trajdata：多个人类轨迹数据集的统一接口

摘要

最近几年，轨迹预测领域取得了显著进展，部分原因是由于发布了大量面向自动驾驶车辆（AVs）和行人运动跟踪的大规模真实世界人类轨迹数据集。虽然这些数据集对社区来说是一大福音，但它们各自使用定制和独特的数据格式和API，使得研究人员难以在多个数据集上训练和评估方法。为了解决这个问题，我们提出了trajdata：一个统一的接口，用于多个人类轨迹数据集。在其核心，trajdata提供了一个简单、统一和高效的轨迹和地图数据表示和API。作为其功能的演示，在这项工作中，我们对现有的轨迹数据集进行了全面的实证评估，为用户提供了对支撑当前大部分行人和AV运动预测研究的数据的深入了解，并根据这些见解提出了未来数据集的建议。trajdata采用宽松许可（Apache 2.0）并可在线访问https://github.com/NVlabs/trajdata。

English

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

trajdata：多个人类轨迹数据集的统一接口

trajdata: A Unified Interface to Multiple Human Trajectory Datasets

摘要

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