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Safety assurance is a critical yet challenging aspect when developingself-driving technologies. Hamilton-Jacobi backward-reachability analysis is aformal verification tool for verifying the safety of dynamic systems in thepresence of disturbances. However, the standard approach is too conservative tobe applied to self-driving applications due to its worst-case assumption onhumans' behaviors (i.e., guard against worst-case outcomes). In this work, weintegrate a learning-based prediction algorithm and a game-theoretic humanbehavioral model to online update the conservativeness of backward-reachabilityanalysis. We evaluate our approach using real driving data. The results showthat, with reasonable assumptions on human behaviors, our approach caneffectively reduce the conservativeness of the standard approach withoutsacrificing its safety verification ability.

Negotiation-Aware Reachability-Based Safety Verification for AutonomousDriving in Interactive Scenarios