Richard Capraru, Emil C. Lupu, Soteris Demetriou, Jian-Gang Wang, Boon Hee Soong, Rain-Reaper: Unmasking LiDAR-based Detector Vulnerabilities in Rain, 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
Abstract:
LIDAR-based 3D object detection aims to enhance the situational awareness of autonomous vehicles. Despite recent advancements in this technology, there has been evidence that the susceptibility of 3D object detectors to signal spoofing is high, leading to the erroneous detection of “ghost objects” or the
failure to detect genuine ones. While prior work has investigated the design of these new attacks and new defenses, the effect of weather conditions, which is a hot topic in autonomous vehicle research, on both attacks and defenses has never been studied. Inspired by this observation, in this paper, we present a
novel genetic algorithm-based attack, entitled Rain-Reaper, that leverages on the effect of rain and identifies critical detection points used by 3D detectors. We show that adverse weather conditions not only diminish detection distance and accuracy but also expose the limitations of existing defenses. We have found that the unique characteristics of wet roads lead to underperforming defenses, thus, leading to a false sense of confidence in them. The effectiveness and efficiency of the attack and the robustness of the defenses have been evaluated with both simulated and real data. Our Rain-Reaper demonstrates a high attack success rate while successfully evading existing defenses with an adversarial point budget of up to 8.8 times smaller
than previously demonstrated state-of-the-art attacks.
License type:
Publisher Copyright
Funding Info:
This research was supported by the NTU-Imperial Global Fellows
Program.
This research was supported by the A*STAR SINGA scholarship.