Simultaneous Cyber Attack Estimation and Radar Spoofing Attack Detection for Connected Automated Vehicles

This paper addresses the problem of simultaneous cyber attack estimation and sensor attack detection for connected automated vehicles (CAVs), where the vehicle-to-vehicle communication network suffers from false data injection attacks and vehicular radar sensors experience spoofing attacks. First, a delicate proportional-integral observer (PIO) is developed to estimate the unavailable longitudinal vehicle tracking errors and the injected attack signal in real-time. Second, leveraging a residual signal generated from the PIO, an effective detection mechanism, which involves residual evaluation and thresholding, is designed to identify the radar spoofing attacks. Furthermore, due to the concurrent effects of false data injection attacks and spoofing attacks on the estimation errors, formal stability and performance analysis in terms of robustness against false data injection and sensitivity against spoofing is then performed. Finally, simulation examples are provided to demonstrate the efficacy of the proposed attack estimation and attack detection method.