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.

翻译：本研究评估了三维目标检测中激光雷达-相机融合模型的对抗鲁棒性。我们提出一种攻击技术，通过在一辆汽车上方添加数量有限的物理约束对抗点，即可使该车辆对融合模型不可见。实验结果表明，即使不改变图像数据通道，仅通过操控激光雷达数据通道就能欺骗融合模型。这一发现引发了自动驾驶领域的安全担忧。此外，我们探讨了对抗点数量、前车与搭载激光雷达车辆的距离以及多种角度因素对攻击成功率的影响。我们相信该研究有助于理解多传感器鲁棒性，为增强自动驾驶安全性提供见解与指导。