Probabilistic Planning With Comprehensive Reactive Safety

The ability of autonomous vehicles to plan under uncertainty is crucial to their safe operation in urban environments. However, research remains sparse on planning methods that coordinate well with most modern motion prediction techniques using probabilistic trajectory modes as the uncertainty representation. In this work, we outline the desired properties of such a planner, and where existing methods fall short. Then, we present a probabilistic planning approach built on top of the Comprehensive Reactive Safety framework. This planner optimizes for driving utility in expectation, while ensuring collision constraints are satisfied for all potential prediction trajectories, regardless of their probabilities. Through a series of simulation experiments, the proposed planner is shown to be effective. It achieves safety in rare events, demonstrates strong progress-making ability in uncertain situations, and provides comfortable and efficient driving on average.