As a fundamental task in autonomous driving, LiDAR semantic segmentation aims to provide semantic understanding of the driving environment. We demonstrate that existing LiDAR semantic segmentation models in autonomous driving systems can be easily fooled by placing some simple objects on the road, such as cardboard and traffic signs. We show that this type of attack can hide a vehicle and change the road surface to road-side vegetation.
Demo #13: Attacking LiDAR Semantic Segmentation in Autonomous Driving
Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)
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Isaiah J. King (The George Washington University)Read More
Xueluan Gong (Wuhan University), Yanjiao Chen (Zhejiang University), Jianshuo Dong (Wuhan University), Qian Wang (Wuhan University)Read More
Zifeng Kang (Johns Hopkins University), Song Li (Johns Hopkins University), Yinzhi Cao (Johns Hopkins University)Read More