High-angle cameras are commonly used for trajectory data collection in transportation research. However, without refinement and validation, trajectory data obtained through video processing software may be unreliable, inaccurate, or incomplete. This paper focuses on a critical issue in the field of trajectory data acquisition and analysis—there is still no reliable and fully vetted trajectory dataset in the research community. The current practice for validating video-based trajectory can be classified as indirect methods and direct methods. Indirect methods of trajectory validation use algorithms to efficiently correct data anomalies without human intervention but may overlook detailed driving behaviors, whereas direct methods involve meticulous manual verification to preserve data fidelity but are labor-intensive and less scalable. The spatial-temporal maps (STMaps) method offers an additional layer of verification to affirm the accuracy and reliability of trajectory data. To enhance the performance, the deep spatial-temporal embedding model is proposed for trajectory instance segmentation on STMaps using the contrastive learning framework. The parity constraints at both pixel and instance levels guide the deep neural network to learn the embedding spaces that can be built on different backbone networks. The reconstructed Next Generation Simulation (NGSIM) highway dataset trajectory dataset is thoroughly validated against manually processed ground truth, and the error-free NGSIM data are refined to be a reliable resource for transportation research based on car-following behaviors, lane-change frequency, consistency, and jerk value measurements.

中文翻译：

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用于车辆轨迹验证和细化的深度时空嵌入

高角度摄像机通常用于交通研究中的轨迹数据收集。然而，如果没有细化和验证，通过视频处理软件获得的轨迹数据可能不可靠、不准确或不完整。本文重点关注轨迹数据采集和分析领域的一个关键问题——研究界仍然没有可靠且经过充分审查的轨迹数据集。目前验证基于视频的轨迹的实践可以分为间接方法和直接方法。轨迹验证的间接方法使用算法在无需人工干预的情况下有效纠正数据异常，但可能会忽略详细的驾驶行为，而直接方法需要细致的手动验证以保持数据保真度，但劳动密集型且可扩展性较差。时空地图（STMaps）方法提供了额外的验证层，以确认轨迹数据的准确性和可靠性。为了提高性能，提出了使用对比学习框架在 STMap 上进行轨迹实例分割的深度时空嵌入模型。像素和实例级别的奇偶校验约束引导深度神经网络学习可以在不同骨干网络上构建的嵌入空间。重建的下一代模拟（NGSIM）高速公路数据集轨迹数据集针对手动处理的地面实况进行了彻底验证，并且无差错的 NGSIM 数据经过提炼，成为基于跟车行为、变道频率、一致性和加加速度值测量。