Abstract
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samples with partially-annotated captions. The model learns the meaning of each of the words without direct per-word supervision. At inference time, it generates a linguistic description of trajectories which captures maneuvers and interactions over an extended time interval. This generated description is used to refine predictions of the trajectories of multiple agents. We train and validate our model on the Argoverse dataset, and demonstrate improved accuracy results in trajectory prediction. In addition, our model is more interpretable: it presents part of its reasoning in plain language as captions, which can aid model development and can aid in building confidence in the model before deploying it.
Original language | English |
---|---|
Title of host publication | 2022 IEEE International Conference on Robotics and Automation, ICRA 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2868-2875 |
Number of pages | 8 |
ISBN (Electronic) | 9781728196817 |
DOIs | |
State | Published - 2022 |
Event | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States Duration: 23 05 2022 → 27 05 2022 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
---|---|
ISSN (Print) | 1050-4729 |
Conference
Conference | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 |
---|---|
Country/Territory | United States |
City | Philadelphia |
Period | 23/05/22 → 27/05/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.