The act of driving always carries some level of risk. With the introduction of vehicle automation, it is probable that computer -driven vehicles will assess this changing level of risk while driving, and make decisions as to the allowable risk for itself and other road users. In certain situations, an automated vehicle may be forced to select whether to expose itself and its passengers to a small risk in order to protect other road users from an equal or greater amount of cumulative risk. In legal literature, this is known as the du ty to act. The moral and legal responsibilities of an automated vehicle to act on the behalf of other road users are explored. INTRODUCTION Road vehicle automation has progressed rapidly in recent decades. Many technologies designed to assist humans by a utomating aspects of the driving process are in today’s vehicles, including anti-lock brakes, cruise control, stability control, and lane keeping. Today’s prototype vehicles employ 360 degree sensing (1) and utilize probabilistic models to anticipate the movements of nearby vehicles and plan accordingly (2). The constant monitoring of the environment and quicker reaction times of computerized control are expected to significantly reduce the es timated 93% of crashes that are the result of human error (3, 4) . Several automakers insis t that the human driver will ultimately be responsible for monitoring the vehicle and the roadway, and in the event of a crash, the driver will be available to take control with little notice. This is the scenario described by the National Highway Safety Administration for le vel 2 automation, with levels 3 and 4 allowing the driver significant warning before returning to the driving task (5). Yet recent research suggests that it is unrealistic to assume that a human passenger can effectively monitor an automated vehicle’s . On test tracks, passengers in automated vehicles spend more time looking away from the road than those in traditional vehicles (6). In the absence of monitoring by its human driver, an automated vehicle could attempt to predict dangerous situations and alert the driver in advance. This is difficult t o implement in practice, as humans may require substantial warning time . Drivers in simulators exhibit excessive swerving, braking, and failure to check blind spots when given seven seconds of warning to respond to a stopped vehicle after riding in an auto mated vehicle (7). Stanford’s Sven Beiker has mentioned studies where blindfolded drivers in automated vehicles required five to six seconds to assess the situation when the blindfolds were removed, and up to a minute before their driving matched an un -blindfolded control subject (8). No algo rithm can Noah J. Goodall, Research Scientist, Virginia Center for Transportation Innovation and Research 530 Edgemont Road, Charlottesville, VA 22903, noah.goodall@vdot.virginia.gov -2- possibly predict with certainty the movements of other vehicles, pedestrians, cyclists, or wildlife six seconds int o the future. In many potentially dangerous situations, the computer must take evasive action without waiting on the approval of its human driver. In regular operations, h ighly -automated vehicles use path planning algorithms to develop and select from a set of candidate paths (1). A similar strategy can be used in near -crash situations to determine the best way to avoid collisions. In most situations , selecting the safest path should be straightfo rward, as the best path is one with an excepti onally low probability of collision. In situations with a great deal of uncertainty or when a crash is unavoidable, the decision may become much more complex. Given a choice between emergency braking to avoid a deer in the road, techniques from risk analys is can be used to balance the uncertainty of a choice (e.g. the deer may move on its own, or the following truck driver may not notice the sudden braking in time to stop) with the severity of its possible outcomes. When a crash is unavoidable, crash severi ty models could be used to determine the safest alternative. This assumes that the goal is to minimize the cumulative injury, expressed quantitatively . Using this metric exclusively leads to some morally ambiguous results. One example that has been provided is that when given a choice between colliding with two different vehicles, an automated vehicle may consistently choose the SUV with which it has a better crash compatibility (9) rather than the sedan, or worse may consistently collide with the “safer” helmeted motorcyclist over the non -helmeted rider (10). These gray areas of decision making in vehicle automation can be addressed using the language of morals and ethics. Previous work has discussed the need for a moral component in an automated vehicle’s path planning algorithms (11, 12) , as well as the challenges of encoding a moral system in software (10). An espe cially difficult problem concerns when a n automated vehicle has the opportunity to rende r aid, by subjecting itself to a small risk in order to protect another roadway user . This can occur either by taking some action or , as in the example discussed in the next section, by refraining from an action. For example, should an automated vehicle, w ithout the permission of its owner, intentionally collide at low -speed and with miniscule risk of injury in order to protect a more vulnerable pedestrian? An advanced automated vehicle will likely be aware of its environment and continuously calculating ri sk, and may be able to anticipate these types of situations more often than humans. Because it is software -based, its actions in these rare but important situations can be dictated ahead of time, with careful thought and consideration. In the following sections, the obligation of an automated vehicle to the safety of other road users, referred to in legal literature as the affirmative duty to act , is examined from legal and moral perspectives. THE MORAL DUTY TO ACT In a recent m agazine article, a vehicle automation engineer describe s a crash where a distracted driver failed to notice stopped traffic ahead and collided with a stopped vehicle . The author of the article concludes that if the distracted driver’s vehicle had been automated, “it would have seen the obstruction three cars ahead. It would have calculated the distance to impact, scanned the neighboring lanes, realized it was boxed in, and hit the brakes, all within a tenth of a second,” thus avoiding the crash entirely (13). -3- Changing the circumstances slightly raises a new problem. Assume that the distracted driver is driving himself in vehicle A , while the leading driver is in an automated vehicle B which is not boxed in. In the original example, B was struck at thirty miles per hour, with enough force to c ollide with the vehicle in front (13). If B were inste ad automated with the ability to sense three cars ahead and behind, to calculate distance to impact, and to scan neighboring lanes, B could realize a collision was imminent and pull into an adjacent lane , avoiding the distracted driver entirely . See Figure 1 for a representation. Figure 1. An impending collision between a distracted driver (A) and an automated vehicle (B). This avoids one crash , but creates a new, possibly more dangerous crash. If the distracted driver remains distracted, he may continue through the crosswalk, striking the pedestrian. Even if the driver begins to brake, at 30 miles per hour he requires 40 feet to stop in dry conditions, and may still strike the pedestrian. Yet if the automated vehicle had remain ed in place, it may have absorbed much of the impact and spared the pedestrian. While a 30 mph collision with a passenger car is certainly dangerous, it is far safer within a modern vehicle than without. This configuration creates several new problems. Should the automated vehicle be allowed to move into the adjacent lane? Does the rule change if the automated vehicle is empty? If the same situation occurred, except that the automated vehicle began in the adjacent lane but had the opportunity to block the path of the oncoming vehicle, is it obligated (or permitted) to do so? These questions are considered briefly from the perspectives of US common law and ethics. Legal Perspective There are many laws that may affect the example collision scenario describe d in the previous section , including roadway laws and product liability . The scope of this paper is limited to the affirmative duty to act under common law in the United States . For a more detailed analysis of the legality of automated vehicles, see Smith (14). For a discussion of the legal issues surrounding autonomous agents generally, see Chopra and White (15). Because there is no literature on the duties of automated vehicles, this section assumes that vehicles will be held to the same standards as a reasonable person in regards to the duty to act . The affirmative duty to act refers to one’s responsibilities to assist another in an emergency , and is classified as a type of negligence. In general, there is no duty to act affirmatively according to common law. First year law students are often surpr ised to learn that there is no duty to act unless the bystander is one of the following: contractually obligated to rescue (e.g. a life guard) , in a special relationship with the victim such as a parent -child , responsible for the predicament , or subject to a special requirement such as mandatory reporting . Put simply, as long as the drowning vic tim is not your child, you didn’ t push them in, and you ’re not a -4- lifeguard, you can legally walk away because you don ’t want to get your feet wet. There is even a disincentive to assist —one who voluntarily begins a rescue assumes the duty to continue the rescue , as they may have prevented others from initiating a second rescue . From Figure 1, Vehicle B has no duty to protect the pedestrian from harm unless they are determined to have a special relationship. This has been acknowledged by many as a shortcoming of the legal system (16–18). (For a defense of no -duty-to-act laws, see Scordato (19)). Obviously it ’s asking a great d eal for a bystander to tolerate massive risk for a stranger, but to require absolutely no effort seems counter to society’s p