  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.
