Cover: The Enemy of Good

The Enemy of Good

Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles

Published Nov 7, 2017

by Nidhi Kalra, David G. Groves

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至善者,善之敌: 近乎完美之无人驾驶汽车的等待成本估算 (内容摘要)

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Research Questions

  1. How safe should highly automated vehicles be before they are allowed on the roads for consumer use?
  2. Under what conditions are more lives saved by each policy in the short term and the long term, and how much are those savings?
  3. What does the evidence suggest about the conditions that lead to small costs from waiting for significant improvements prior to deployment?
  4. What does this imply for policies governing the introduction of HAVs for consumer use?

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

Results Suggest That More Lives Will Be Saved the Sooner HAVs Are Deployed

  • We used the RAND Model of Automated Vehicle Safety to compare road fatalities 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 (Improve10) and (2) a policy that waits to deploy HAVs only once they have reached significant performance improvements of 75 percent or 90 percent (Improve75 or Improve90).
  • We find that, in the short term, more lives are cumulatively saved under a more permissive policy (Improve10) than stricter policies requiring greater safety advancements (Improve75 or Improve90) in nearly all conditions, and those savings can be significant — hundreds of thousands of lives.
  • In the long term, more lives are cumulatively saved under an Improve10 policy than either Improve75 or Improve90 policies under all combinations of conditions we explored. In many cases, those savings can be more than half a million lives.
  • There is good reason to believe that reaching significant safety improvements may take a long time and may be difficult prior to deployment. Therefore, the number of lives lost while waiting for significant improvements prior to deployment may be large.

Recommendation

  • This evidence could help decisionmakers to better balance public skepticism with evidence about the human cost of different choices and to set policies that save more lives overall. Deploying HAVs when their safety performance is just better than that of the average human driver may be too permissive given social expectations about the safety of robots, machines, and other automated systems, but waiting for improvements many times over or waiting for perfection may be too costly. Instead, a middle ground of HAV performance requirements may prove to save the most lives overall.

This project is a RAND Venture. Funding was provided by gifts from RAND supporters and income from operations. The research was conducted by the Science, Technology, and Policy Program within RAND Justice, Infrastructure, and Environment.

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