用不确定性检测+统计测试，减少自动驾驶中误判静态物体为动态的错误。
Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
- 在3D检测器中加入不确定性估计，识别感知抖动
- 短时窗口内用双样本z检验区分真实运动与抖动
- 无需改动现有系统，实测显著减少误停次数
可靠的运动分类对自动驾驶至关重要，因静态物体被误判为动态会引发不必要的规划干预。不稳定的边界框预测会导致跟踪中产生虚假速度估计和轨迹预测。本文提出一种部署友好的缓解策略：在3D目标检测器中引入类别无关的随机不确定性估计，并在短观察窗口内应用双样本z检验，以区分真实运动与感知抖动。该方法集成到Autoware中仅需最小修改，复用现有数据关联机制，计算开销极低。在nuScenes上的实验表明性能与速度阈值法相当，但在真实道路测试中显著减少误动态预测和非必要停车，原因在于实际数据中存在速度阈值无法识别的中间抖动区间。这证明了不确定性感知检测与轻量统计测试可在更嘈杂的真实场景中带来实用性能提升。
原文摘要 · Abstract (English)
Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bounding box predictions can lead to spurious velocity estimates in tracking and falsely predicted trajectories. We present a deployment-friendly mitigation strategy that augments a 3D object detector with aleatoric uncertainty estimates and applies a two-sample z-test over short observation windows to separate true motion from jitter. Integrated into Autoware with minimal changes, the approach reuses existing data association for minimal compute overhead. Empirical results show parity with velocity thresholding on nuScenes, but substantially fewer false dynamic predictions and unnecessary stops in real-world test drives, explained by the presence of an intermediate jitter band in the recorded data that speed-only rules misclassify. This demonstrates that uncertainty-aware detection and lightweight statistical testing can deliver practical performance gains for autonomous driving in noisier real-world settings.
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