  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
