Imitation learning provides a way to automatically construct a controller by mimicking human behavior from data. For safety-critical systems such as autonomous vehicles, it can be problematic to use controllers learned from data because they cannot be guaranteed to be collision-free. Recently, a method has been proposed for learning a multi-mode hybrid automaton cruise controller (MOHA). Besides being accurate, the logical nature of this model makes it suitable for formal verification. In this paper, we demonstrate this capability using the SpaceEx hybrid model checker as follows. We develop an automated tool to translate the automaton model into constraints and equations required by SpaceEx. We then verify that a pure MOHA controller is not collision-free. By adding a safety state based on headway in time, a rule that human drivers should follow anyway, we do obtain a provably safe cruise control. Moreover, the safe controller remains more humanlike than existing cruise controllers.
Safety Verification of a Data-driven Adaptive Cruise Controller
2020 IEEE Intelligent Vehicles Symposium (IV) ; 2146-2151
2020-10-19
1503309 byte
Conference paper
Electronic Resource
English
SAFETY VERIFICATION OF A DATA-DRIVEN ADAPTIVE CRUISE CONTROLLER
British Library Conference Proceedings | 2020
|Adaptive cruise control method, adaptive cruise controller, storage medium and vehicle
European Patent Office | 2024
|