提出一种带不确定性感知的多源融合算法，提升自动驾驶感知可靠性
Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise
- 用受控噪声注入法分离融合过程与检测误差
- 在不同噪声下定位与朝向误差降低至1/3，尺寸估计误差减半
- 适合对感知精度要求高的自动驾驶系统研究
可靠的多源融合对自主系统鲁棒感知至关重要。然而，独立于检测误差评估融合性能仍具挑战。本文提出系统性评估框架，通过向真实边界框注入受控噪声以隔离融合过程。进而提出基于卡尔曼滤波的统一卡尔曼融合（UniKF）算法，用于融合鸟瞰图（BEV）检测结果并处理同步问题。实验表明，UniKF 在各种噪声水平下均优于基线方法，定位与朝向误差最高降低3倍，尺寸估计误差降低2倍，同时保持99.5%至100%的近乎完美精确率与召回率。
原文摘要 · Abstract (English)
Reliable multi-source fusion is crucial for robust perception in autonomous systems. However, evaluating fusion performance independently of detection errors remains challenging. This work introduces a systematic evaluation framework that injects controlled noise into ground-truth bounding boxes to isolate the fusion process. We then propose Unified Kalman Fusion (UniKF), a late-fusion algorithm based on Kalman filtering to merge Bird's Eye View (BEV) detections while handling synchronization issues. Experiments show that UniKF outperforms baseline methods across various noise levels, achieving up to 3x lower object's positioning and orientation errors and 2x lower dimension estimation errors, while maintaining nearperfect precision and recall between 99.5% and 100%.
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