针对传感器故障时的3D目标检测可靠性问题，提出动态融合框架ReliFusion。
Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection
- 在鸟瞰图空间中设计三模块融合架构，提升多帧稳定性。
- 在nuScenes上实现更优准确率与鲁棒性，尤其在激光雷达失效时。
- 适合自动驾驶中需高可靠感知的场景，如恶劣天气或传感器故障。
精确且可靠的3D目标检测对自动驾驶至关重要，融合激光雷达（LiDAR）与摄像头数据可提升检测性能。然而，传感器异常（如数据污染或断连）会导致性能下降，现有融合模型在单模态失效时往往表现不佳。为此，本文提出ReliFusion，一种基于鸟瞰图（BEV）空间的新型激光雷达-摄像头融合框架。该框架包含三个核心组件：时空特征聚合（STFA）模块，用于捕捉跨帧依赖以稳定预测；可靠性模块，为各模态分配置信度分数以量化其在复杂条件下的可信度；以及置信加权互注意力（CW-MCA）模块，根据置信度动态调整激光雷达与摄像头信息的融合权重。在nuScenes数据集上的实验表明，ReliFusion显著优于现有先进方法，在激光雷达视场受限及传感器严重故障场景下仍保持优异的鲁棒性与准确性。
原文摘要 · Abstract (English)
Accurate and robust 3D object detection is essential for autonomous driving, where fusing data from sensors like LiDAR and camera enhances detection accuracy. However, sensor malfunctions such as corruption or disconnection can degrade performance, and existing fusion models often struggle to maintain reliability when one modality fails. To address this, we propose ReliFusion, a novel LiDAR-camera fusion framework operating in the bird's-eye view (BEV) space. ReliFusion integrates three key components: the Spatio-Temporal Feature Aggregation (STFA) module, which captures dependencies across frames to stabilize predictions over time; the Reliability module, which assigns confidence scores to quantify the dependability of each modality under challenging conditions; and the Confidence-Weighted Mutual Cross-Attention (CW-MCA) module, which dynamically balances information from LiDAR and camera modalities based on these confidence scores. Experiments on the nuScenes dataset show that ReliFusion significantly outperforms state-of-the-art methods, achieving superior robustness and accuracy in scenarios with limited LiDAR fields of view and severe sensor malfunctions.
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