A Cyberattack Detection-Isolation Scheme For CAV Under Changing Driving Environment

Under a changing driving environment, a Connected Autonomous Vehicle (CAV) platoon relies strongly on the acquisition of accurate traffic information from neighboring vehicles as well as reliable commands from a centralized supervisory controller through the communication network. Even though such modalities are imperative to ensure the safe and efficient driving performance of CAVs, they led to multiple security challenges. Thus, a cyberattack on this network can corrupt vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, which can lead to unsafe or undesired driving scenarios. Hence, in this paper, we propose a cyberattack detection-isolation algorithm comprised of a unified V2V and V2I cyberattack detection scheme along with a V2I isolation scheme for CAVs under changing driving conditions. The proposed algorithm is constructed using a bank of residual generators with Lyapunov function-based performance guarantees, such as disturbance-to-state stability, robustness, and sensitivity. Finally, we showcase the efficacy of our proposed algorithm through extensive Monte-Carlo simulations using real-world highway and urban driving data. The results show that the proposed algorithm can enhance the cybersecurity of CAVs by detecting cyberattacks on CAV platoons and isolating infrastructure-level traffic manipulation.