Raise the Playing Field
Reject the Status Quo
The reality that human drivers often violate rules of the road prompts speculation that programming automated vehicles to comply with these rules would reduce their appeal. Suggestions for addressing this perceived disadvantage have included expressly permitting automated vehicles to travel at or above the prevailing traffic speed and delegating decisions about speed or aggression to the human users of these vehicles.
Drivers, however, currently behave in ways that are neither lawful nor reasonable . They drive too fast for conditions, they follow other vehicles too closely, and they fail to yield the right of way to pedestrians. They drive while intoxicated or distracted. They fail to properly maintain their vehicles' tires, brakes, and lights. These largely unlawful behaviors occasionally result in crashes, and those crashes occasionally result in serious injury. This tragic status quo suggests that the current approach to traffic enforcement should be reformed rather than transferred to automated vehicles.
At this early stage in automation, transportation authorities would do better to optimize and then enforce rules of the road for all motor vehicles. Increasing the expectations placed on human drivers – by cracking down on speeding, texting, drunk driving, and other dangerous activities – could increase the appeal of automated vehicles at least as much as allowing those automated vehicles to speed.
Automated enforcement could be a key tool for increasing compliance. Such enforcement currently relies both on roadway devices (including speed and red light cameras) and on in-vehicle devices (including alcohol locks, speed regulators, and proprietary data recorders). Private entities such as fleet managers and insurance companies already provide some of this enforcement indirectly through private incentives. The potential proliferation of outward-facing cameras on vehicles and drones in the air might also facilitate increased public and private enforcement of rules of the road.
Increased enforcement could, on one hand, address equity concerns of discretionary enforcement and, on the other hand, raise privacy and liberty concerns. While these are important questions, a status quo in which laws are openly flouted even by the officers enforcing them is one that begs for reform.
Indeed, more consistent and comprehensive enforcement could create pressure for a careful evaluation of existing law. Better access to and analysis of location-specific information about the driving environment (including roadway geometry, pavement, traffic, and weather) could enable the precise calibration of dynamic speed limits. These dynamic limits might then be communicated to drivers through variable message signs and, in the future, vehicle-to-infrastructure communication.
Because reasonable speed also depends on the driver and her vehicle, posted limits might nonetheless have only limited utility. Pursuant to the basic speed law , a human driver should account for each of these variables implicitly and adjust her speed accordingly. Automated vehicles, however, may account for more of these variables explicitly – and reasonably.
Consider, for example, the common requirement that the “driver of a vehicle shall yield the right-of-way to a pedestrian crossing the roadway within any marked crosswalk or … unmarked crosswalk at an intersection, except as otherwise provided” . Although pedestrians may not create an “immediate hazard” by “suddenly” leaving the curb , the statutory obligation to yield does suggest one possible bound on vehicle speed.
Figure 27.2 Illustration of Vehicle Stopping
Imagine a driver traveling down a typical neighborhood street with a parking lane that provides 3 m between her car and the curb, as shown in Figure 27.2. Assuming that her view of the pedestrian is not blocked, what maximum speed will enable this driver to stop for any pedestrian who, at a walking speed of 1.4 m/s, steps from the curb into the street?
Although stopping sight distance depends on several vehicle, environment, and driver variables , this illustration simplifies these to consider only the driver's reaction time and the friction between the tires and the road surface. An average driver with good tires on a flat dry street might achieve a reaction time of 1 s and a subsequent deceleration rate of 5 m/s2, which implies a maximum speed of 20 km/h (13 mph). In contrast, a hypothetical automated vehicle reacting twice as fast and braking at 7 m/s2 could reach a maximum speed of about 40 km/h (25 mph), which is a typical residential speed limit today. In other words, if automated vehicles are traveling slowly on a road, perhaps conventional vehicles should be traveling even more slowly.
Reasonable speed is also an answer to some, though not all, of the ethical dilemmas popularly raised in the context of automated driving , . Positing a choice between killing one group of pedestrians and another, for example, fails to account for the possibility of negating the dilemma simply by driving more slowly. Slower speeds can increase controllability as well as reduce the magnitude of harm.
Speed is not the only relevant driver action. Tire condition, for example, is an important consideration in stopping distance, is at least nominally regulated , and yet varies widely within the current vehicle fleet. If the hardware on automated vehicles is expected to be regularly inspected, so too should the hardware on conventional vehicles. Moreover, driving imposes environmental costs that are not internalized by vehicle owners and operators . If automated driving proves to be more fuel efficient than human driving, a higher fuel tax would also incentivize automation.
In short, reform should seek to more closely align what is lawful with what is reasonable and to more closely align actual driver behavior with both . The expectation that both automated vehicles and human drivers should behave reasonably is itself reasonable and ultimately advantageous to automated driving.
-  initial speed = rate of deceleration * ((pedestrian speed / orthogonal distance from curb to car)– reaction time) = (0.5 * 9.8 m/s2)*(((1.4 m/s)/3 m) – 1 s) = 6 m/s = 20 km/h = 13 mph
-  initial speed = rate of deceleration * ((pedestrian speed / orthogonal distance from curb to car)– reaction time) = (0.7 * 9.8 m/s2)*(((1.4 m/s)/3 m) – 0.5 s) = 11 m/s = 41 km/h = 25 mph