Secure and Safe Control of Connected and Automated Vehicles Against False Data Injection Attacks

This paper studies the secure and safe control problem of connected and automated vehicles (CAVs) with false data injection (FDI) attacks. A secure and safe controller with a novel surrounding vehicles’ state estimator and an attack detector is proposed. The state estimation for surrounding vehicles is collectively processed by combining the deep neural network-based predictions with model-based estimations. Additionally, a weight in the loss function is proposed for more accurate predictions of vehicles that are closer to the ego vehicle. For attack detection, a novel scheme that utilizes control outcomes to train detection actions is proposed. Different from the objectives of existing detectors, the reward for the proposed detector is designed to encourage the CAV to fully utilize the observations. A reward setting and the decision preference of the ego vehicle are theoretically analyzed. The effectiveness of the proposed algorithm is validated in an open simulation environment.