Demo #1: Security of Multi-Sensor Fusion based Perception in AD under Physical-World Attacks

Yulong Cao (University of Michigan), Ningfei Wang (UC, Irvine), Chaowei Xiao (Arizona State University), Dawei Yang (University of Michigan), Jin Fang (Baidu Research), Ruigang Yang (University of Michigan), Qi Alfred Chen (UC, Irvine), Mingyan Liu (University of Michigan) and Bo Li (University of Illinois at Urbana-Champaign)

In autonomous driving (AD) vehicles, Multi-Sensor Fusion (MSF) is used to combine perception results from multiple sensors such as LiDARs (Light Detection And Ranging) and cameras for both accuracy and robustness. In this work, we design the first attack that fundamentally defeats MSF-based AD perception by generating 3D adversarial objects. This demonstration will include video and figure demonstrations for the generated 3D adversarial objects and the end-to-end consequences.

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Keynote #1

Generation of CAN-based Wheel Lockup Attacks on the Dynamics...

Alireza Mohammadi (University of Michigan-Dearborn), Hafiz Malik (University of Michigan-Dearborn) and Masoud Abbaszadeh (GE Global Research)

Demo #4: Recovering Autonomous Robotic Vehicles from Physical Attacks

Pritam Dash (University of British Columbia) and Karthik Pattabiraman (University of British Columbia)

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)