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.

本研究评估了 LiDAR - 相机融合模型在 3D 物体检测中的对抗鲁棒性。我们介绍了一种攻击技术，在汽车上简单添加一定数量的物理限制的对抗点后，可以使车辆在融合模型中无法检测到。实验结果表明，即使在图像数据通道没有变化的情况下，通过操纵 LiDAR 数据通道，融合模型仍然可以被欺骗。这一发现引发了自动驾驶领域的安全担忧。此外，我们还研究了对抗点数量、前方近处车辆与搭载 LiDAR 的车辆之间的距离以及不同角度因素对攻击成功率的影响。我们相信我们的研究可以增进对多传感器鲁棒性的理解，为增强自动驾驶的安全性提供见解和指导。