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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Above and Beyond: Organizational Efforts to Complement U.S. Digital...

Rock Stevens (University of Maryland), Faris Bugra Kokulu (Arizona State University), Adam Doupé (Arizona State University), Michelle L. Mazurek (University of Maryland)

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WeepingCAN: A Stealthy CAN Bus-off Attack

Gedare Bloom (University of Colorado Colorado Springs) Best Paper Award Winner ($300 cash prize)!

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Drivers and Passengers Maybe the Weakest Link in the...

Aiping Xiong (Pennsylvania State University), Zekun Cai (Pennsylvania State University) and Tianhao Wang (University of Virginia)

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SoK: A Proposal for Incorporating Gamified Cybersecurity Awareness in...

June De La Cruz (INSPIRIT Lab, University of Denver), Sanchari Das (INSPIRIT Lab, University of Denver)

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