Experimental Results in Cyber-Physical Transportation Systems: A Case Study in Cybersecurity

This paper presents experimental results from a learning-based control framework for cyber-physical transportation systems. Building on theoretical guarantees that establish an upper bound on denial-of-service (DoS) attack durations to maintain closed-loop stability, we deploy a resilient learning-based lane-changing control algorithm on a remote-controlled (RC) autonomous vehicle equipped with GPS, IMU, and camera sensors, interfaced with an Nvidia Jetson AGX Xavier board. The algorithm leverages real-time sensor data to make suboptimal yet robust lane-change decisions while enduring intermittent DoS attacks that disrupt communication. Our experiments confirm the resilience of this learning-based approach, demonstrating safe and efficient maneuvers under adversarial conditions in obstacle-rich driving scenarios. By highlighting these experimental findings, this work underscores the importance of cybersecurity in next-generation vehicle control algorithms for autonomous transportation applications.