A Survey on Attack Detection and Resilience for Connected and Automated Vehicles: From Vehicle Dynamics and Control Perspective
Zhiyang Ju, Hui Zhang, Xiang Li, Xiaoguang Chen, Jinpeng Han, Manzhi Yang
Abstract
Recent advances in attack/anomaly detection and resilience strategies for connected and automated vehicles (CAVs) are reviewed from vehicle dynamics and control perspective. Compared to traditional vehicles, CAVs are featured in the increasing number of perception sensors, advanced intra-vehicle communication technologies, capabilities of driving automation and connectivity between single vehicles. These features bring about safety issues which are not encountered in traditional vehicle systems. One main type of these issues is the attack or anomaly launched onto the perception sensors and the communication channels. With such a consideration, this survey summarizes and reviews the existing results on attack/anomaly detection and resilience of CAVs in control frameworks. This paper reviews the results according to the positions at which the attacks/anomalies occur. These positions are divided into three categories, namely, intra-vehicle communication network, perception sensors and inter-vehicle communication network. From this perspective, the recent attack/anomaly detection and resilience results are reviewed according to different positions attacked. After reviewing existing results, some potential research directions and challenges are identified.