This report presents a framework for measuring safety in automated vehicles (AVs) that could be used broadly by companies, policymakers, and the public. In it, the authors considered how to define safety for AVs, how to measure safety for AVs, and how to communicate what is learned or understood about AVs. Given AVs' limited total on-road exposure compared with conventional, human-driven vehicles, the authors also consider options for proxy measurements — i.e., factors that might be correlated with safety — and explore how safety measurements could be made in simulation and on closed courses. The report identifies key concepts and illuminates the kinds of measurements that might be made and communicated. It presents a structured way of thinking about how to measure safety at different stages of an AV's evolution, and it proposes a new kind of measurement. While acknowledging that the closely held nature of AV data limits the amount of data that are made public or shared between companies and with the government, the report highlights the kinds of information that could be presented in consistent ways in support of public understanding of AV safety.

The research described in this report was prepared for the Uber Advanced Technologies Group and conducted by the Science, Technology, and Policy Program and the Justice Policy Program within RAND Justice, Infrastructure, and Environment (JIE).

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