Cloud-based cyber-attack detection and secure estimation for connected and automated vehicles

As sensing technologies and wireless communication advance, connected and automated vehicles (CAVs) will be able to share local sensor measurements with other surrounding vehicles for various driving tasks, including intelligent traffic routing, lane change alerts, and collision avoidance. However, CAVs are more susceptible to cyber-attacks because of vehicle connectivity. To address the practical issue of navigation systems of CAVs being vulnerable to malicious attacks from both internal and external-vehicle networks, the same physical variables are measured by multiple sensors in order to provide redundancy in the detection of cyber-attacks. Firstly, taking advantage of this redundancy, a cyber-attack detection method based on a convolutional social layer is proposed, which can accurately detect and identify cyber-attacks and track the location of malicious vehicles. Secondly, based on the accurate detection results, a real-time anomaly detection and recovery system (RADRS) is developed to safely estimate the real-time location information of CAVs. Then, a robust lateral control scheme is provided, which can stabilize the closed-loop dynamics and minimize the impact of cyber-attacks and network effects on vehicle tracking performance. Finally, simulation experiments are performed to demonstrate the effectiveness of our methods.