title: trajdata: A Unified Interface to Multiple Human Trajectory Datasets
summary: 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
paper_authors._id: 64c24ad690f24e254f6824de
paper_authors.name: Boris Ivanovic
paper_authors._id: 64c24ad690f24e254f6824df
paper_authors.name: Guanyu Song
paper_authors._id: 64c24ad690f24e254f6824e0
paper_authors.name: Igor Gilitschenski
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-27T11:38:28.498Z
paper_authors._id: 64893f0b9ab28735f1a1ec0b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64893f0b9ab28735f1a1ec0b/2lPBjV16l2qLEWOKU2zwR.jpeg
paper_authors.fullname: Igor Gilitschenski
paper_authors.type: user
paper_authors.user: igilitschenski
paper_authors._id: 64c24ad690f24e254f6824e1
paper_authors.name: Marco Pavone
paper_title: trajdata: A Unified Interface to Multiple Human Trajectory Datasets
paper_summary: 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
paper_ai_keywords: trajectory forecasting autonomous vehicles (AVs) pedestrian motion tracking trajdata local-world human trajectory datasets cross-valuation empirical evaluation MTEB (Multimodal Trajectory Evaluation Benchmark) uniform representation API (Application Programming Interface) map data Apache 2.0 License