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.
Demo #15: Remote Adversarial Attack on Automated Lane Centering
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)
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Shout-Out for PASS (Platform for Auto-driving Safety and Security)
Zhisheng Hu (Baidu Security)
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Phillip Rieger (Technical University of Darmstadt), Thien Duc Nguyen (Technical University of Darmstadt), Markus Miettinen (Technical University of Darmstadt), Ahmad-Reza...
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Reethika Ramesh (University of Michigan), Leonid Evdokimov (Independent), Diwen Xue, Roya Ensafi (University of Michigan)
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Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!
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