自動運転におけるLiDAR-カメラ融合モデルの敵対的ロバスト性の探究
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving
車の上に少数の物理的制約付き敵対的点を追加するだけで、LiDAR-カメラ融合3D物体検出モデルが車を検出できなくなることを示し、その攻撃成功率に影響する要因を分析した研究。
著者: Bo Yang, Xiaoyu Ji, Zizhi Jin, Yushi Cheng, Wenyuan Xu
分類: cs.RO, cs.AI
原文アブストラクト
Our study assesses the adversarial robustness of LiDAR-camera fusion models in 3D object detection. We introduce an attack technique that, by simply adding a limited number of physically constrained adversarial points above a car, can make the car undetectable by the fusion model. Experimental results reveal that even without changes to the image data channel, the fusion model can be deceived solely by manipulating the LiDAR data channel. This finding raises safety concerns in the field of autonomous driving. Further, we explore how the quantity of adversarial points, the distance between the front-near car and the LiDAR-equipped car, and various angular factors affect the attack success rate. We believe our research can contribute to the understanding of multi-sensor robustness, offering insights and guidance to enhance the safety of autonomous driving.
