Risk-aware motion planning for autonomous vehicles with safety specifications

Abstract

Ensuring the safety of autonomous vehicles (AV s) in uncertain traffic scenarios is a major challenge. In this paper, we address the problem of computing the risk that AV s violate a given safety specification in uncertain traffic scenarios, where state estimates are not perfect. We propose a risk measure that captures the probability of violating the specification and determines the average expected severity of violation. Using highway scenarios of the US101 dataset and Responsible Sensitive Safety (RSS) as an example specification, we demonstrate the effectiveness and benefits of our proposed risk measure. By incorporating the risk measure into a trajectory planner, we enable AVs to plan minimal-risk trajectories and to quantify trade-offs between risk and progress in traffic scenarios.