Demo: Remote Adversarial Attack on Automated Lane Centering
Yulong Cao, Yanan Guo, Takami Sato, Qi Alfred Chen, Z. Morley Mao, Yueqiang Cheng · 2022
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.