Fusion is Not Enough: Single-Modal Attacks to Compromise Fusion Models in Autonomous Driving. (arXiv:2304.14614v1 [cs.CV])

Multi-sensor fusion (MSF) is widely adopted for perception in autonomous
vehicles (AVs), particularly for the task of 3D object detection with camera
and LiDAR sensors. The rationale behind fusion is to capitalize on the
strengths of each modality while mitigating their limitations. The exceptional
and leading performance of fusion models has been demonstrated by advanced deep
neural network (DNN)-based fusion techniques. Fusion models are also perceived
as more robust to attacks compared to single-modal ones due to the redundant
information …

attacks autonomous autonomous driving autonomous vehicles camera compromise detection driving fusion lidar modal object sensor sensors single task vehicles