Distributed Data-Driven Control for a Connected Autonomous Vehicle Platoon Subjected to False Data Injection Attacks

In this paper, we consider the need for deployment in the long-distance safe longitudinal formation control task when the connected autonomous vehicle (CAV) platoon is subjected to malicious cyber attacks. To ensure the safe, orderly, stable and efficient driving performance of the vehicle platoon, a novel distributed data-driven control (DDDC) approach for a homogeneous connected autonomous vehicle platoon under false data injection (FDI) attacks is investigated. First, an FDI attacks detection and compensation mechanism is designed to detect whether the received position signals are under attack or not and compensate the attacked position signals. Then, a novel DDDC approach for the vehicle platoon longitudinal formation control is developed by using the compensation data from the designed attack compensation mechanism and a dynamic linearization data model. Theoretical analysis verifies that the proposed DDDC method can ensure the internal stability (IS) and string stability (SS) of the homogeneous platoon subjected to FDI attacks. Finally, the effectiveness and practicality of the proposed DDDC approach are validated through a group of comparative simulations subjected to random FDI attacks of equal frequency and magnitude.