Waymo开放运动数据集能否支持真实的行为建模？一项与自然轨迹相结合的验证研究
Can the Waymo Open Motion Dataset Support Realistic Behavioral Modeling? A Validation Study with Naturalistic Trajectories
AI总结：
本研究通过对比自然主义数据与Waymo数据集，发现其无法准确反映真实自动驾驶行为，需谨慎使用。
摘要：
Waymo开放运动数据集（WOMD）已成为用于自动驾驶车辆（AV）行为的数据驱动建模的流行资源。然而，由于专有后处理、缺乏误差量化以及轨迹被分割为20秒片段，其在行为分析中的有效性仍存疑。本研究探讨了WOMD是否能准确捕捉现实世界AV操作中观察到的动力学和相互作用。利用独立收集的来自亚利桑那州凤凰城四级自动驾驶车辆（PHX）的自然主义数据集，我们在三个具有代表性的城市驾驶场景中进行比较分析：信号灯交叉口的车辆放行、车距控制和车道变更行为。对于放行分析，从空中视频中手动提取头车时间间隔以确保测量误差极小。对于车距控制和车道变更案例，我们应用Simulation-Extrapolation（SIMEX）方法以考虑PHX数据中经验估计的误差，并使用动态时间规整（DTW）距离来量化行为差异。所有场景的结果一致显示，PHX中的行为均超出WOMD的行为范围。值得注意的是，WOMD低估了短头车时间和突然减速。这些发现表明，仅基于WOMD校准的行为模型可能会系统性低估自然驾驶的变异性、风险和复杂性。因此，在没有适当验证的情况下使用WOMD进行行为建模需谨慎。
英文摘要：
The Waymo Open Motion Dataset (WOMD) has become a popular resource for data-driven modeling of autonomous vehicles (AVs) behavior. However, its validity for behavioral analysis remains uncertain due to proprietary post-processing, the absence of error quantification, and the segmentation of trajectories into 20-second clips. This study examines whether WOMD accurately captures the dynamics and interactions observed in real-world AV operations. Leveraging an independently collected naturalistic dataset from Level 4 AV operations in Phoenix, Arizona (PHX), we perform comparative analyses across three representative urban driving scenarios: discharging at signalized intersections, car-following, and lane-changing behaviors. For the discharging analysis, headways are manually extracted from aerial video to ensure negligible measurement error. For the car-following and lane-changing cases, we apply the Simulation-Extrapolation (SIMEX) method to account for empirically estimated error in the PHX data and use Dynamic Time Warping (DTW) distances to quantify behavioral differences. Results across all scenarios consistently show that behavior in PHX falls outside the behavioral envelope of WOMD. Notably, WOMD underrepresents short headways and abrupt decelerations. These findings suggest that behavioral models calibrated solely on WOMD may systematically underestimate the variability, risk, and complexity of naturalistic driving. Caution is therefore warranted when using WOMD for behavior modeling without proper validation against independently collected data.
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