Yulong Cao (University of Michigan), Yanan Guo (University of Pittsburgh), Takami Sato (UC Irvine), Qi Alfred Chen (UC Irvine), Z. Morley Mao (University of Michigan) and Yueqiang Cheng (NIO)

Advanced driver-assistance systems (ADAS) are widely used by modern vehicle manufacturers to automate, adapt and enhance vehicle technology for safety and better driving. In this work, we design a practical attack against automated lane centering (ALC), a crucial functionality of ADAS, with remote adversarial patches. We identify that the back of a vehicle is an effective attack vector and improve the attack robustness by considering various input frames. The demo includes videos that show our attack can divert victim vehicle out of lane on a representative ADAS, Openpilot, in a simulator.

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Demo #5: Disclosing the Pringles Syndrome in Tesla FSD...

Zhisheng Hu (Baidu), Shengjian Guo (Baidu) and Kang Li (Baidu)

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SemperFi: Anti-spoofing GPS Receiver for UAVs

Harshad Sathaye (Northeastern University), Gerald LaMountain (Northeastern University), Pau Closas (Northeastern University), Aanjhan Ranganathan (Northeastern University)

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WIP: Infrastructure-Aided Defense for Autonomous Driving Systems: Opportunities and...

Yunpeng Luo (UC Irvine), Ningfei Wang (UC Irvine), Bo Yu (PerceptIn), Shaoshan Liu (PerceptIn) and Qi Alfred Chen (UC Irvine)

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Phishing awareness and education – When to best remind?

Benjamin Maximilian Berens (SECUSO, Karlsruhe Institute of Technology), Katerina Dimitrova, Mattia Mossano (SECUSO, Karlsruhe Institute of Technology), Melanie Volkamer (SECUSO,...

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