Learning Based Longitudinal Vehicle Platooning Threat Detection, Identification and Mitigation

The security of cyber-physical systems, such as vehicle platoons, is critical to ensuring their proper operation and acceptance to society. In platooning, vehicles follow one another according to an agreed-upon control law that determines vehicle separation. We show that a vehicle within a platoon and under the control of a malicious actor could cause collisions or decrease the efficiency of surrounding vehicles. This paper focuses on detecting, identifying, and mitigating so-called destabilizing attacks that could cause vehicle collisions. Our approach is decentralized and requires only local sensor information for each vehicle to identify the vehicle responsible for the attack and then deploy an appropriate mitigating controller that prevents collisions. We use a Deep Learning approach (Convolutional Neural Network) with various data preprocessing techniques to detect and identify the malicious vehicle. Results indicate that even with decent noise levels in range and relative speed data, we achieve accuracy up to 96.3%. Also, once the adversarial vehicle is localized, we derive conditions for controller gains using the Routh Hurwitz criterion to mitigate the attack and ensure the stability of the platoon. Realistic simulator CARLA and MATLAB simulation results validate the effectiveness of our proposed approaches.