The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking. While such datasets have been a boon for the community, they each use custom and unique data formats and APIs, making it cumbersome for researchers to train and evaluate methods across multiple datasets. To remedy this, we present trajdata: a unified interface to multiple human trajectory datasets. At its core, trajdata provides a simple, uniform, and efficient representation and API for trajectory and map data. As a demonstration of its capabilities, in this work we conduct a comprehensive empirical evaluation of existing trajectory datasets, providing users with a rich understanding of the data underpinning much of current pedestrian and AV motion forecasting research, and proposing suggestions for future datasets from these insights. trajdata is permissively licensed (Apache 2.0) and can be accessed online at https://github.com/NVlabs/trajdata

翻译：近年来，轨迹预测领域发展迅猛，部分得益于自动驾驶车辆和行人运动追踪领域大量大规模真实世界人类轨迹数据集的发布。尽管这些数据集对学界大有裨益，但每个数据集均采用定制化的独特数据格式与应用程序编程接口，导致研究人员在跨数据集训练和评估方法时极为繁琐。为此，我们提出trajdata：一个面向多个人类轨迹数据集的统一接口。其核心在于为轨迹与地图数据提供简洁、统一且高效的表征方式与API。作为能力验证，本研究对现有轨迹数据集进行了全面的实证评估，使用户能够深入理解支撑当前多数行人与自动驾驶车辆运动预测研究的基础数据，并基于这些洞见为未来数据集提出建议。trajdata采用宽松授权协议（Apache 2.0），可通过https://github.com/NVlabs/trajdata 在线访问。