Driving State-Aware Anomaly Detection for Autonomous Vehicles
Zhihao Wang, Lei Xue, Xiapu Luo, Xiaobo Ma, Guofei Gu · IEEE Transactions on Information Forensics and Security · 2025
With the increasing popularity of autonomous driving systems (ADS) in autonomous vehicles (AV), in recent years, there have been many attacks targeting AVs and ADSs. Meanwhile, recent studies have attempted to improve the safety and security of AVs from different perspectives, and they mainly focus on the spoofing attacks against the sensors and the injection attacks against the vehicle chassis and actuators. However, direct attacks on ADSs (i.e., communication hijacking and malicious codes) remain inadequately addressed, and even worse, such attacks can cause AVs to make unsafe driving decisions rapidly. In this paper, we introduceDSAD, a driving state-aware anomaly detection framework designed to enhance AV safety and security by identifying ADS attacks, such as communication hijacking and malicious codes, through chassis states. First,DSADmodels ADS operations (i.e., driving states) as a two-layer state machine, utilizing real-time chassis data to infer driving states and detect anomalies in ADS outputs. This reduces false positives and negatives by aligning detection with the diverse operational modes of AVs. To achieve this, we develop a prototype system,DSAD, incorporating a Detection Policy Update mechanism that dynamically adjusts detection policies based on the vehicle’s driving states, such as lane changing and obstacle avoidance. Second,DSADconsiders both collision avoidance and control stability, addressing potential conflicts through hard and soft requirements. Furthermore,DSADintegrates a fault handling module compatible with existing autonomous driving fault handling mechanisms, ensuring timely response to detected anomalies. We develop a prototype anomaly detection system calledDSADand deploy it on four ADSs. We evaluateDSADusing various attack scenarios, and the results show thatDSADcan identify over 90% of attacks on ADSs.