Trajectory Prediction with Linguistic Representations
Name
CBMM-Memo-132.pdf
Size
1.15 MB
Format
Adobe PDF
Checksum (MD5)
224527c2b6ce010d79e81c2995a7220c
Author(s)
Kuo, Yen-Ling
Huang, Xin
Barbu, Andrei
McGill, Stephen G.
Katz, Boris
Leonard, John J.
Rosman, Guy
Date Issued
March 9, 2022
Publisher
Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA)
Series/Report no.
CBMM Memo;132
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 sam- ples 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.
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