用样条+机器学习融合轨迹去噪，保证运动一致性。
Physical spline for denoising object trajectory data by combining splines, ML feature regression and model knowledge
- 结合样条拟合、特征回归与物理模型约束
- 去噪后轨迹满足速度=加速度积分，位置=速度积分
- 适合需要高质量轨迹数据的自动驾驶训练场景
本文提出一种从噪声测量数据中估计动态驾驶状态（位置、速度、加速度和航向）的方法。该方法在完整与部分观测下均有效，生成具有运动一致性的精炼轨迹信号，确保速度为加速度的积分，位置为速度的积分，并考虑车辆仅能沿自身朝向移动的约束。方法以可配置的Python库实现，支持仅基于位置数据的轨迹估计。通过正则化防止状态突变。关键应用是提升记录轨迹数据质量，作为机器学习模型的参考输入。文章最后展示了该方法与真实数据的对比结果。
原文摘要 · Abstract (English)
This article presents a method for estimating the dynamic driving states (position, velocity, acceleration and heading) from noisy measurement data. The proposed approach is effective with both complete and partial observations, producing refined trajectory signals with kinematic consistency, ensuring that velocity is the integral of acceleration and position is the integral of velocity. Additionally, the method accounts for the constraint that vehicles can only move in the direction of their orientation. The method is implemented as a configurable python library that also enables trajectory estimation solely based on position data. Regularization is applied to prevent extreme state variations. A key application is enhancing recorded trajectory data for use as reference inputs in machine learning models. At the end, the article presents the results of the method along with a comparison to ground truth data.
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