Negotiation-Aware Reachability-Based Safety Verification for AutonomousDriving in Interactive Scenarios
This addresses safety assurance for autonomous vehicles in interactive scenarios, representing an incremental improvement over standard methods.
The paper tackled the problem of overly conservative safety verification for autonomous driving by integrating learning-based prediction and game-theoretic models to update backward-reachability analysis online, resulting in effectively reduced conservativeness without sacrificing safety verification ability as shown with real driving data.
Safety assurance is a critical yet challenging aspect when developing self-driving technologies. Hamilton-Jacobi backward-reachability analysis is a formal verification tool for verifying the safety of dynamic systems in the presence of disturbances. However, the standard approach is too conservative to be applied to self-driving applications due to its worst-case assumption on humans' behaviors (i.e., guard against worst-case outcomes). In this work, we integrate a learning-based prediction algorithm and a game-theoretic human behavioral model to online update the conservativeness of backward-reachability analysis. We evaluate our approach using real driving data. The results show that, with reasonable assumptions on human behaviors, our approach can effectively reduce the conservativeness of the standard approach without sacrificing its safety verification ability.