Autonomous Vehicles Meet the Physical World: RSS, Variability, Uncertainty, and Proving Safety (Expanded Version)
This addresses safety-critical issues for autonomous vehicle deployment, but it is incremental as it builds on existing RSS frameworks.
The paper tackles the challenge of ensuring provable safety in autonomous vehicles under real-world variability and uncertainty, expanding the RSS model to handle edge cases in braking maneuvers and proposing a Micro-Operational Design Domain approach to improve permissiveness while maintaining safety guarantees.
The Responsibility-Sensitive Safety (RSS) model offers provable safety for vehicle behaviors such as minimum safe following distance. However, handling worst-case variability and uncertainty may significantly lower vehicle permissiveness, and in some situations safety cannot be guaranteed. Digging deeper into Newtonian mechanics, we identify complications that result from considering vehicle status, road geometry and environmental parameters. An especially challenging situation occurs if these parameters change during the course of a collision avoidance maneuver such as hard braking. As part of our analysis, we expand the original RSS following distance equation to account for edge cases involving potential collisions mid-way through a braking process. We additionally propose a Micro-Operational Design Domain (μODD) approach to subdividing the operational space as a way of improving permissiveness. Confining probabilistic aspects of safety to μODD transitions permits proving safety (when possible) under the assumption that the system has transitioned to the correct μODD for the situation. Each μODD can additionally be used to encode system fault responses, take credit for advisory information (e.g., from vehicle-to-vehicle communication), and anticipate likely emergent situations.