Active CyberAttack Detection on Autonomous Vehicle Using Model Predictive Control

This article focuses on enhancing the resilience of autonomous vehicles (AVs) against cyberattacks. It integrates Model Predictive Control (MPC) and Lyapunov-based Economic Model Predictive Control (LEMPC) to manage vehicle dynamics and detect potential cyber threats. The research comprehensively covers vehicle modeling, the implementation of MPC for vehicle control, and a detailed analysis of various cyber-attacks targeting autonomous vehicles. Detection mechanisms using MPC are developed to identify and mitigate these attacks, ensuring the stability and reliability of AV operations. These mechanisms are compared with traditional detection methods such as chi-square, cumulative sum (CUSUM), and multivariate exponentially weighted moving average (MEWMA). Additionally, a Kalman Filter (KF) is employed for state estimation from noisy measurements, enhancing the accuracy of the detection system. Extensive simulation studies validate the effectiveness of the proposed methods in a lane change scenario, demonstrating significant improvements in the resilience and operational safety of autonomous vehicles.