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.

翻译：相机、激光雷达与四维（4D）雷达的传感器融合为自动驾驶（AD）带来了显著的性能提升。然而，该领域仍存在根本性挑战：深度耦合的融合方法假设传感器持续可用，使其易受传感器性能退化与故障的影响；而基于传感器级交叉注意力的融合方法则受限于计算成本与统一特征表示的困难。本文提出可用性感知传感器融合（ASF），该方法采用统一规范投影（UCP）实现所有传感器特征在融合空间中的一致性表达，并通过跨传感器补丁级交叉注意力（CASAP）机制增强传感器融合对性能退化与故障的鲁棒性。实验结果表明，在各种天气条件与传感器退化（或故障）场景下，所提出的ASF在目标检测任务中均优于现有最先进的融合方法；在K-Radar数据集上的大量实验表明，在IoU=0.5时，ASF在鸟瞰图平均精度（AP BEV）上达到87.2%（提升9.7%），在三维平均精度（AP 3D）上达到73.6%（提升20.1%），同时保持较低的计算成本。代码将在https://github.com/kaist-avelab/K-Radar公开。