Attack-Resilient Lateral Stability Control for Four-Wheel-Driven EVs Considering Changed Driver Behavior Under Cyber Threats

This article presents an attack-resilient lateral stability control design approach for four-wheel-driven electric vehicles to enhance the cyber-physical security of the steering system. Specifically, this work examines cyberattacks on the steering angle, which is considered one of the most safety-critical signals in vehicles. First, a robust predictive controller is designed to mitigate the impact of cyberattacks, which carries a low computational burden, making it applicable for real-time applications. Second, by developing a collaborative mechanism between the attack-resilient controller and the human driver's action, the proposed method considers the altered driver behavior during cyber threats, e.g., enlarged driver's neural response delay and muscle action delay, which, to the best of our knowledge, has not been examined before. Third, the lateral dynamics model and the driver model are verified by experimental results under a production vehicle, based on which the effectiveness and feasibility of the proposed attack-resilient control methodology are demonstrated.