Sensor fusion of camera, LiDAR, and 4-dimensional (4D) Radar has brought a significant performance improvement in autonomous driving (AD). However, there still exist fundamental challenges: deeply coupled fusion methods assume continuous sensor availability, making them vulnerable to sensor degradation and failure, whereas sensor-wise cross-attention fusion methods struggle with computational cost and unified feature representation. This paper presents availability-aware sensor fusion (ASF), a novel method that employs unified canonical projection (UCP) to enable consistency in all sensor features for fusion and cross-attention across sensors along patches (CASAP) to enhance robustness of sensor fusion against sensor degradation and failure. As a result, the proposed ASF shows a superior object detection performance to the existing state-of-the-art fusion methods under various weather and sensor degradation (or failure) conditions; Extensive experiments on the K-Radar dataset demonstrate that ASF achieves improvements of 9.7% in AP BEV (87.2%) and 20.1% in AP 3D (73.6%) in object detection at IoU=0.5, while requiring a low computational cost. The code will be available at https://github.com/kaist-avelab/K-Radar.

本研究解决了自主驾驶中传感器融合方法对传感器可用性假设的局限性，提出了一种新颖的可用性感知传感器融合（ASF）方法。该方法通过统一典范投影实现传感器特征的一致性，并通过跨传感器注意力增强传感器融合的鲁棒性，实验结果显示在各种天气和传感器降级条件下，ASF在目标检测性能上显著优于现有最先进的融合方法。

基于统一典范空间的可用性感知传感器融合方法：雷达、激光雷达与摄像头的四维融合