Ghost Vehicle: A Game-Theoretical Attack Strategy Targeting CAV Platoons

With the rapid development of V2X and autonomous driving technologies, the security and stability of platoons based on Connected Autonomous Vehicles (CAVs) have become a critical research focus. However, these systems remain vulnerable when facing sophisticated attacks. In this study, we propose a Game Theory-based attack framework that introduces a Ghost Vehicle capable of executing three distinct attack modes, Leader Attack, Mid-Platoon Attack, and Follower Attack. Experimental results demonstrate that our attack effectively destabilizes the platoon during Leader Attack and Mid-Platoon Attack, while seamlessly integrating as a normal member during the Follower Attack. Unlike prior sensor-spoofing attacks that rely on hardware-intensive methods, the behavior of the Ghost Vehicle is dynamically controlled using Mode Predictive Control (MPC) and Finite State Machine (FSM), ensuring high efficiency with a maximum computation time of 40 seconds, which represents an average across multiple runs. Our attack modes are validated in a highway scenario, demonstrating the high effectiveness with low computational cost.