The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles

How safe should highly automated vehicles (HAVs) be before they are allowed on the roads for consumer use? This question underpins much of the debate around how and when to introduce and use the technology so that the potential risks from HAVs are minimized and the benefits maximized. In this report, we use the RAND Model of Automated Vehicle Safety to compare road fatalities over time under (1) a policy that allows HAVs to be deployed for consumer use when their safety performance is just 10 percent better than that of the average human driver and (2) a policy that waits to deploy HAVs only once their safety performance is 75 or 90 percent better than that of average human drivers — what some might consider nearly perfect. We find that, in the long term, under none of the conditions we explored does waiting for significant safety gains result in fewer fatalities. At best, fatalities are comparable, but, at worst, waiting has high human costs — in some cases, more than half a million lives. Moreover, the conditions that might lead to comparable fatalities — rapid improvement in HAV safety performance that can occur without widespread deployment — seem implausible. This suggests that the opportunity cost, in terms of lives saved, for waiting for better HAV performance may indeed be large. This evidence can help decisionmakers better understand the human cost of different policy choices governing HAV safety and set policies that save more lives.

Key findings