Kanglan Tang, Junjie Shen, and Qi Alfred Chen (UC Irvine)

The perception module is the key to the security of Autonomous Driving systems. It perceives the environment through sensors to help make safe and correct driving decisions on the road. The localization module is usually considered to be independent of the perception module. However, we discover that the correctness of perception output highly depends on localization due to the widely used Region-of-Interest design adopted in perception. Leveraging this insight, we propose an ROI attack and perform a case study in the traffic light detection in Autonomous Driving systems. We evaluate the ROI attack on a production-grade Autonomous Driving system, named Baidu Apollo, under end-to-end simulation environments. We found our attack is able to make the victim a red light runner or cause denial-of-service with a 100% success rate.

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Diogo Barradas (Instituto Superior Técnico, Universidade de Lisboa)

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Demo #4: Attacking Tesla Model X’s Autopilot Using Compromised...

Ben Nassi (Ben-Gurion University of the Negev), Yisroel Mirsky (Ben-Gurion University of the Negev, Georgia Tech), Dudi Nassi, Raz Ben Netanel (Ben-Gurion University of the Negev), Oleg Drokin (Independent Researcher), and Yuval Elovici (Ben-Gurion University of the Negev) Best Demo Award Winner ($300 cash prize)!

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From WHOIS to WHOWAS: A Large-Scale Measurement Study of...

Chaoyi Lu (Tsinghua University; Beijing National Research Center for Information Science and Technology), Baojun Liu (Tsinghua University; Beijing National Research Center for Information Science and Technology; Qi An Xin Group), Yiming Zhang (Tsinghua University; Beijing National Research Center for Information Science and Technology), Zhou Li (University of California, Irvine), Fenglu Zhang (Tsinghua University), Haixin Duan…

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Towards a TEE-based V2V Protocol for Connected and Autonomous...

Mohit Kumar Jangid (Ohio State University) and Zhiqiang Lin (Ohio State University)

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