conference paper

WIP: Practical Removal Attacks on LiDAR-based Object Detection in Autonomous Driving

ISOC Symposium on Vehicle Security and Privacy (VehicleSec)

Publication Date

January 1, 2023

Author(s)

Takami Sato, Yuki Hayakawa, Ryo Suzuki, Yohsuke Shiiki, Kentaro Yoshioka, Qi Alfred Chen

Abstract

LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application strongly motivates its security research. A recent line of research demonstrates that one can manipulate the LiDAR point cloud and fool object detection by firing malicious lasers against LiDAR. However, these efforts evaluate only a specific LiDAR (VLP-16) and do not consider the state-of-the-art defense mechanisms in the recent LiDARs, so-called next-generation LiDARs. In this WIP work, we report our recent progress in the security analysis of the next-generation LiDARs. We identify a new type of LiDAR spoofing attack applicable to a much more general and recent set of LiDARs. We find that our attack can remove >72% of points in a 10×10 m2 area and can remove real vehicles in the physical world. We also discuss our future plans.

Suggested Citation
Takami Sato, Yuki Hayakawa, Ryo Suzuki, Yohsuke Shiiki, Kentaro Yoshioka and Qi Alfred Chen (2023) “WIP: Practical Removal Attacks on LiDAR-based Object Detection in Autonomous Driving”, in ISOC Symposium on Vehicle Security and Privacy (VehicleSec). Available at: https://par.nsf.gov/biblio/10427123-wip-practical-removal-attacks-lidar-based-object-detection-autonomous-driving (Accessed: September 13, 2024).