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.

翻译：可靠的运动分类对于自动驾驶至关重要，因为对静态物体的错误动态预测可能引发规划器不必要的干预。不稳定的边界框预测会导致跟踪中出现虚假的速度估计和错误预测的轨迹。我们提出了一种易于部署的缓解策略，通过为三维目标检测器添加偶然不确定性估计，并对短时间观测窗口应用双样本Z检验，以区分真实运动与抖动。该方法以最小改动集成到Autoware中，复用现有数据关联以实现极小的计算开销。实验结果表明，在nuScenes数据集上，该方法与速度阈值法性能相当，但在实际道路测试中，虚假动态预测和不必要停车的次数显著减少。原因在于记录数据中存在一个速度规则难以分类的中间抖动区间。这证明，不确定性感知检测与轻量级统计检验能在更嘈杂的真实环境中为自动驾驶带来实际性能提升。