trajdata: A Unified Interface to Multiple Human Trajectory Datasets
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
64c24ad690f24e254f6824de
Boris Ivanovic
64c24ad690f24e254f6824df
Guanyu Song
64c24ad690f24e254f6824e0
Igor Gilitschenski
admin_assigned
2023-07-27T11:38:28.498Z
64893f0b9ab28735f1a1ec0b
https://cdn-avatars.huggingface.co/v1/production/uploads/64893f0b9ab28735f1a1ec0b/2lPBjV16l2qLEWOKU2zwR.jpeg
Igor Gilitschenski
user
igilitschenski
64c24ad690f24e254f6824e1
Marco Pavone
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
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 provides a unified interface for trajectory and map data, facilitating research across multiple human trajectory datasets for autonomous vehicles and pedestrian motion tracking.
trajectory forecasting human trajectory datasets autonomous vehicles pedestrian motion tracking trajdata uniform representation API empirical evaluation motion forecasting Apache 2.0 license