "safety driver" or "steward", who is required to follow the traffic and be prepared to take over control immediately if the need arises. The safety driver has essentially the same legal responsibilities as the driver of a conventional vehicle. However, this is seen as a temporary solution, and the automobile industry aims at releasing the safety driver, so that all occupants of the vehicle can be passengers. Such a step would seem implausible unless and until automatic driving has achieved a markedly higher level of safety than human driving. Possibly, this will first be attained only in certain parts of the road system (e.g. motorways), and fully automatic driving may then initially be allowed only there.

If and when this happens, a radically new situation will arise with respect to responsibility. If there is no driver who controls the vehicle, who is then responsible for the safety of its passengers and of those who travel or walk on the same roads? If a car is "driven" by a computer possessing artificial intelligence, does that intelligence constitute an entity that can be held responsible? What are the responsibilities of the vehicle's current owner? Its manufacturer? The owner and manager of the road system? The organization running the traffic control centre that the vehicle communicates with?

Even without automatic vehicles, traditional assumptions about responsibilities in road traffic have been subject to change in the last few decades. Traditionally, drivers and others moving on the roads have been taken to carry almost the whole burden of responsibility (Melcher et al., 2015, p. 2868).2 Vision Zero, which was introduced in Sweden 1997 and is now adopted in numerous countries, states, and cities around the world, aims at eliminating all fatalities and serious injuries in road traffic. It puts much more emphasis than previous approaches on the responsibilities of road builders and managers, vehicle manufacturers, and others who contribute to creating and maintaining the traffic system, or use it professionally (Belin et al., 2012; Rosencrantz et al., 2007). Future changes in responsibility ascriptions will have to be seen in that perspective.

These two major forms of responsibility coincide in many practical situations, but in particular in complex social situations, they can be born by different agents. For instance, suppose that a motorist who drives too fast kills a child crossing a road on its way to school. In the subsequent trial, the driver will be held (blame) responsible for the act. And of course the driver is (task) responsible for not driving dangerously again. But that is not enough. We also need to prevent the same type of accident from happening again, with other drivers. This is not something that the culpable driver can do. Instead, measures are needed in the traffic system. We may have reasons to introduce traffic lights, speed bumps, or perhaps a pedestrian underpass. The task responsibility for these measures falls to decision-makers, such as public authorities. In cases like this, blame and task responsibility part company.

What will happen with our responsibility ascriptions when driverless cars are introduced? One thing should be clear: since the users of fully automated vehicles have no control over the vehicle, other than their choice of a destination, it would be difficult to hold them responsible either for safety (task responsibility) or for accidents (blame responsibility) (Gurney, 2017). We do not usually hold people responsible for what they cannot control (King, 2014).3 There are three major alternatives for what we can do instead. First, we can hold other persons responsible instead. The most obvious candidates are the vehicle manufacturers and the people responsible for the road system (including the communication and coordination systems used to guide the vehicles). The second option is to hold the artificial intelligence built into the vehicles responsible. The third is to treat traffic accidents in the same way as natural accidents such as tsunamis and strokes of lightning, for which no one is held responsible. In Matthias' ( 2004) terminology, this would mean that there is a "responsibility gap" for these accidents. Several authors have warned that self-driving vehicles may come with a responsibility gap (Coeckelbergh, 2016; de Jong, 2020).

Although the future is always difficult to predict, the first option is by far the most probable one. Previous experience shows that this is how we usually react when a person to whom we assigned responsibility is replaced by an automatic system. For instance, if an aviation accident unfolds after the pilot turned on the autopilot, we do not blame the artificial intelligence that took over the flight, and neither do we treat the failure as a natural event. Instead, we will probably put blame on those who directed the construction, testing, installation, service, and updating of the artificial intelligence. Such an approach is not unknown in road traffic. In the past few decades, proponents of the Vision Zero approach to traffic safety have had some success in achieving an analogous transfer of responsibility to vehicle and road system providers, although human drivers are still in place.

It cannot be excluded that future, perhaps more human-like, artificial agents will be assigned blame or task responsibility in the same way as human agents (Nyholm, 2018a, pp. 1209-1210). However, in the foreseeable future, the systems running our vehicles do not seem to be plausible candidates for being so treated. These will be systems taking and executing orders given to them by humans. There does not seem to be any need for them to express emotions, make self-reflective observations, or exhibit other behaviours that could make us see them as our peers.4 It should also be noted that current approaches to automatic driving are predominantly based on pre-programmed response patterns, with little or no scope for autonomous learning. This is typical for safety-critical software. The cases in which it appears to be difficult to assign responsibility for an artificial agent to its creator(s) are those that involve extensive machine learning, which means that the programmers who constructed the software have no chance of predicting its behaviour.

We should therefore assume that for driverless vehicles, the responsibilities now assigned to drivers will for the most part be transferred to the constructors and maintainers of the vehicles and the roads and communication systems on which they depend (Bonnefon et al., 2020, pp. 53-63; Crane et al., 2017; Luetge, 2017, p. 503; Marchant & Lindor, 2012).5 This also seems to be what the automobile industry expects to happen (Atiyeh, 2015; Nyholm, 2018c). It will have the interesting consequence that blame responsibility and task responsibility will be more closely aligned with each other since they are carried by the same organization (Nyholm & Smids, 2016, p. 1284n). The responsibility of manufacturers can either be based on products liability or on some new legal principle, such as Gurney's ( 2017) proposal that in liability cases, the manufacturers of autonomous vehicles should be treated as drivers of those vehicles. Abraham and Rabin ( 2019) suggested a new legal concept, "manufacturer enterprise responsibility" that would involve a strict liability compensation system for injuries attributable to autonomous vehicles. Some authors, notably Danaher ( 2016) and de Jong ( 2020), have put focus on the "retribution gap", i.e. the lack of mechanisms to identify individual persons that are punishable for a crash caused by an autonomous vehicle. This part of the responsibility gap cannot be so easily filled by a corporate entity as the parts concerning compensation (another part of blame responsibility) of future improvements (task responsibility). However, finding someone to punish is not necessarily as important as compensating victims and reducing the risks of future crashes.

It is much less clear how responsibilities will be assigned in near-automated driving, in which a human in the driver's seat is constantly prepared to take over control of the vehicle in the case of an emergency (Nyholm, 2018a, p. 1214). However, although this may be adequate for test driving, it is unclear whether the same system can be introduced on a mass scale. Human interventions will tend to be slow, probably often slower than if the human is driving, and such interventions may also worsen rather than improve the outcome of a dangerous situation (Hevelke & Nida-Rümelin, 2015; Sparrow & Howard, 2017, pp. 207-208). It is highly doubtful whether such arrangements satisfy the requirement of "meaningful human control" that is frequently referred to in the AI literature (Mecacci & Santoni de Sio 2020). Since meaningful control is a standard criterion for both blame and task responsibility, it is therefore also doubtful whether either type of responsibility can be assigned to a person sitting in the driver's seat under such conditions (Hevelke & Nida-Rümelin, 2015).

3 What Can and Should Be Accepted?

Although the automotive industry and public traffic administrations are planning for automatized road traffic, its introduction will, at least in democracies, ultimately depend on how public attitudes will develop. Some studies indicate that large parts of the population in most countries have a fairly positive attitude to autonomous vehicles (Kyriakidis et al., 2015). However, such studies should be interpreted with caution. Not many have any experience of self-driving vehicles, and no one has experience of their large-scale introduction into a traffic system. Furthermore, other studies indicate a less positive attitude (Edmonds, 2019).

Public attitudes to accidents involving autonomous vehicles will be important, perhaps decisive, for the introduction of such vehicles in regular traffic. Will we accept the same frequency of serious accidents with self-driving cars as that which is now tolerated for vehicles driven by humans? There are several reasons to believe that we will not. Already today, tolerance for safety-critical vehicle malfunctions is low. Manufacturers recall car models to repair faults with a comparatively low probability of causing an accident. They would probably encounter severe public relations problems if they did not. Previous attempts to limit such recalls to cases when they have a favourable cost-benefit profile have proved disastrous to the manufacturer's public relations (Smith, 2017). The public tends to expect much lower failure rates in vehicle technology than in the behaviour of drivers (Liu et al., 2019). This difference is by no means irrational, since technological systems can be constructed to be much more predictable, and in that sense more reliable, than humans.6

Another reason to put high demands on the safety features of driverless vehicles is that improvements in technology are much more generalizable than improvements in human behaviour. Suppose that a motorist drives over a child at dusk because of problems with his eyesight. This may be reason enough for him to change his way of driving, or to buy new eyeglasses. If his eyesight cannot be sufficiently improved, it is a reason for authorities to withdraw his driver's licence. However, all these measures will only affect this particular driver. In contrast, if a similar accident occurs due to some problem with the information processing in an automatized vehicle, then improvements to avoid similar accidents in the future will apply (at least) to all new vehicles of the same type. The fact that a crash with a self-driving vehicles cannot be written off as an exception due to reckless behaviour may also contribute to higher demands on the safety of these vehicles.

In addition to these rational reasons for high safety requirements on driverless vehicles, public attitudes may be influenced by factors such as fear of novelties or a particular revulsion to being killed by a machine. There have already been cases of enraged opponents slashing tyres, throwing rocks, standing in front of a car to stop it, and pointing guns at travellers sitting in a self-driving car, largely due to safety concerns (Cuthbertson, 2018). At least one company has left its self-driving test vehicles unmarked in order to avoid sabotage (Connor, 2016).

On the other hand, strong negative reactions to driverless cars can be expected to develop in segments of the population. In road traffic as we know it, drivers communicate with each other and with unprotected persons in various informal ways. Drivers show other drivers that they are leaving them space to change lanes, and pedestrians tend to wait for drivers to signal that they have seen them before stepping into the street. Similarly, drivers react to pedestrians showing that they wait for the vehicle to pass (Brooks, 2017a; b; Färber, 2015, p. 143; Färber, 2016, p. 140). Inability of automatic vehicles to take part, as senders or receivers, in such communications, may give rise to reactions against their presence in the streets. There may also be disapprovals of patterns of movement that differ from the driving styles of most human drivers, such as strictly following speed limits and other traffic laws, and accelerating and decelerating slowly in order to save energy (Nyholm & Smids, 2020; Prakken, 2017).

Furthermore, negative reactions can have their grounds in worries about the social and psychological effects of dependence on artificial intelligence, or about the uncertainties pertaining to risks of sabotage or large accidents due to a breakdown of the system. There are signs that significant reactions of this nature may arise. According to a study conducted by the American Automobile Association, three out of four Americans are afraid of riding a fully autonomous car (Edmonds, 2019). Such attitudes may be connected with other misgivings about a future, more technocentric society. Such reactions should not be underestimated. The experience of genetically modified crops in Europe shows that resistance to new technologies can delay their introduction several decades, despite extensive experience of safe use (Hansson, 2016).

Attitudes to automatized road traffic can also be influenced by devotion to the activity of driving. For some people, driving a motor vehicle is an important source of pride and self-fulfilment. The "right to drive a car" is important in their lives (Borenstein et al., 2019, p. 392; Edensor, 2004; Moor, 2016). Notably, this does not necessarily involve negativity to mixed traffic, as long as one is allowed to drive oneself, and the "pleasure of driving" is not too much thwarted by the self-driving vehicles and the arrangements made for them. The "Human Driving Manifesto" that was published in 2018 argued explicitly for mixed traffic, claiming that "[t]he same technology that enables self-driving cars will allow humans to retain control within the safe confines of automation" (Roy, 2018). However, from an ethical (but perhaps not a political) point of view, the pleasures of driving would tend to be lightweight considerations in comparison with the avoidance of fatalities on the road.

All this adds up to prospects for severe social and political conflicts on the automatization of road traffic. Judging by previous introductions of contested technology, there is a clear risk that this can develop into a trench war between parties with impassioned and uncompromising positions. If driverless cars achieve a much better safety record than conventional vehicles-otherwise their introduction seems unlikely-then proponents will be invigorated by the safety statistics and will see little reason to make concessions that would be costly in terms of human lives. On the other hand, opponents motivated by abhorrence of a more technology dependent society cannot be expected to look for compromises. Dealing with the terms of such an entrenched clash of social ideals may well be the dominant issue of ethical involvement in road traffic automatization. Needless to say, rash and badly prepared introductions of self-driving vehicles could potentially trigger an escalation of such conflicts.

4 Safety and the Trade-Offs of Constructing a Traffic System

In the construction of a new traffic system, safety will be a major concern, and possibly the most discussed aspect in public deliberations. However, there will also be other specifications of what the traffic system should achieve. Just as in the existing traffic system, this will in practice often lead to trade-offs between safety and other objectives. Since safety is an ethical requirement, all such trade-offs have a considerable ethical component. In a new traffic system, they will have to be made with a considerably higher priority for safety than in the current system with its dreadful death toll.

Many of the more specific features of self-driving vehicles, such as short reaction time and abilities to communicate with other vehicles, can be used both to enhance safety and to increase speed. For instance, driving on city roads and other roads with unprotected travellers, such as pedestrians and cyclists, will always be subject to a speed-safety trade-off (Flipse & Puylaert, 2018, p. 55). With sufficiently low speeds, fatal car-pedestrian collisions can virtually be eradicated. Probably, passengers of driverless vehicles would not tolerate such low speeds. They can also cause annoyance and possibly risky behaviour by the drivers of conventional vehicles. On the other hand, if the tolerance for fatal accidents becomes much lower for self-driving than for humanly driven vehicles (as discussed above), then demands for such low speeds can be expected. As noted by Goodall ( 2016, pp. 815-816), since fast transportation in city areas is beneficial to many types of businesses, the speed-safety trade-off will be accompanied by an economy-safety trade-off connected with the efficiency of logistics.

Increased separation between pedestrians and motor vehicles can efficiently reduce accident risks. The introduction of inner city zones, similar to pedestrian zones but allowing for automatized vehicles driving at very low speeds and giving way to pedestrians, could possibly solve the safety problem and the need for transportation of goods. However, such zones may not be easily accepted by people who wish to reach city destinations with conventionally driven vehicles. This can lead to an accessibility-safety trade-off.

Self-driving vehicles can drive close to each other in a caravan, where the first vehicle sends out instructions to brake or accelerate, so that these operations are performed simultaneously by the whole row of vehicles. This technology ("platooning") can significantly reduce congestion and thereby travel time. However, an efficient use of this mechanism will inevitably tend to reduce safety margins (Hasan et al., 2019; Hu et al., 2021). This will give rise to a speed-safety trade-off, but also to an economy-safety trade-off concerning infrastructure investments.

Even if accidents due to incoordination in fast-moving vehicle caravans will be very unusual, the effects can be enormous. This may place road traffic in a situation more similar to that of civil aviation, whose safety considerations are dominated by rare but potentially very large accidents (Lin, 2015, p. 80; Lin, 2016, p. 80). There may then be incentives to limit the number of vehicles in a caravan, and thereby the size of a maximal accident, although such a limitation may not decrease the expected total number of fatalities in these rare accidents. Discussions on such measures will involve a small-vs.-large-accidents trade-off.

Already in today's traffic system there are large differences in safety between different cars. Important safety features are present in some car models but not in others. Some of these safety features, such as crumple zones, safety cells, and airbags, reduce the severity of the injuries affecting drivers and passengers (crashworthiness). Others, such as driver monitoring systems and anti-lock braking systems, reduce the probability of accidents (crash avoidance). Many of the crash avoidance features that are now installed on human-driven cars can be seen as forerunners of components that will be integrated into fully autonomous driving systems. The efficiency of the total crash avoidance system of self-driving cars will be crucial for the extent to which these vehicles can be introduced into road traffic. Like all other features, those affecting crash avoidance can be expected to differ between car models. New models will expectedly have better crash avoidance systems. Expensive car models may be equipped with better systems than less expensive ones; for instance, they may have better and more costly sensors (Holstein et al., 2018).

Currently, our tolerance is in practice fairly high for large differences in the risks that different vehicles expose other road users to, due to variations in equipment as well as in driver skills and behaviour. In many countries, a minimal technical safety level is ensured by compulsory periodic motor vehicle inspections, which include checks of brakes and other basic requirements. However, there are still large differences between vehicle types and models for instance in driver monitoring systems and anti-lock braking systems. In general, new cars have a higher standard than old cars in these respects. Recalls to update old cars to the technical safety standards of new cars are, to our knowledge, not practised anywhere.7 Software updates in old vehicles may become a difficult issue, in particular for vehicles that outlive their manufacturing company (Smith, 2014). Today, most accidents are ascribed to human failures (Rolison et al., 2018). When the majority of crashes are ascribed to vehicle failures, prohibition of inferior vehicle types will be a much more obvious way to improve safety. Doing so will be good for safety, but achieving the higher safety level will be costly. To the extent that the higher costs for safety will prevent people with low incomes from owning motor vehicles, it can also involve an equity-safety trade-off.

The protection of passengers against accident risks will have to be implemented in a new situation in driverless cars. There may no longer be a person present in the vehicle who is responsible for the safety of all passengers. Presumably, this also means that there will no longer be a need for one sober person in the car. We can foresee trade-offs between, on the one hand, passengers' free choice of activities and behaviour in the vehicle, and on the other hand, the measures required for their safety, in short freedom-safety trade-offs. A car or a bus can be occupied by a company of befuddled daredevils trying to bypass whatever features the vehicle has been equipped with to prevent dangerous behaviour such as leaning out of windows or throwing out objects. The introduction of mechanisms to detect and prevent dangerous behaviour, such as non-belted travel, can be conceived as privacy intrusive, and we then have a privacy-safety trade-off. It should be noted, however, that such mechanisms have an important function for minors travelling alone. Children may easily indulge in unsafe behaviour, such as travelling without a seat belt, and standard anti-paternalist arguments are not applicable to under-age persons. Vehicle-to-vehicle and vehicle-to-infrastructure communication can give rise to another privacy-safety trade-off; see "Sect. 6 ".

Just like human drivers, self-driving vehicles can become involved in traffic situations where an accident cannot be avoided, and a fast reaction is needed in order to reduce its consequences as far as possible. A considerable number of ethics papers have been devoted to cases in which this reaction has to deal with an ethical dilemma, for instance between driving either into two elderly persons or one child.8 Such dilemmas are virtually unheard of in the history of human driving. The reason for this is that the dilemmatic situations are extremely rare in practice. In order for such a situation to arise, two unexpected human obstacles will have to be perceived simultaneously and with about the same degree of certainty, so that the (human or artificial) agent's first reaction will take both into account. Furthermore, there have to be two reasonably controlled options to choose between. As excellently explained by Davnall ( 2020), such situations are extremely rare. In almost all situations when a crash is imminent, the most important reaction is to decrease the car's speed as much as possible in order to reduce its momentum. The choice is therefore between braking maximally without swerving and braking maximally and at the same time swerving. The latter option has severe disadvantages: swerving reduces the efficiency of braking, so that the collision will take place with a larger momentum. Swerving leads to loss of control, so that (in sharp contrast to the unrealistic examples in this literature) the car's trajectory becomes unpredictable. This can lead to skidding, spinning, and a sideways collision that is not alleviated by the crumple zones at the car's front. The chances for pedestrians and others to move out of harm's way are also smaller if the car is spinning and skidding. In summary, the self-driving car "does not face a decision between hitting an object in front of it and hitting an object off to one side. Instead, the decision is better described as being between a controlled manoeuvre-one which can be proven with generality to result in the lowest impact speed of any available option-and a wildly uncontrolled one." (Davnall, 2020, pp. 442-443). Due to the physics of braking and crashing, the situation is very much the same for self-driving systems as it is for human drivers. Consequently, the need for including deliberations on this type of dilemmas does not seem to be larger in the programming of automatized vehicles than in driver's education9 (Brooks, 2017a). Discussions of such dilemmatic situations seem to have been driven by theoretical considerations, rather than by attempts to identify the ethical problems arising in automated road traffic.10 The ethical problems of crash avoidance, in particular the speed-safety trade-offs and the other trade-offs described above, will in all probability be much more important and should therefore be at the centre of the ethical discussion.

5 External Control of Driverless Vehicles

We typically think of an automated car as a vehicle following the directions of the human being who instructs it, both concerning the destination and the route. However, it will not be difficult to construct systems in which the decisions by individual drivers can be overridden by the traffic guidance system. In the case of a traffic jam on a particular road section, driverless vehicles can be redirected to uncongested roads. Such automatic redirection will be much more efficient than sending messages to the passengers who will then have to choose whether or not to follow the recommended new route. However, enforced redirection of a vehicle due to congestion may be conceived as an infringement on the freedom of its occupants. It is both possible and desirable to retain a personal choice for the road users in that case.

The ability of emergency service vehicles to reach their destination as quickly as possible is often a matter of life or death. In a fully automatized road traffic system, both the velocity of the blue light vehicles and the safety of other travellers can be substantially increased if all other vehicles on the pertinent roads are kept out of way through external control by the traffic guidance system. In addition, such external control of vehicles can be used for various law enforcement purposes, such as stopping a car at the roadside in order to arrest a traveller or to search for drugs, contraband or stolen goods. It has been predicted that such remote seizure can decrease the risk of deadly violence when a car is stopped by the police (Joh, 2019, p. 309).

However, as the risks involved in stopping a vehicle become smaller, there may be moves to use the method for many more purposes than what traditional car chases are used for (namely, to capture persons trying to escape law enforcement). For instance, vehicles can be stopped in order to seize foreign nationals without a valid visa, persons suspected of having committed a minor misdemeanour, or a person whose travel destination indicates an intention to violate a restraining order (Holstein et al., 2018). The purposes for which law enforcement agencies can take over control of a vehicle, and the procedures for decisions to do so, will therefore have to be determined, based on a balance between the interests of law enforcement and other legitimate interests.

6 Information Handling

The potential advantages of self-driving vehicles can only be realized with well-developed communication systems. Vehicle-to-vehicle (inter-vehicle) communication can be used to avoid crashes and organize platooning. Vehicle-to-road-management communication systems can provide updated local information on traffic and accessibility. Both types of communication can complement the information gathered by the vehicle itself. Information about obstacles ahead can be obtained before they are registered by the car's own sensors. Furthermore, sensor or sensor interpretation errors can be detected by comparison with information from other cars or from the roadside. If vehicle-to-road-management systems are interconnected on a large scale, then they can also be used for optimizing the traffic flow (van Wyk et al., 2020).

However, like all large-scale handling of person-related information, the collection and processing of traffic information can give rise to considerable privacy intrusions (Zimmer, 2005). Today, it is still largely possible to travel anonymously. A person who drives a private car does not necessarily leave any electronic traces, and the same applies to someone travelling by collective transportation (unless she pays with a payment card or a personal travel card) or by taxi (unless she pays with a payment card or the taxi has video surveillance).

All this will be different in an automatized traffic system. Self-driving vehicles will depend on geopositioning transponders operating in a highly standardized fashion (Borenstein et al., 2019, p. 384), and possibly on centralized communication systems that keep track of each vehicle's planned route and destination (Luetge, 2017, p. 554). For privately owned cars, this information will be linkable to the owner. It can potentially be accessed by the road manager and by authorities. The situation will be similar for cars that are rented on a short-term or long-term basis. Just as today, companies renting out vehicles for personal use will register the identity of their customers. Furthermore, there will presumably be an incentive to install video surveillance systems in driverless vehicles-in particular buses-in order to deal with potential disturbances.

Geopositioning of persons can be highly sensitive. It can reveal memberships in religious or political organizations, as well as sensitive private relationships. For a member of a cult, a criminal or extreme political organization, disclosure of visits to an organization offering exit counselling can be life-threatening. The disclosure of travel destinations can be equally dangerous for a person who has obtained a new identity, for instance in a witness protection programme or a programme protecting women from harassment by ex-husbands. More generally, freedom to travel without being surveilled-by government, companies, or private persons-is arguably one of the values universally cherished in liberal societies (Sobel, 2014).

Geopositioning data can also potentially be used for commercial purposes. Currently, web browsing data on a person's movements in the virtual space of the internet is used to tailor a massive flow of advertisements (Véliz, 2019; Vold & Whittlestone, 2019). With geopositioning data, our movements in real space can be used in the same way (Gillespie, 2016). Sellers and rental providers of vehicles will have economic incentives to include an advertisement function over which they retain control, so that they can sell space on it. For instance, after a car has been parked outside a timber yard, the owner or renter of the car would receive commercial messages from other construction stores. A (devotional or touristic) visit to a church or a mosque could be followed by messages from proselytizing organizations etc. Political ads could be individualized, based for instance on the combination of past travel and web surfing habits. These commercial messages could be conveyed via loudspeakers or screens in the car, or through other media connected with the person who owns or rents the vehicle. It is not inconceivable that such personalized commercials may become as ineluctable for travellers as the (personalized) commercials are today for the web surfer and the (impersonal) ads for the newspaper reader (King, 2011; Svarcas, 2012). Car manufacturers are already developing recommender systems that deliver commercial information based on the recipient's previous behaviour. Such systems can be installed in both human-driven and self-driving cars (Vrščaj et al., 2020). In addition, ride-sharing can be tailored, based on personal information for instance from web browsing, which is used to find a suitable travel companion (Moor, 2016; Soteropoulos et al., 2019, p. 46). However, we still have a (political) choice whether we want our real-world movements to be registered and used for such purposes.

A person going by a driverless car may have a destination that is less precise than a specific address, such as "a grocery" or "a place on the way to the final destination where I can buy some flowers". Such destinations leave open for considerable commercial opportunities of the same types that are currently used on web browsers and social media. The car-traveller can then find herself driven, not to the closest grocery or flower shop, but to a store further away that has paid for being directed to. Travellers can also be offered to stop at places, for instance restaurants, for which they have not expressed any desire. There will be strong incentives for the sellers and renters of vehicles to display such services. But in this case as well, we still have an option to decide (politically) what types of messages our future travels should impose on us.

If the coordination between automatized vehicles is efficient, then the vast majority of accidents will probably result from collisions with cars driven by humans and with unprotected travellers such as pedestrians, cyclists, motorcyclists, and horseback riders. An obvious solution to this would be for non-autonomous vehicles, pedestrians etc. to carry a transponder that communicates with motor vehicles in order to avoid collisions (Morhart & Biebl, 2011). Parents may wish to provide their children with transponders in order to ensure their safety. It is not inconceivable that demands may arise to make transponders mandatory for certain types of vehicles (such as motorcycles), or for persons walking, cycling or horse-riding on particularly dangerous roads. Obviously, personal transponders would give rise to much the same privacy issues as vehicle-bound geopositioning.

7 Effects on Health and the Environment

To the extent that public transportation such as fixed route buses is replaced by self-driving vehicles that are called to the user's location, there will no longer be a need to walk to and from a bus stop or a train or subway station. Such walks are an important part of the physical exercise performed by large parts of the population. Reducing the amount of exercise from an already suboptimal level can have negative health effects (Sallis et al., 2012). This may call for counter-measures, such as making residential areas car-free (Nieuwenhuijsen & Khreis, 2016).

The distribution between road traffic and other modes of traffic, in particular aviation and rail-bound traffic, may change due to the introduction of self-driving vehicles, but it is not possible to foresee what direction such changes will take. If road traffic replaces air-trips, then this will have positive environmental and climate effects. If it replaces rail traffic, then the effect may go in the opposite direction.

It seems plausible that self-driving vehicles will have better energy efficiency than vehicles driven by humans (Urmson & Whittaker, 2008). It has also been proposed that electric vehicles will be more attractive if they are self-driven so that they can "recharge themselves" when they are not needed (Brown et al., 2014). However, it is also plausible that the total mileage will increase (ibid.). The effects of automatized road traffic on the climate and the environment will also depend on several other factors, such as the distribution between privately owned and rentable vehicles (Zhang et al., 2018), and the extent of car- and ride-sharing (Fagnant & Kockelman, 2018). The introduction of a traffic management system that coordinates travel will make it easier than in the current system to arrange ride-sharing. However, if most of the vehicles continue to be privately owned (or long-time rented), then incentives to ride-sharing may be insufficient, and car travelling may continue to be as inefficient as today in terms of the number of passengers per vehicle. If traffic is mostly organized with cars hired for each occasion, similar to the current taxi system, then large-scale ride-sharing can more easily be organized and made economically attractive. Needless to say, the choice between these alternatives is a policy decision that need not be left to the market. The climate crisis provides strong reasons to support ride-sharing for instance with incentives in the transport fare system (Greenwald & Kornhauser, 2019). However, it is doubtful whether improved energy efficiency and increased car- and ride-sharing can outweigh the increased mileage that is expected to follow with the introduction of self-driving vehicles. At any rate, increased use of climate-friendlier modes of transportation, such as trains and bicycles, is necessary to achieve climate objectives.

A routing system for automatized traffic can be constructed to ensure that each vehicle reaches its destination as soon as possible. Alternatively, it can be tailored to achieve energy efficiency. This will mean lower velocities and fewer accelerations and decelerations, and therefore also increased travel time. Policy-makers will have to decide whether to leave this choice to the individual vehicle user (just as the same decision is left to individual drivers in the present system), or to regulate it in some way. Such a regulation can for instance impose a minimal priority to be assigned to energy conservation in all motor vehicles, or it can involve some form of taxation incurring additional costs on energy-inefficient transportation. Probably, platooning will be so energy-efficient that there will be strong reasons for policy-makers to consider the introduction of a unified speed on major highways (Brown et al., 2014).

Both road lighting and exterior automotive lighting can be substantially reduced in an automatized road traffic system (Sparrow & Howard, 2017, p. 212). This will reduce energy consumption, and it will also lead to a reduction in light pollution (Stone et al., 2020). No large effects on the noise pollution emitted from each vehicle can be expected, since the noise level depends primarily on the energy source and the type of motor, rather than on whether the vehicle is automatized or conventionally driven. An increase in road traffic, which is a plausible consequence of automation, will lead to increased noise pollution.

8 Social and Labour Market Consequences

The effects for people with disabilities would seem to be more unequivocally positive. Costly adaptations of vehicles can to a large part be dispensed with. A considerable number of people who cannot drive a car will be able to go on their own in a self-driving car (Mladenovic & McPherson, 2016, p. 1137). This will increase their mobility, and it can potentially have positive effects on their well-being and social connectedness.

On the negative side, an automatized road traffic system makes it possible to introduce new social divisions among travellers. We already have divisions between more and less affordable manners of travelling on-board the same vehicle. However, although those who travel first or business class on trains and airplanes have more legroom, and (on airplanes) receive more drinks and presumably better food, they leave and arrive at the same time. If there is a traffic delay, first class passengers are not sent off in a separate vehicle, leaving the second (or "tourist") class passengers behind. A road management system will of course ensure the swift passage of emergency vehicles when other vehicles have to travel slowly, but will it also offer swift passage to those who can afford a "first" or "business" option for their travel? There will certainly be economic incentives to provide such services for those who can pay for them (Dietrich & Weisswange, 2019; Mladenovic & McPherson, 2016). The negative effects on social cohesion and solidarity of such a system should not be underestimated. Fortunately, the choice whether to allow such shortcuts for the prosperous is a political decision yet to be made.

Sensors currently in use tend to be less reliable in detecting dark-skinned than light-skinned pedestrians (Cuthbertson, 2019). This will expose dark-skinned pedestrians to higher risks than others. The probable cause of this defect is that too few dark-skinned faces have been included in the training sets used when the sensor software was developed. This is a problem that will urgently have to be eliminated.

As in other branches of industry, automatization of the traffic system will lead to a decreased need of personnel. Driving professions such as those of a bus driver, lorry driver or taxi driver will gradually diminish. For instance, it has been estimated that 5 million Americans work at least part time as drivers (Eisenstein, 2017). That is about 3% of the workforce. Even a partial and gradual replacement of these jobs by automatized vehicles will require solutions such as training schemes and other forms of labour market policies (Hicks, 2018, p. 67; Ryan, 2020). If such measures are not taken, or are not efficient enough, the result will be unemployment, with its accompanying social problems.11 It should be noted that other branches of industry are expected to undergo a similar process at the same time. The labour market effects of automatized road traffic can therefore be seen as part of the much larger question whether and how the labour market can be readjusted at sufficient pace to deal with the effects of artificial intelligence and its attendant automatization (Pavlidou et al., 2011).

However, self-driving vehicles may also have a positive effect on the supply side of the labour market. To the extent that travel becomes faster and/or more convenient, workers will be willing to take jobs at larger distance from home, thus facilitating matching on the labour market. Affordable travel opportunities to workplaces can make it possible for underprivileged people to escape poverty (Epting, 2019, p. 393).

It is highly uncertain what effects the introduction of self-driving cars will have on employment in the automotive industry. A decrease in the number of cars produced would have a negative impact on employment. However, as noted in "Sect. 2 ", the industry is expected to have a much higher post-production involvement in self-driving than in human-driven cars. This should have positive effects on employment in the automobile industry. However, parts of this effect may be due to a transfer of employments from other branches of industry. Furthermore, the automotive industry is at the same time subject to other developments that affect the size of its labour force, in particular the automatization of its production processes and economic developments in third-world countries that increase the number of potential users and owners of motor vehicles. The total effect of all these developments is uncertain.

9 Criminality

Almost invariably, major social changes give rise to new forms of criminality that threaten human welfare. We have no reason to believe that vehicle automatization will be an exception from this. Four important potential variants of criminality are illegal transportation, unauthorized access to data, sabotage, and new forms of auto theft.

Automated vehicles can be used for illegal transportation tasks, for instance smuggling and the delivery of drugs, stolen goods, and contraband. For law enforcement, this can give rise to new challenges. Police inspection of vehicles with no traveller will be less intrusive than inspection of vehicles containing humans, but privacy concerns will nevertheless have to be taken into account.

The most obvious way to steal data from a vehicle is to hack into its computer system, either by surreptitious physical connection or using its links to other vehicles and to the traffic guidance system (Jafarnejad et al., 2015). If the system contains sensitive information, such as geopositioned travel logs, then this information can be used for instance for blackmailing or for arranging an "accident" at a place to which the owner returns regularly. Information about individual travel patterns obtained from hacking of the traffic guidance system can be used in the same ways.

All self-driving vehicles depend on sensor and software technology, both of which are sensitive to manipulation. Physical sensor manipulation can be performed in order to make the vehicle dysfunctional or (worse) to hurt or kill its passengers (Petit & Shladover, 2015). The effects of such manipulation (as well as other forms of sensor malfunction) can to a large extent be eliminated with sensor redundancy. By comparing the inputs from several sensors with overlapping functionalities, sensor malfunctioning can be detected.

Software manipulation can be performed for various criminal purposes, for instance to make the vehicle inoperable, to make it crash, or to direct the vehicle to a destination undesired by the passengers, for instance with the intent of frightening or kidnapping travellers (Crane et al., 2017, pp. 239-251; Jafarnejad et al., 2015; Joh, 2019, p. 313). Such manipulations can be connected with terrorism or organized crime. The prospect of being helplessly driven at high speed to an unknown place would seem to be scary enough to intimidate a witness. The risk of such software manipulation should be taken seriously. In addition to the usual measures to prevent, detect, contain and respond to an attack, vehicles can be provided with an overriding option for passengers to order it to stop at the nearest place where it can be safely parked (Kiss, 2019).

Software manipulation performed by insiders in the automotive industry is much more difficult to prevent. In the recent diesel emission scandals, prominent motor vehicle industries were capable of illegal manipulation of software, sanctioned on top level in the business hierarchies (Bovens, 2016). Since car manufacturers have much to lose from a bad safety record, they do not have an incentive to manipulate software in a way that leads to serious accidents. However, they may have an incentive to manipulate vehicle-to-road-management information in ways that avoid unfavourable reporting to statistical systems based on these communications. Manufacturers working under an authoritarian regime may be ordered to provide exported vehicles with software backdoors that can be used in a potential future conflict to create havoc in another country's traffic system.

Terrorists or enemy states can hack the traffic guidance system (rather than individual vehicles) in order to sabotage a country's road traffic. They can for instance stop or redirect transportation of goods, or they can direct targeted vehicles to deadly collisions. This is a serious security problem that requires at least two types of responses. First, traffic guidance systems have to be made as inaccessible as possible to attacks. Secondly, vehicle-to-vehicle communication systems should include warning signals sent out from crashing vehicles, giving rise to crash-avoiding reactions in vehicles in the vicinity.

Automatized cars need to be protected against unauthorized access. Privately owned cars can be equipped with face recognition or other bioidentification systems that only allow certain persons to start a ride (similar systems can exclude unauthorized persons from driving a conventional car, Park et al., 2017). Companies renting out self-driving cars will have strong incentives to install identification mechanisms that ensure proper payment and make it possible to trace customers who have done damage to the vehicle. Auto theft may therefore become much more difficult to get away with. This may lead to an increased prevalence of kidnappings with the sole purpose of using the kidnapped person to direct a self-driving car to a desired destination.

In mixed traffic, some roads or lanes may be reserved for driverless vehicles. The traffic on such roads may potentially run at higher speed than the highest speed allowed on roads that are open to conventionally driven cars. Illegal human driving on such roads can give rise to considerable risks, and will therefore have to be strictly forbidden. One potential new form of criminality is driving on such roads, as a form of street racing. There may also be other ways for human drivers to exploit the fast reactions of self-driving vehicles. Safety margins can be transgressed for the thrill of it or in order to pass queues and reach a destination faster (Lin, 2015, p. 81; Lin, 2016, p. 81; Sparrow & Howard, 2017, p. 211). Pedestrians may develop over-reliance on the reactions of self-driving vehicles, and step out in front of a vehicle with an insufficient safety margin, relying on its fast braking (Färber, 2015, p. 143; Färber, 2016, p. 138; Loh & Misselhorn, 2019). Such over-trust in autonomous systems may offset the safety gains that are obtainable with automated road traffic. Measures against it may run into ethical problems concerning paternalism and intrusiveness.

10 Conclusion

In this final section, we will summarize some of the major ethical issues that require further deliberations.

10.1 Responsibility

The introduction of automated road traffic will give rise to large changes in responsibility ascriptions concerning accidents and traffic safety. Probably, the responsibilities now assigned to drivers will for the most part be transferred to the constructors and maintainers of vehicles, roads, and communication systems.

10.2 Public Attitudes

We can expect a much lower tolerance for crashes caused by driverless vehicles than for crashes attributable to errors by human drivers. Such high safety requirements may postpone the introduction of driverless systems even if these systems in fact substantially reduce the risks.

Public opinion will also be influenced by other issues than safety. Apprehensions about a future society dominated by increasingly autonomous technology can lead to resistance against self-driving vehicles. Such resistance can also be fuelled by aberrant "behaviour" of self-driving cars, and by wishes to retain human driving as a source of pride and self-fulfilment. On the other hand, if human driving coexists with much safer automated traffic, it may be put under pressure to become safer. There may also be proposals to limit human driving or to prohibit it altogether. All this can add up to severe social and political conflicts on automatized road traffic. Rash and badly prepared introductions of self-driving vehicles can potentially lead to an escalation of such conflicts.

10.3 Safety

The short reaction times of self-driving vehicles can be used to enhance safety or to increase speed. A trade-off between safety and speed will have to be struck. This applies to platooning on highways, and also to vehicle movements in the vicinity of pedestrians.

A fully automatic vehicle can carry passengers that could not travel alone in a conventional car, for instance a group of inebriated daredevils, or children unaccompanied by adults. It may then be difficult to ensure safety, for instance that seatbelts are used and that no one leans out of a window.

Over-reliance on the swift collision-avoiding reactions of self-driving cars can induce people to take dangerous actions. Pedestrians may step out in front of a vehicle, relying on its fast braking. Motorists may choose to drive (illegally) on roads or lanes reserved for automatic vehicles.

10.4 Control

The police will probably be able stop a self-driving vehicle by taking control of it electronically. This is much safer than traditional high-speed pursuits. However, the purposes and procedures for decisions to halt a vehicle will have to be based on a balance between the interests of law enforcement and other legitimate interests.

More ominously, criminals can take control over a vehicle in order to make it crash or become inoperable. Terrorists or enemy states can use self-driving vehicles to redirect the transportation of important goods, drive into crowds, carry bombs to their designed places of detonation, or create a general havoc in a country's road system.

10.5 Information

Extensive information about routes and destinations will have to be collected in order to optimize the movements of self-driving vehicles. Such information can be misused or hacked. It can for instance be used to convey commercial and political messages to car users. An authoritarian state can use it to keep track of the opposition.

The safety of pedestrians, cyclists, and people travelling in conventional motor vehicles can be improved if they carry transponders that keep self-driving vehicles in their vicinity informed of their positions and movements. Such transponders will give rise to the same issues concerning privacy as the transponders in self-driving vehicles.

10.6 Social Justice

Vehicle types and models will differ in their crash avoidance systems, expectedly with newer and more expensive models having the best systems. It will be technically possible to allow cars with better safety features to operate on different places or at higher speeds than other cars. Socio-economic segregation of road traffic can potentially have considerable negative effects on social cohesion.

The need for professional drivers will gradually decrease, and many will lose their employments. This will require solutions such as training schemes and other forms of labour market policies.

In general, the ethical implications of introducing autonomous vehicles are not inherent in the technology itself, but will depend to a large extent on social choices, not least the decisions of law-makers. Choices have to be made for instance on the required level of safety, the distribution of responsibilities between infrastructure providers and vehicle manufacturers and providers, the organization of traffic control, trade-offs between privacy and other interests, and the adjustment of the traffic sector as a whole to climate and environmental policies. It is essential that these decisions be made in the public interest and based on thorough investigations of the issues at hand. There is also an urgent need for further ethical and social research that penetrates the full range of potential issues that the introduction of autonomous vehicles can give rise to, including key ethical issues such as equity, privacy, acceptability of risk, responsibility, and the social mechanisms for dealing with trade-offs and value conflicts.

Availability of Data and Material

This research is based on publicly available texts that are listed in the bibliography.

Notes

1. For a previous review focusing on crashes with self-driving cars, see Nyholm ( 2018b, c). For a comprehensive scenario-based treatment, see Ryan ( 2020).

2. Husak ( 2004) highlighted the unacceptably high level of risk-taking in the current road traffic system, but laid the responsibility on individual road-users, arguing for instance that trips taken for "frivolous purposes" (p. 352), such as recreational travels by car, are morally objectionable. In contrast, Vision Zero emphasizes the responsibility of those who can transform the traffic system and make it safer.

3. Hevelke and Nida-Rümelin ( 2015) proposed a form of collective (blame) responsibility, shared by all users of fully automated vehicles. However, such shared responsibility can only be implemented through an insurance-based compensation system. It cannot include the possibility of criminal charges. This does not seem to be a plausible way to deal with offences that may potentially include the causation of deaths and serious injuries.

4. Tigard ( 2020) proposed that in cases when a technological system has failed, we can "demand answers from the system itself" and even "hold AI to account by imposing sanctions, correcting undesirable behavioral patterns acquired, and generally seeing that the target of our response works to improve for the future." Although this may be possible as a purely intellectual venture, it is difficult to see how the emotional components of responsibility ascriptions could be established in relation to software.

5. Possibly, large companies that rent out cars will take on more extensive responsibilities than private car owners, whether or not these companies are owned by the car industry.

6. However, it does not follow that machines necessarily perform better in a complex environment where unpredictable disturbances may require reactions that cannot be pre-programmed. Arguably, road traffic is such a complex environment, in particular mixed traffic with both driverless and conventional vehicles.

7. In Sweden between 1975 and 2007, recycling of older vehicles was rewarded with a bonus. This was primarily for environmental reasons, but the bonus also contributed to the disposal of vehicles lacking modern safety equipment.

8. See Nyholm ( 2018b, c) and Davnall ( 2020, pp. 431-434) for references and systematic reviews of this literature.

9. The most plausible scenario in which an ethical dilemma could arise seems to be sudden loss of braking power. This is a rare event in human driving and it is not expected to become more common in self-driving vehicles (Davnall 2020). The dilemmas that it can give rise to do not seem to be a common topic in drivers' education.

10. For further clarifications of the lack of realism of these deliberations, see Gasser ( 2015, p. 556), Goodall ( 2016), Hansson ( 2012, p. 44), Hern ( 2016), Himmelreich ( 2018), and Nyholm and Smids ( 2016). For a well-articulated contrary view, see Keeling ( 2020). Keeling does not take into account the problems with swerving discussed above, and seems to grossly overestimate the frequency of cases with a controlled choice between different ways to crash.

11. This will not be the case in areas with a large shortage of drivers. Self-driving vehicles have been referred to as a potential solution to driver shortage (Mittal et al., 2018).

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Open access funding provided by The Royal Institute of Technology. This research was supported by funding from the Swedish Transport Administration.

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1. Division of Philosophy, Royal Institute of Technology (KTH), Stockholm, Sweden

Sven Ove Hansson & Matts-Åke Belin

2. The Swedish Transport Administration, Borlänge, Sweden

Matts-Åke Belin

3. The Institute for Futures Studies, Stockholm, Sweden

Björn Lundgren

4. Department of Historical, Philosophical and Religious Studies, Umeå University, Umeå, Sweden

Björn Lundgren

5. Department of Philosophy, Stockholm University, Stockholm, Sweden

Björn Lundgren

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Correspondence to Sven Ove Hansson.

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Hansson, S.O., Belin, MÅ. & Lundgren, B. Self-Driving Vehicles-an Ethical Overview. Philos. Technol. 34, 1383-1408 (2021). https://doi.org/10.1007/s13347-021-00464-5
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# data[].attributes.attachment_link.title
Self-Driving Vehicles-an Ethical Overview - Philosophy & Technology

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The introduction of self-driving vehicles gives rise to a large number of ethical issues that go beyond the common, extremely narrow, focus on improbable dilemma-like scenarios. This article provides a broad overview of realistic ethical issues related to self-driving vehicles.

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Introduction

Rarely will a day go by where we do not see a new story, controversy, or debate surrounding self-driving vehicles (SDVs). Despite the repeated influx of information on SDVs, such prevalent discussion should not reduce the importance of continued evaluation of the potential issues this emerging technology will bring. While there has been an abundance of ethical, legal, and social research done already (De Sio 2017; Nyholm 2018; Nyholm and Smids 2016), the range and diversity of concerns are rarely brought together in a single body of work, nor does prior research take into account specific and projected timelines for issues which may emerge from the new technology, with relevant policy responses to those issues. This paper is the first to provide such a comprehensive approach through the use of a 'policy scenario' methodology, providing a stakeholder-engagement-centred approach, to examine the impacts of SDVs by the year 2025.

Debate about the societal implications of SDVs often become pre-empted by discussions about widespread level 5 automation, where the vehicle performs all driving functions in all circumstances, which is not expected for several decades, so there tends to be less urgency to consider more immediate impacts. There is a need, then, to consider nearer-term developments in the use of SDVs to effectively deal with issues that we face in the next five to 6 years. One possible approach for meeting this need to consider these more immediate concerns is the use of scenarios. A scenario is not a future reality, but is a plausible future reality, grounded on current understandings of technological developments and issues surrounding those technologies (Durance and Godet 2010). The purpose of a scenario in this instance is to provide a well-formulated, and plausible, prediction of the state of SDVs in the year 2025 and potential impacts that they may cause. Therefore, the scenario presented in this paper is grounded on the views of experts in the field (generated in a 1-day workshop), stakeholder engagement (generated through outreach for iterations on the scenario), as well as integration of current scientific research in the field (literature analysis).

Methodology

This paper reports on work from an existing project, the SHERPA Project,1 convened to evaluate five applications of emerging technologies and their potential societal implications by 2025; one of those technologies was SDVs (full outline can be found at Wright et al. 2019). The project adopted a scenario methodology to assist in the identification of ethical, legal, social and economic issues related to self-driving vehicles in the coming 6 years. Scenarios are "a tool for ordering one's perceptions about alternative future environments in which one's decisions might be played out concretely, so people can help people make better decisions" (Schwartz 1998, p. 4). While there are many benefits of using scenarios, there are also several drawbacks. For example, scenario approaches are often unstructured, making it difficult to find clear-cut guidance about the issues at stake. Some scenarios are very interesting thought-experiments but provide little guidance on what actions should be taken in practice. Particularly, when they need to be understood by policymakers, vague and convoluted scenarios that require elaborate deconstruction reduce their effectiveness.

From the six scenario approaches (see Table 1) outlined in the SHERPA project, we used the policy scenario for the SDV case, as that scenario construction is the most coherent approach for policymakers. The aim is to have one concise, understandable, and plausible scenario to show policymakers what the most pressing issues and impacts are and steps to achieve a desirable future for SDV use. Policy scenarios are intended to provide an effective approach to identify a diversity of issues, but constructed in an articulate and precise way to support policymakers in the following ways: "To explore possible consequences of current trends; to engage stakeholders; to uncover issues that might otherwise be overlooked; to help decision-making; to consider desired and undesired futures; to determine what steps should be taken to reach the desired future and avoid an undesired future" (Wright et al. 2019, p. 5).

Table 1 See Wright et al. (2019)

The policy scenario aims to establish plausible outcomes, which are evidenced and grounded on current scientific understanding and projections. Scenarios are a very effective tool for policymakers, if they contain a strong degree of plausibility (Volkery and Ribeiro 2009). A scenario should indicate a future timeline that has a level of predictability and does not veer too close to science fiction (Cairns and Wright 2018, pp. 34-45). Therefore, this scenario opts for near-term developments in SDVs, i.e. within the next 5-6 years. This timeline also creates an urgency for policymakers to act-an important component of the policy scenario methodology. Stakeholder engagement is a key component of scenario development and has been integrated in this methodology (Duckett et al. 2017). Stakeholders have been engaged from the scenario's conception, right through to its final construction. One of the key factors for policymakers is to have stakeholder engagement and feedback, which this scenario development process fulfils. This lends strength to the findings and recommendations for decisions on SDVs policy implementation and greater credence to its scientific plausibility and probability.

As detailed in the SHERPA report, the SDV scenario went through four iterative stages, beginning with a 1-day workshop, consisting of 20 experts. A single, or multiple, participant(s) from the project was responsible for the resultant iterations of the scenario. The participants came from a wide range of different backgrounds, experiences, and disciplines, such as standardisation bodies, SDV testing, computer scientists, engineers, psychologists, AI specialists, cybersecurity experts, ethicists, and legal scholars. The SDV workshop was split into several sections that would mirror the sections of the scenario's construction: SDV technological development in 20252; driving forces and barriers for SDVs; the ethical, legal, social, and economic impacts of SDVs; how to mitigate the negative and accentuate the positive impacts of SDVs; and how to reach a desirable future. These sections were split between group-work, open discussion, and critical dialogue of SDVs, so it often involved collective brainstorming and evaluations of the topics discussed.3

Following the workshop, a draft scenario was sent back to the workshop participants for their input and feedback. Further insights were retrieved from relevant journal publications and articles on SDVs to incorporate any additional issues overlooked, while also supporting the scientific validity of the views purported in the scenario construction. Additional literature was used to support the scientific merit of the scenario, to show that many of the topics being discussed are legitimate current concerns.4 A revised scenario was sent to a wider range of experts (30+ people) for the third iteration; before finally posting it on a public platform to receive public engagement (100+ people). The aim of this process was to create a nuanced, cohesive, and consensus-driven scenario for SDV development in the coming 6 years. Overall, the scenario incorporated feedback until the scenario was at a stable state for dissemination on the SHERPA website. This paper is a revised and more detailed examination of the topics outlined in the report for this project. However, there are sections that are quite similar to the original version, but for stylistic and cogency reasons, there will be a strong degree of overlap with the project report (Table 2).

Table 2 Six levels of automation (NHTSA 2017)

Scenario of Self-Driving Vehicles Between 2019 and 2025 (Ryan 2019)

In 2025, SDVs are used in different urban areas throughout the world. 38-year-old Software Developer Hans Adrian uses his self-driving car to go to his office in München every morning, which was one of the first places to roll them out. "So far, so good", explains Hans, who has been using his SDV for over 4 months now. "I am able to work in my car while commuting. When you factor in an hour commute each way, I get back 10 h of my life that is lost in the commute every week. I sit back with my laptop, while listening to Spotify. It's great!" Hans' Waymo Centauri b is one of the few permitted self-driving car models on the market and has been one of the most widely adopted of these vehicles, so far.5 The Centauri b is still in the hybridisation stage towards full automation, having both automated, semi-automated, and manual driving possibilities at level 4 automation. Legally, Hans can only drive fully automated within designated areas of München, but for most other places the car must be in semi-automated or manual mode. "It is a nuisance when I have to drive outside München. It takes a while to get used to the wheel again. But I understand that it will take other cities time before they catch up with us," Hans claims, as the vehicle navigates through his neighbourhood in level 4 automation. His car changes lanes and stops at pedestrian lights, gives way at roundabouts, while allowing him the comfort to catch up on work or just relax and take in the scenery.

So far, SDVs have gained universal integration in only seven cities in the world, but there are hopes that this number will increase dramatically by 2030. Many of the leading car manufacturers and experts estimate that this number will be between 50 and 70 cities by the end of the decade. Some of the most pioneering and revolutionary developments have been coming from Silicon Valley, while the most prolific countries behind SDV development have been the US, South Korea, the UK, Japan, China, and Germany. The US has been the real innovator behind SDVs, with more than 40 cities piloting SDVs as far back as 2017, dwarfing all other countries in comparison (Hao 2017). At the start of 2025, there were 100 cities in the US piloting SDVs, and this number is set to increase dramatically by 2030.

Hans has reaped the benefits of autonomous driving, but only after he passed his SDV driving test. This test consists of both an SDV-driving theory test on how to operate and use the vehicle, with a strong emphasis on shifting from driving to automation, as well as a practical driving examination. Hans was required by German law to undertake 12 driving lessons prior to taking in-car tests. In addition, cities integrating SDVs must also be authorised with the National Self-Driving Vehicle Transportation Board (NSDVTB) and the vehicle owner must be registered with the Department of Self-Driving Vehicles Authority (DSDVA).

The vehicle itself must pass strict manufacturing standards before being allowed on the market. Outside of these designated areas, cars must function at level 3 capacity-limited automation. The car senses when conditions require the driver to retake control and provides enough transition time for the driver to do so. Some SDV companies wanted to skip this stage, but the limitations of technological organisation, the interaction with manual drivers, and the lack of infrastructure to accommodate this move have been too problematic. In areas where there are mixed drivers (automation and non-automation), SDVs must have a level 3 option for legal reasons. One of the main reasons behind these laws is to ensure safety, which has been one of the main facilitators of the development of SDVs in the first place.6

Facilitators of SDVs Between 2019 and 2025

Safety facilitators Approximately 90% of crashes are the result of mistakes by the driver and while road deaths have been decreasing, they were as high as 1.4 million in 2015 (NHTSA 2013, 2017; WHO 2018). Over the past 10 years, safety has been one of the strongest motivators among the driving industry and road safety organisations for the implementation of SDVs but we have yet to reap their true benefits because of SDVs' low level of use. The National Safety Council's 'Road to Zero' campaign is an ambitious goal to have zero automobile-related deaths in the United States by 2050, which may be feasible if they are successfully adopted nationally (Ecola et al. 2018). As far back as 2017, there have been studies to show that deploying SDVs when they are only marginally safer (say, 10%) than human drivers, would still have a dramatic impact on reducing road deaths. Policymakers around the world have largely indicated that waiting for SDVs to be far safer (say, 75-95%) than human drivers is not an option because of how long it would take to reach that stage (Kalra and Groves 2017).

Social facilitators The general public are enthused by the fact that SDVs may offer people the ability to work, sleep, read, eat, or watch TV, while "driving". In 2014, the American Trucking Association (ATA) predicted that there would be a huge shortage of truck drivers, which would necessitate the development of self-driving trucks. Their prediction of 175,000 drivers by 2024 came up short of the reported 215,000-figure taken in November 2024 (Seattle Truck Law PLLC 2018).

Environmental facilitators In the cities where SDVs have been integrated, there are positive indications of carbon emission reductions. Many environmental agencies have demanded more environmentally-sustainable vehicles since the Kyoto and Paris climate agreements, and cities view electric SDVs as one way to meet their EU carbon emission requirements (European Environment Agency 2016). Since 2023, several auto manufacturers have been testing single-user SDVs to bring people from their homes to public SDV buses, which would further reduce environmental impact, while reducing costs.

Economic facilitators While the price of SDVs has been decreasing every year, they are still more expensive than non-automated cars. There has been a recent influx of SDV car-sharing and ride-sharing apps to reduce costs, so that the cars do not sit idle in people's garages or parking lots and can be used throughout the day (Ohnsman 2018). Fuel costs are lower because of greater fuel-efficiency and when they reach widespread level 4 integration, and safety is improved, production costs will decrease because there will be no need for airbags and steering wheels (Davies 2018). Between the 2020 and 2025 periods, many new non-traditional players, such as ICT and data analytics companies, have emerged in the SDV automotive market. Some automotive companies view SDVs as a threat because they cannot put the same kind of investments into developing these technologies as much as their larger automotive counterparts. Fiat, who had been struggling for several years, has closed several of their manufacturing depots, claiming that the shift to automation has massively impacted their sales (Eisenstein 2019).

Market facilitators Over the years, many social critics have stated that the SDV market is supply-driven (McCarthy 2018). SDV manufacturers have seen the benefit of SDVs for goods transportation and data analytics (DHL 2014; Hawthorne-Castro 2018). Auto manufacturers have been hugely competitive in the race to develop SDVs, bringing global success and prestige to their companies. Companies have been extensively patenting their cars, products, and services to lock customers into their brand. However, the notion of automotive branding has been changing over the past few years, with a shift from luxury, status and appearance, towards efficiency, safety, and functionality.

Efficiency and productivity facilitators As a result of greater driving efficiency, Traffic Management Authorities have heralded SDVs for their ability to reduce traffic jams and congestion, identifying better routes to take, more sustainable driving, and a reduction in crashes holding up traffic flow. Despite SDVs being heralded as a way for people to get extra sleeping or relaxation time on their commutes to work, some businesses view them as offering the possibility of cutting out needless 'driving time', allowing staff to work while in the vehicle.

Political facilitators National Ministers for Transportation are encouraging the uptake of SDVs because of their potential to reduce lane size and quantity of lanes due to driving efficiency. However, this has proven difficult in many larger US cities because of the sheer difficulty of implementation. SDVs also promise to reduce road deaths, which will reduce costs on governmental healthcare spending. Furthermore, people will be able to commute longer distances because of the comfort of SDVs, thus moving further from work, which will reduce the strain and congestion in many urban areas.

Potential Barriers and Inhibitors for SDVs Between 2019 and 2025

While crashes with SDVs have decreased over the past few years, they are still more risk-prone in terms of accidents per mile driven than driver-controlled vehicles. Even going back as far as 2017, figures indicated accident rates for every 48,000 miles driven for SDVs, compared to every 2.08 million miles driven for non-autonomous cars (Johnsen et al. 2017, p. 33). Mode transitions has raised additional safety issues, such as distraction, loss of situational awareness, and high workload during take-over. These factors have proven to be inhibitors to the successful development of SDVs and are issues that are constantly being tested and rectified. Many people have also been worried about the security risk of SDVs, such as hacking, manipulation and malicious activity.

Political barriers Since SDVs were first developed, there has been a difficulty in establishing standardisation between companies and countries. It has been challenging to develop protocols, with some claiming that regulation has been too stringent, halting progress, while others have stated that it has not been stringent enough. Governments have found it difficult to strike an appropriate balance between the two and there has also been a great deal of diversity with SDV policies globally, ranging from extremely detailed and dense (EU, US, and Japan) to non-existent (Eritrea, North Korea, and Somalia).

Economic and geographic barriers One barrier for SDVs adoption has been their cost and the infrastructure required to facilitate them. It has been costly to implement policies to accommodate SDVs, so SDVs have largely been adopted by wealthier countries. They have mostly remained untested in many of the world's poorer countries, which is proving to be a key concern in global SDV and social justice circles. Even within richer nations, there has been a wide divergence in acceptance rates of SDVs. For example, willingness-to-pay studies have varied widely amongst nationalities, with many of these divergences remaining largely unchanged since 2017, despite national efforts: 'Italian participants were most interested in using autonomous vehicles (65%), followed by the Spanish participants (54%), the French participants (51%), the Belgian participants (50%), the German participants (44%) and the American participants (32%)' (Johnsen et al. 2017, p. 25). Location has played a fundamental role in the acceptance or rejection of SDVs, due to varying local attitudes, reliance on employment in driving professions, and technological capabilities, as well as economic stability of the country. For example, despite there being a greater acceptance rate among Italian and Spanish citizens, the economic instability of both regions over the past decade has inhibited the integration of SDVs.

Employment barriers One of the main inhibitors to the acceptance of SDVs has been a concern around job security. There has been an increased concern in recent years about SDVs replacing taxi drivers, bus drivers, delivery drivers, and anyone dependent on driving as a profession. Many trade unions and organised workforces in these areas have petitioned and protested at the replacement of workers in these sectors. Animosity towards SDVs from these groups has led to isolated incidences of abuse towards SDV taxi managers, destruction of vehicles and protests outside Waymo headquarters in Mountain View, California.

Data protection and privacy barriers Since the creation of the General Data Protection Regulation (GDPR) in the EU in 2018 and the many controversial data leaks and privacy debacles over the past 7 years, there has been a heightened concern about data protection and privacy, which has inhibited SDV deployment. SDV developers have been trying to navigate between privacy and data protection on the one side, and the need for vast amounts of processing data for SDVs to function, on the other. After the first large fine of €50 million against Google back in 2019 from European regulators (Porter 2019), which created a snowball of large ICTs being heavily fined, there has been a strong fear in the industry about breaching the GDPR. The GDPR has sometimes proven to be a hurdle for SDV manufacturers selling into the EU market, whereas countries not abiding by this regulation have been able to develop their SDV data-dependent algorithms quicker.

Because SDVs are relatively new to the market, it has also been difficult to estimate user acceptance. In many reports, there is an expressed fear that others will have access to your data. Some organisations have long established protocols to ensure that users' privacy is protected when selling their SDVs (NADA 2018).

Legal barriers There has been a difficulty uniting cohesive legal analysis due to national differences on road traffic and transportation. One of these barriers has been determining accountability in cases of accidents. Some manufacturers have tried to keep accountability in the hands of the driver, keeping SDVs at level 3 automation. However, this has also prompted some manufacturers to take full responsibility in order to promote trust in their vehicles. The different levels of accountability have led to some confusion in the insurance industry about how to deal with accidents and how to define culpability.

Ethical, Legal, Social and Economic Impacts of SDVs Between 2019 and 2025

Ethical Impacts

Safety and prevention of harm Over the years, there has been a great deal of discussion about whether non-autonomous driving should be banned when we reach a level where SDVs can safely and easily replace non-autonomous driving. When SDVs become used so prevalently, it begs the question whether non-autonomous vehicles should be banned for safety reasons (Nyholm 2018, p. 6). Because the rollout of SDVs has been so slow, this has not been a pressing question, thus far, but the US and UK road safety authorities have indicated that this is an inevitability within the next 50 years. Meanwhile, groups such as Humans Against Autonomous Vehicles (HAAV) have strongly opposed SDVs because they are not safe enough to drive and are just "glorified smartphones".

Since SDVs were first developed, the issue surrounding the vehicle's decision-making in unavoidable crash situations has been widely discussed. There have been several ethical guidelines and best practice documents established to assist SDV programmers in developing ethically-sound crash algorithms. However, these guidelines have been criticised as being too vague, incoherent, and ineffective at addressing some of the most problematic issues with SDVs, namely, developing driving algorithms for crash scenarios. Very few people would buy an SDV if they prioritised the lives of others over the vehicle's driver and passengers, but if they only aim to protect the driver, they may crash into children or light vehicles, instead of other cars, walls, or lampposts (Contissa et al. 2017, p. 67).

If safety is prioritised, they may swerve towards a motorcyclist wearing a helmet, as opposed to one without a helmet, because they would be more likely to survive in a crash (De Sio 2017, p. 425). If algorithms target those less at risk, then people may start to take unsafe activities in order to become safe, i.e. cycling without a helmet so that SDVs view you cautiously, thus avoiding collision (Johnsen et al. 2017, p. 42). SDV manufacturers have taken different approaches to these problems, with some attempting to constrain SDVs at level 4 automation to areas that prohibit non-autonomous vehicles, because the uncertainty of non-autonomous driving is one of the biggest risks to SDV driver safety. SDV manufacturers have been aware of the problematic issues with crash algorithms, but have rarely addressed this issue head-on, consistently stating that their SDVs will reduce crashes in the long-term, while implicitly conceding that there will be some degree of accidents and issues along the way.

Autonomy The criticism that programmed responses remove control from the human being in driving circumstances has gained significant prominence in debates on SDVs. We lose the choice and ability to make our own decisions in the car's navigation. 2021 witnessed much greater concern about individual autonomy in SDVs, as there were cases in China where the vehicle took control from the driver in non-automation mode. In cities where level 4 automation is in place, there have been personal accounts of individuals feeling a loss of control in these vehicles. In other instances, SDVs have been programmed to abide by speed limits and rules of the road, thus removing driving freedoms. In California recently, a pregnant woman went into labour and had to be rushed to hospital, but there were considerable delays because of the SDV's speed limit regulation, which almost resulted in delayed birth injuries for the new-born.

Responsibility There has been a concern that SDVs are threatening our free will and moral responsibility, because of an overreliance on algorithms and artificial intelligence in SDVs (CNIL 2017, p. 26). There has been a worrisome trend towards shifting responsibility by autonomous vehicle owners, preferring to alleviate themselves from responsibility by driving in autonomous mode. During several interviews in recent years, many drivers said that they were not responsible for the actions of their SDV and subsequently should not be held accountable or liable for accidents involving their vehicle.

Rights Policymakers have identified that while SDVs open the possibility for more people to use them (such as the elderly, disabled, and blind), it also poses the challenge of who one does deny the right to use them. As of now, countries are still following non-autonomous driving policies in relation to driving capacity, as most still require level 3 automation, but once they reach widespread level 4 and 5 rollouts, more groups will benefit from them.

Insurance and discrimination Now that cars can retrieve a wide array of driving habits, patterns, and behaviours, it means that if insurance companies gain access to this information, insurance could be tailored to meet individuals' driving performance. While being heralded as a positive move towards providing better insurance premiums to safer drivers, others have proposed that it would infringe on people's sense of privacy, with the feeling of constantly being monitored in the vehicle. Others have disavowed it because of the imbalance in insurance between manual cars and SDVs-namely, that insurance companies will provide better conditions for SDV drivers who allow their data to be monitored by insurance companies, to the disadvantage of non-SDV drivers.

Privacy As a result of the large amounts of data retrieved from SDVs, policymakers have had to identify methods to ensure privacy and data security and if a SDV is breaking the law, whether the police should be allowed to hack it or not. Regulators have determined that strong levels of encryption, anonymization and aggregation need to be implemented to protect the individual's personal data. Many automobile manufacturers are promoting their DRIC "data remains in car" compliant approach (CNIL 2018), which attempts to process data within the car, rather than being transmitted to different service providers or third-parties. This has been recommended since late 2024, but manufacturers have found it technically challenging to abide by.

Legal Impacts

There are global concerns about how long data should be stored; where it should be stored (e.g., on the car's hard drive, the manufacturer's cloud platform or an independent cloud platform); who should be granted access to this data; under what conditions; what happens to the owners data when they sell the car; how will the data be protected from being hacked; and who owns this data. So far, it has been difficult to have any international uniformity on these issues, except for EU-states, where there has been a much greater cohesion of legal frameworks on SDVs. This is expected to be a prime topic of concern in the third annual autonomous driving national leaders meeting later this year.

While driver and passenger data were the primary concern in 2024's international meeting, external vehicle data will be a predominant topic this year. Sensors collect information about the environment, which threatens bystanders' privacy. Because car companies are compiling mixed data (both personal and non-personal), it has been a little unclear how they are abiding by the GDPR and other regulations. In addition, they have also had to incorporate how they were securely and safely protecting privacy in accordance with ePrivacy Regulations (ePR) created to ensure that automotive companies abide by its guidelines. The European Automobile Manufacturers' Association (ACEA) and the Council of the European Union have been paramount for ensuring that these governments implement the ePR and that those working in the industry follow the recommendations outlined (ACEA 2018).

Cyber-security People have been fearful that SDVs will be easily hacked because of the abundance of digital infrastructure required for them to work. Criminals have been making explicit use of the data that they retrieve, hacking the vehicle and getting it to perform actions the user is unaware of, unable to undo, and maliciously causing harm to the individual(s) in the car (Bowles 2018). If cyber-criminals take over a vehicle, they can cause minor nuisances, such as opening and closing windows, or they can create greater threats such as disabling the car's functionality to read stop signs, maliciously causing vehicles to crash and harm its passengers, or using SDVs for terrorist purposes, such as transporting and detonating remote-controlled bombs. While there is a greater need for transparency from car manufacturers, there is the problem that cars will become more vulnerable as a result. So far, there have been only a few minor issues related to cyber-security, such as the case in London where attackers found weaknesses in SDVs through crypto malware and were able to extort money from the passengers before releasing control of the vehicle. However, these were isolated incidents and most of the cybersecurity insecurities have been identified by grey-hat hackers before malicious incidences occurred.

There has been a greater emphasis on strengthening counter-measures to avoid these situations. For example, in January 2025, UK police were granted the ability to take over cars that are hacked or under control for malicious purposes. This was done using Decentralised Environmental Notification Messages (DENM), which are messages exchanged between peer-to-peer SDVs and their digital infrastructures (Article 29 Data Protection Working Party 2017, p. 3). If there are abnormalities, DENM sends messages that indicate that the vehicle has been hacked to the police, through certification and Public Key Infrastructure (PKI) architecture (Article 29 Data Protection Working Party 2017, p. 4). However, these anomaly-based detection methods can identify a lot of attacks, but miss others, so there have been developments towards remote attestation methods, which check protocols before granting access to services (Kylänpää 2017). If there are abnormal issues addressed during this process that indicate potential hacking, this is relayed to the Police ICT Departments for further testing before intervention.

Liability At levels 0-2 automation, it has always been clear that, legally, the driver is responsible for the car's behaviour. SDVs become a liability issue at levels 3 and 4, because of the uncertainty of who is responsible in cases of accidents. It is very important, under law, to identify who is responsible for the vehicle and under what circumstances. So far, some traditional insurance companies have established insurance policies for SDVs, with premiums at the same rate as non-autonomous vehicles, unless the driver grants them access to their SDV data. Since 2020, some of the main issues relating to SDV liability have been:

Determining accident liability if the driver can concentrate on tasks other than driving. For example, if the car is in self-driving mode and the driver is reading, but needs to quickly take control of the wheel, and fails to do so in time, should the driver be held accountable? So far, manufacturers have largely claimed responsibility for crashes at level 4. However, at level 3, manufacturers have tried to place liability with the driver, but the nature of our cognitive setup disallows an exact and smooth transition between vehicle and driver in these situations. While drivers are not permitted to do other activities that would prevent them from taking control of the wheel during level 3 automation, the transition between the autonomous system and the human agent has been problematic to determine liability. There have many court cases that resulted with the driver facing diminished liability in these situations. There has been a greater push for manufacturers to progress to level 4 automation to overcome many of these issues.

In incidents where it has been reported to be the fault of the vehicle, it has been difficult to identify when and where the reported malfunction on the vehicle occurred, making it a challenge to identify liability. There is an increased impetus to improve malfunction detection, and several manufacturers are implementing a range of on-board and external cameras and sensors to better determine time and origin of malfunction. However, this has been met with some hostility from passengers, worrying that they are losing their privacy and freedom within the vehicle. Manufacturers attest that this data will only be retrieved in cases of accidents and at the discretion and consent of the driver and passengers.

There have been problems determining liability in situations where drivers activate the car when they should not have, or they do not take over control when requested. For example, there was one notable case in 2023 in Canberra, Australia, where a driver did not take over the wheel when prompted and was criticised for actively allowing the vehicle to crash to remove himself from potential liability. This has been a problem for SDV manufacturers and has led some companies to try to bypass level 3 automation to overcome this issue.

Problems have arisen when the vehicle and driver have reacted at the same time, which has created problems for determining liability. For example, there was a situation in Seoul last year (May 6th, 2024), where the driver turned the steering wheel to the right to avoid a collision, but the SDV tried to veer left to avoid the collision. Both actions led the vehicle to crashing into the car in front. Luckily, nobody was badly injured, and the manufacturer admitted responsibility after reviewing the driver's on-board footage because it was not clear that the vehicle should take control. Manufacturers have begun implementing audio and visual signals to demonstrate the car's actions to minimise these types of incidents.

In a Volvo SDV test-drive in December 2021, there was thick fog on the road, impeding the car's object recognition sensors. A dog came onto the road, but the SDV's was unable to detect it on time and the car skidded out of control into a bollard nearby. The driver would have seen the dog earlier, with the use of his high-beam fog lights and could have avoided the crash. SDV sensors has progressed immensely over the past few years, but manufacturers still state that there will be occasional glitches in difficult driving conditions, which has forced companies to limit automation in difficult driving conditions, such as heavy fog and snow.

It is difficult to determine if the driver is liable in situations where their SDV breaks the law, if the driver is not required to monitor the vehicle's actions. So far, in the locations where level 4 vehicles have been integrated, manufacturers state that they are strictly following local laws and rules of the road, and this issue has not yet materialised.

Social Impacts

Joy of driving For many, SDVs threaten to take away one of the primary pleasures of vehicles-the joy of driving itself (Kemp 2018). While for some driving is a necessary ordeal that must be endured, for others, it is a form of pleasure in itself: a sense of control, a form of relaxation, a sense of adventure, and a connectedness with their surroundings, that is being threatened by SDVs. 2022 and 2023 saw a rapid increase in the number of driving enthusiast affiliations attempting to ensure SDV safety does not force their non-autonomous vehicles off the road. There is a conflict between those who promote the reduced numbers of traffic deaths and those who want to protect their right to drive.

Gender differences Many years ago, the BRAVE Project was one of the first to highlight that there are different perceptions about SDVs between men and women. Men have had less worry about embracing SDVs, while women have been less enthusiastic and more fearful about their safety (Johnsen et al. 2017, p. 27). Men have been buying SDVs at a greater rate than women, with an approximate 60-40 split in SDV usage. Manufacturers are investing in female-focused SDV advertising.

Inclusion SDVs hold the potential to reduce inequalities and promote inclusion amongst drivers by allowing certain groups (senior citizens, non-drivers, and people with disabilities) access to automobiles that were limited, or unable to, previously (Johnsen et al. 2017, p. 56). However, because of our current low levels of automation, this has yet to materialise, although many of these groups have indirectly benefitted from the use of SDV ride-hailing.

Travel behaviour and demands It is still unclear if total travel miles increase as a result of improved comfort, ease of travel, and the ability to multitask while in the vehicle. So far, the limited integration of SDVs indicates that people travel more often as SDV use reduces many of the stresses found in traditional driving. In addition, fuel costs have been decreasing in five of the seven cities where level 4 automation has been implemented, because of more efficient driving, while the other two cities showed no change. In the past, it was assumed that insurance costs for SDVs would decline with a lower number of accidents. However, insurance companies are still dubious about the safety of SDVs and have kept insurance costs mostly the same as for non-autonomous vehicles, unless drivers can prove their safe driving through their SDV data. While SDVs initially had a higher number of accidents per mile than traditional cars, this was simply because they were in such early stages of development.

Decreased urbanisation What has been happening in some cities is that people are beginning to rent further away from the city centres because of the ease of commuting and reduced costs of running their SDV. There is less of a need to live in cities, which have started to see a reduction in urbanisation, allowing for a more evenly spread out population throughout the region. It has started to take some of the strain off amenities and busyness of very congested cities, while also potentially eliminating the need for so many parking spaces in the future, when SDVs can be used throughout the day (Lubell 2016).

Environmental There is an uncertainty about whether SDVs are ameliorating or exacerbating congestion levels. So far, people with SDVs have increased their overall travel time because they see it as less of a burden. Early signs indicate that increased efficiency of SDVs is reducing carbon emissions more than non-autonomous vehicles. SDV developers have been trying to walk the tightrope between ensuring their vehicles are environmentally-sustainable and having economically-affordable vehicles. Some manufacturers have placed a greater emphasis on emission reductions with the foresight that governments are implementing harsher penalties for poorly performing vehicles.

Economic Impacts

Job-losses In the past, there were concerns that SDVs would lead to job losses for 'taxi drivers, parking attendants, valet parkers, car mechanics, meter attendants, traffic officers, and potentially bus and freight drivers' (Lari et al. 2015, p. 758). There was also the issue that there were not enough people to drive trucks in places such as Canada (CBC News 2018), so truck manufacturers, such as Mercedes, noticed this trend and capitalised on autonomous trucks (Mercedes-Benz 2018). They have been testing level 5 trucks in locations where it is too dangerous or unsuitable for humans to drive, since Autumn 2023. Uber also saw that many of its drivers could become unemployed because of SDVs, so they have created computer science, engineering, and maintenance programmes for those interested in upskilling and transitioning professions (Engelbert 2017).

Competition As a result of the large investments and technological capacities of SDV development, we have seen several smaller automotive companies beginning to dissolve because they will be unable to compete with these giants going forward. While SDV start-ups flourished in the early infancy stage, the larger players have started outcompeting them with innovation, thus minimising the competitive market of SDV manufacturers.

Luxury vehicle business Some of the luxury vehicle manufacturers were worried about how SDVs would impact their business models, especially if driving were solely relegated to a hobby. However, some manufacturers have flourished through this period, with Audi and Mercedes taking leading roles in the SDV market (Autotech 2018). However, companies such as Ferrari, Lamborghini and Lexus are trying to re-market their vehicles and are increasing investment into their 'drive for fun' initiatives and racing tracks.

Digital divide SDVs are very expensive, which has limited ownership to rich people (Oliver et al. 2018). It is difficult for poor people to drive SDVs and may become problematic when it becomes the prevalent form of transportation. There are concerns that the increased safety of SDVs may cause non-SDVs to be unsafe and eventually prohibited from being sold, limiting people to more expensive SDVs.

Cost reduction: In the past, it was suggested that SDVs would cause insurance and energy costs to decrease, but we have only witnessed minor changes. While SDVs are hailed as safer, which should have reduced insurance costs, this has still not materialised in practice-insurance costs on SDVs have not become cheaper than non-automated vehicles.

Road infrastructure There has been a lot of debate over whether governments should maintain existing infrastructure or start implementing a more digitised infrastructure to accommodate for SDVs (Peters 2017). So far, SDVs have had to develop to understand human signs, rather than digital signs. Furthermore, there has been a public outcry about governmental investment in SDV infrastructure, with many claiming that it should be partly funded by auto companies. In late 2024, demonstrations in France and Germany called on SDV manufacturers to help cities pay for SDV infrastructure in the coming years.

Law enforcement income There has been a concern, in London and Mountain View, California, that SDVs will impact income generation of law enforcement. With more law-abiding vehicles, there has been a marginal and slow reduction in speeding and illegal parking. While more law-abiding vehicles is obviously a good thing, it still means a lost form of revenue generation by the police (Marshall and Davies 2018).

Electricity and power While SDVs have been powered by a mix of electric and traditional fossil fuel, there has been a strong push by many governments to switch to all electric. For example, the UK government stated back in 2018 that more than half of all vehicles on the road should be electric by 2030 (Harrabin 2018). While this is nowhere near achievable in the next 5 years, electric SDV use is being hailed as the way forward to reduce our automobile dependency on fossil fuels.

Mitigating Negative and Accentuating Positive Impacts of SDVs Between 2019 and 2025

As far back as 2019, there have been many different actions to mitigate negative impacts, while accentuating the positive impacts, of SDV technology, through national, international and supranational legislation and policy. One of the ways this was achieved was through national standardisation protocols between policy-makers, auto manufacturers, computer scientists, and transportation agencies. Standardisations have been created to ensure sufficient cyber security capabilities for SDVs are developed and implemented; minimum requirements established for the use of sensor technology; safety levels have been incorporated into earlier vehicle regulations to include hardware standardisations; and there have been several layers of enforced testing for different levels of vehicle automation.

National governments have implemented an array of different measurements and regulation to ensure that safety standards are being met. Many countries have heavily invested in their own independent testing, as there were several concerns related to scientific bias in manufacturing testing. In doing so, the US, Canada and Japan have created a greater transparency around SDV regulation. In total, 65 countries have developed their own SDV driving tests and licensing laws, while also enforcing safety regulations for manufacturers to demonstrate that these vehicles are safe to drive prior to being sold.

There have also been strengthened measures to inform the public about SDVs, how they function, and how non-autonomous drivers should interact with them on the road. This has led to a greater public trust, in conjunction with a large increase in media public awareness campaigns from car manufacturers. There has been a greater emphasis placed on the benefits retrieved from the big data of SDVs, but strict procedures and guidelines have been instituted to ensure personal data is anonymised and encrypted in accordance with GDPR, which has been a milestone for privacy protection over the past 7 years.

The automobile industry has had to adapt its earlier approach to the design process of their vehicles, with a greater emphasis on responsible innovation and value-sensitive design. The increase in ethical evaluations of SDVs resulted from state-supported initiatives and the establishment of oversight bodies, such as the UK's Centre for Data Ethics and Innovation, and Singapore's AI Ethics Council. Manufacturers have also had to increase transparency, while also providing guarantees for the life-span of their vehicles. Free software upgrades are mandatory for a 5-year period with all SDVs sold in the US, Canada, the EU, the UK, China, South Korea and Japan.

Where software updates occur on a regular basis, manufacturers have provided extensive guidelines about these requirements. SDVs have a built-in locking system that will prohibit drivers from using the cars unless their systems are updated. The cars also have clear and purpose-driven maintenance notification for drivers. Depending upon the seriousness of the maintenance, vehicles may prohibit drivers from operating. There has also been collaboration and agreement through the SDV Fair Use Initiative (SDVFUI) to ensure fair sharing of intellectual property for increasing safety in vehicles.

Since 2023, it has been evident that incorporating more digital infrastructure on our roads would be beneficial for the successful implementation of SDVs. While we are still in early stages, SDVs could be used more optimally with improved digital and physical infrastructure. Civil society organisations have been decrying the possibility that all citizens will have to pay extra for those making the change to autonomous driving, when they are not the ones benefiting from them. Policymakers have been negotiating with SDV manufacturers and owners about paying higher taxes to fund the infrastructure required to accommodate SDVs.

Steps Towards a Desired Future and Avoidance of an Undesired Future

This scenario has outlined many different issues, risks, and possibilities of SDVs in the year 2025. It is very important to reflect on some of the situations found in the scenario, and highlight those that are desirable by 2025, those to be avoided, and how to go about doing this. National, international and supranational institutions should be responsible for ensuring that citizens are protected from the over-eagerness of manufacturers to put their vehicles on the road. What became evident in the development of this scenario is that some of the main facilitators for SDV manufacturing will come from market, economic, and efficiency incentives (facilitators section); this should not jeopardise the safety, security, and employment of citizens (inhibitors section). Therefore, the SDV industry needs to be well regulated to ensure the safety of their vehicles through the effective implementation of SDV regulatory institutions.

In the privacy sections (in terms of both ethical and legal impacts), it was shown that under regulated SDV development has the potential to have negative ethical impacts on the privacy of drivers, passengers, pedestrians, and road-users, while also having legal implications in terms of data protection and privacy law. These sections demonstrate that there needs to be adherence to current regulations for the effective control of data generated, retrieved and used by SDVs. Clear delineations need to be established about what constitutes essential data for the vehicle's mobility and if this contains personal and private information. There needs to be clear indication that if essential data contains personal or private information, then it should be strongly anonymized, aggregated, and secured, to protect the privacy of drivers, passengers, and other road users. If it is non-essential data, then there should be adequate policies to ensure that they are not retrieved or stored as a result of using an SDV, unless explicit and informed consent is given.

As was made clear in the legal impacts section of this scenario, European governments need to effectively integrate the tenets of the GDPR into the automotive industry to effectively assure citizens that their personal data will be protected if they use SDVs. Automobile manufacturers have the responsibility of identifying the purposes for which the car collects data in order to demonstrate their compliance with data protection law. For instance, there needs to be careful analysis if this data will be used for advertising, customised pricing, or to sell additional products to the car owner, and either ensuring the owner is aware of these, and consent to it, or prohibit use of data in this way, altogether.

As also brought out in the scenario, there is a current legal concern that SDVs will be used for malicious, illegal, and fraudulent purposes. SDVs may threaten the safety and security of passengers, pedestrians, and cities if controlled or hacked my criminals or terrorists. Therefore, data collected within the vehicle may become important for law enforcement officials in situations where SDVs are hacked or used for malicious purposes. Police authorities should be allowed to identify illegal SDV activity, as long as it does not infringe upon the privacy of innocent citizens (cybersecurity section). Methods such as DENM, certifications, cryptographic signatures, and attestation methods require heavy investment by automotive companies and need to be fit for purpose; which illustrates the tensions discussed in the paper between safety and economic concerns. There needs to be careful statutory regulation, third-party testing, and planning for the security of these technologies, which may inhibit the speedy deployment of SDVs (inhibitor), but which will ensure greater security of these vehicles (facilitator).

One of the main social and economic issues imagined for the future will be the challenge of transitioning between non-autonomous vehicles and autonomous vehicles. A key foreseeable issue will be the adequate implementation of digital infrastructure to accommodate SDVs. Therefore, policymakers will need to ensure that there is a smooth transition between traditional infrastructure and the digital infrastructure of the future. For the foreseeable future, SDVs will have to use our current road signs lights and markings to navigate on roads. However, these may eventually be replaced by 'digital infrastructure'. While this is not likely to transpire by 2025, governments and companies should already be preparing for this transition as it will be one of the most costly and time-consuming processes in the materialisation of widespread SDVs usage.

While it was imagined that SDVs will offer the potential to reduce our ecological impact, reduce traffic and congestion, and improve driving-times (social drivers), there is nevertheless also the possibility that they will be used more often because of ease of use (social impacts). Therefore, it makes sense that policymakers would need to take careful steps to ensure that they are not 'overused', thus reversing many of their social and environmental benefits. Policymakers may be able to do this is through greater investment into SDV public transportation systems to ensure convenience, cost and energy reductions. This may also have the positive social impact of preventing poorer citizens from being excluded from the transportation system-the digital divide issue, highlighted in the economic impacts section of this scenario. Furthermore, careful attention must be placed on ensuring more inclusionary SDVs, especially when they reach level 4 and 5, especially the elderly, handicapped, and those who cannot drive, can be granted accessibility to SDVs, in an attempt to reduce many rights-based issues when it comes to automobile usage, as discussed in the ethics section of this paper.

Conclusion

SDVs are set to dramatically transform the way we live in the future, but it is often not abundantly clear when in the future these transformations will happen, or, relatedly, the future is conceived as being too distant to have significant enough urgency for policy considerations and implementation now. This paper presents the policy-focused scenario approach which was developed as one way to evaluate these emerging concerns. Scenarios are useful for planning and mapping the potential trajectory of emerging technologies and provide us with insightful visions of what the future may hold and how policymakers can either put steps in place for a desirable future. The approach we report on in this paper demonstrates a variety of inhibitors and facilitators of SDV development by the year 2025, indicating concerns and methods for policymakers to take into account in the coming years. The scenario also highlighted many of the most current and widely discussed as well as likely ethical, legal, social and economic impacts of SDVs in the coming years and steps and procedures that should be taken to reap the benefits, while curbing the unwanted outcomes, of these emerging technologies. While scenario planning is not an exact science, it provides policymakers with additional insights of how their actions now will affect the future development and use of emerging technologies, such as SDVs.

Acknowledgements

A sincere thank you goes out to all of the people who were involved in the self-driving vehicle scenario at the University of Twente on September 25th 2018 and to everyone who contributed and made comments about the further drafts and iterations of the project's scenario, which has culminated in this paper.

Funding

This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme Under Grant Agreement No. 786641.

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University of Twente, Enschede, The Netherlands

Mark Ryan

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Correspondence to Mark Ryan.

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Ryan, M. The Future of Transportation: Ethical, Legal, Social and Economic Impacts of Self-driving Vehicles in the Year 2025. Sci Eng Ethics 26, 1185-1208 (2020). https://doi.org/10.1007/s11948-019-00130-2
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DOI: https://doi.org/10.1007/s11948-019-00130-2

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Keywords

Ethics of self-driving vehicles

Self-driving cars

Artificial intelligence

Big data

Philosophy of technology

Scenario foresight analysis

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The Future of Transportation: Ethical, Legal, Social and Economic Impacts of Self-driving Vehicles in the Year 2025 - Science and Engineering Ethics

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Self-driving vehicles (SDVs) offer great potential to improve efficiency on roads, reduce traffic accidents, increase productivity, and minimise our environmental impact in the process. However, they have also seen resistance from different groups claiming that they are unsafe, pose a risk of being hacked, will threaten jobs, and increase environmental pollution from increased driving as a result of their convenience.

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