标题： 受控噪声下多源鸟瞰图检测的不确定性感知后期融合算法评估 显示英文标题

标题： Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise

摘要： 可靠得多源融合对于自主系统中的鲁棒感知至关重要。 然而，独立于检测误差评估融合性能仍然具有挑战性。 本工作引入了一个系统评估框架，通过向真实边界框注入受控噪声来隔离融合过程。 我们随后提出了统一卡尔曼融合（UniKF），一种基于卡尔曼滤波的后融合算法，用于合并鸟瞰图（BEV）检测，同时处理同步问题。 实验表明，UniKF在各种噪声水平下均优于基线方法，实现了物体定位和方向误差降低高达3倍，尺寸估计误差降低2倍，同时保持精度和召回率在99.5%到100%之间。 显示英文摘要

摘要： 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%.