Safety Verification of a Data-driven Adaptive Cruise Controller

Qin Lin, Sicco Verwer, John Dolan

Research output: Contribution to conferencePaperScientificpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Number of pages6
Publication statusPublished - 2020
Event31st IEEE Intelligent Vehicles Symposium, IV 2020 - Virtual, Las Vegas, United States
Duration: 19 Oct 202013 Nov 2020


Conference31st IEEE Intelligent Vehicles Symposium, IV 2020
Country/TerritoryUnited States
CityVirtual, Las Vegas


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