Safety Verification of Advanced Driver Assistance Systems Using Hybrid Automaton Reachability

Abstract

Advanced driving assistance system (ADAS) is effectively promoting the vehicular automation level and it is critical to ensure its functional safety. While existing analysis mainly focuses on individual functions of ADAS, safety violations in the overall system can be found by extensive road tests, which are not only costly in terms of time and money but also lack a formal safety guarantee. This is because tests may not cover all driving scenarios, especially the ones that involve function mode switching. In this paper, we focus on the longitudinal vehicle motion and provide a pipeline to perform safety verification for all the related ADAS functions. To that end, we specify safety constraints and boundaries for a vehicle’s longitudinal cruising and collision avoidance and validate a longitudinal dynamic model against the high-fidelity simulation software CarSim. Then we define hybrid automata to describe the closed-loop system composed of the vehicle dynamics and the ADAS. Finally, by computing the reachable sets of the hybrid automata and comparing them with the specified safety boundaries, the ADAS is verified. Numerical experiments demonstrate the efficacy of the proposed approach.

Qi Sun

Master of Cyberspace Security

My research interests include natural language processing, time series forecasting and big data analysis.