Real-Time Adaptive Camera-Attack Detection and Correction for Autonomous Vehicle Platoons in Cyber-Physical Systems via a MADRL Approach

Abstract

In autonomous vehicle platooning, members of the platoon not only use their own sensor data for making driving decisions. They also rely on data shared by other members of the platoon. This research proposes a novel framework for real-time detection and correction of camera-based attacks on platoonbased autonomous vehicles within Cyber-Physical Systems (CPS) using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. Camera systems, critical for perception in autonomous vehicles, are vulnerable to adversarial attacks such as image manipulation, occlusion, or sensor jamming, which can disrupt platoon coordination and safety. The proposed system leverages MADRL to enable adaptive, decentralized decision-making among vehicles, ensuring robust attack detection and mitigation while maintaining platoon stability. By integrating real-time camera sensor data fusion, anomaly detection, and corrective action policies, the framework enhances resilience against cyber-attacks. Experimental results demonstrate improved detection accuracy, reduced response time, and enhanced platoon performance under various attack scenarios compared to traditional methods.