提出可感知传感器可用性的融合方法，提升自动驾驶在恶劣环境下的检测鲁棒性。
Availability-aware Sensor Fusion via Unified Canonical Space
- 通过统一规范空间投影实现多传感器特征对齐
- 在复杂天气与传感器故障下，3D检测精度提升20.1%
- 适合追求高鲁棒性的自动驾驶感知系统
相机、激光雷达和4维雷达的传感器融合显著提升了自动驾驶性能。然而，深度耦合融合方法假设传感器持续可用，易受退化或故障影响；而基于跨注意力的融合方法存在计算开销大、特征表示不统一的问题。本文提出可用性感知融合（ASF），采用统一规范空间投影（UCP）实现所有传感器特征的一致性对齐，并设计沿块交叉注意力机制（CASAP），增强对传感器退化与故障的鲁棒性。在K-Radar数据集上的大量实验表明，该方法在IoU=0.5时，平均精度（AP BEV）达87.2%，较现有最优方法提升9.7%；3D检测平均精度（AP 3D）达73.6%，提升20.1%，且计算成本低。代码已公开于https://github.com/kaist-avelab/k-radar。
原文摘要 · Abstract (English)
Sensor fusion of camera, LiDAR, and 4-dimensional (4D) Radar has brought a significant performance improvement in autonomous driving. 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. All codes are available at https://github.com/kaist-avelab/k-radar.
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