Application of Discrete-Time Unknown Input Observers for Cyberattack Detection in Connected CACC Vehicles

This study addresses the increasing security challenges faced by Cooperative Adaptive Cruise Control (CACC) systems due to cyberattacks. These systems, which rely on real-time data processing and critical decision-making, necessitate effective cyberattack detection methods. We propose the appli-cation of unknown input observers (UIOs) to simultaneously estimate system states and detect cyberattacks. A significant challenge in implementing UIOs is satisfying the matrix rank condition necessary for their existence. Traditional discretization methods often fail to meet the required performance and accuracy for effective threat detection. Our contribution lies in employing an exact discretization method that overcomes these limitations, ensuring the rank condition is satisfied. This approach enhances the robustness of CACC systems against cyberattacks, allowing for more accurate tracking and quicker threat response, thereby improving security and traffic flow in modern transportation systems.