trajdata: A Unified Interface to Multiple Human Trajectory Datasets

Boris Ivanovic, Guanyu Song, Igor Gilitschenski, Marco Pavone

Keywords: multi-dataset benchmarking, data standardization, autonomous vehicles, pedestrian motion

TL;DR: In this work, we present trajdata, a unified interface to multiple human trajectory datasets, as well as a comprehensive empirical evaluation of the trajectory datasets underpinning much of AV and pedestrian motion forecasting research.

Abstract: 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.

Submission Number: 569