Junqi Zhang, University of Science and Technology of China; Shaoyin Cheng, University of Science and Technology of China and Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation; Linqing Hu, University of Science and Technology of China; Jie Zhang, CFAR and IHPC, A*STAR; Chengyu Shi, DeepBlue College; Xingshuo Han and Tianwei Zhang, Nanyang Technological University; Yueqiang Cheng, MediaTek; Weiming Zhang, University of Science and Technology of China and Anhui Province Key Laboratory of Digital Security
Localization is crucial for Autonomous Driving (AD), which serves as a critical foundation impacting the performance of downstream modules. While Multi-Sensor Fusion (MSF) techniques enhance localization accuracy and reliability, the security of fusion-based localization systems has emerged as a major concern. Although existing studies have extensively investigated security aspects of these systems, the impact of vehicle dynamics on the effectiveness of Global Positioning System (GPS) spoofing attacks is persistently overlooked.
Bridging this research gap, we propose the Motion-Sensitive Analysis Framework (MSAF), which focuses on analyzing previously underestimated dynamic behaviors of vehicles. Our investigation demonstrates that two dynamic scenarios, acceleration and high-speed cruising, significantly influence the success rates of GPS spoofing attacks. These scenarios, commonly encountered across driving conditions, exhibit heightened vulnerabilities under MSAF analysis. Building on these insights, we design two dynamics-targeted attack strategies and evaluate them across three testbeds: our simulated framework (MSAF_MSF) and two real-world MSF-based autonomous driving systems (Apollo_MSF and Shenlan_MSF). The results demonstrate a significant attack efficiency improvement by our method: MSAF requires substantially less time to complete attacks compared to the baseline while achieving higher success rates. Code and attack demos are available at https://sites.google.com/view/msaf-attack.
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