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Imitation learning provides a way to automatically construct a controller bymimicking human behavior from data. For safety-critical systems such asautonomous vehicles, it can be problematic to use controllers learned from databecause they cannot be guaranteed to be collision-free. Recently, a method hasbeen proposed for learning a multi-mode hybrid automaton cruise controller(MOHA). Besides being accurate, the logical nature of this model makes itsuitable for formal verification. In this paper, we demonstrate this capabilityusing the SpaceEx hybrid model checker as follows. After learning, we translatethe automaton model into constraints and equations required by SpaceEx. We thenverify that a pure MOHA controller is not collision-free. By adding a safetystate based on headway in time, a rule that human drivers should follow anyway,we do obtain a provably safe cruise control. Moreover, the safe controllerremains more human-like than existing cruise controllers.

Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data