  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.
