Abstract
Autonomous vehicles (AVs) are on the verge of changing the transportation industry. Despite the fast development of autonomous driving systems (ADSs), they still face safety and security challenges. Current defensive approaches usually focus on a narrow objective and are bound to specific platforms, making them difficult to generalize. To solve these limitations, we propose AVMaestro, an efficient and effective policy enforcement framework for full-stack ADSs. AVMaestro includes a code instrumentation module to systematically collect required information across the entire ADS, which will then be feed into a centralized data examination module, where users can utilize the global information to deploy defensive methods to protect AVs from various threats. AVMaestro is evaluated on top of Apollo-6.0 and experimental results confirm that it can be easily incorporated into the original ADS with almost negligible run-time delay. We further demonstrate that utilizing the global information can not only improve the accuracy of existing intrusion detection methods, but also potentially inspire new security applications.
Suggested Citation
Ze Zhang, Sanjay Sri Vallabh Singapuram, Qingzhao Zhang, David Ke Hong, Brandon Nguyen, Z. Morley Mao, Scott Mahlke and Qi Alfred Chen (2022) “AVMaestro: A Centralized Policy Enforcement Framework for Safe Autonomous-driving Environments”, in
2022 IEEE Intelligent Vehicles Symposium (IV).
2022 IEEE Intelligent Vehicles Symposium (IV), pp. 1333–1339. Available at:
10.1109/IV51971.2022.9827092.
