TIP: Task-Informed Motion Prediction for Intelligent Vehicles
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2110.08750.pdf
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1.11 MB
Format
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Checksum (MD5)
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Author(s)
Huang, Xin
Rosman, Guy
Jasour, Ashkan
McGill, Stephen G.
Leonard, John J.
Williams, Brian C.
Date Issued
October 23, 2022
Publisher
IEEE
Citation
Huang, Xin, Rosman, Guy, Jasour, Ashkan, McGill, Stephen G., Leonard, John J. et al. 2022. "TIP: Task-Informed Motion Prediction for Intelligent Vehicles."
Version
Author's final manuscript
Abstract
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specifications. However, existing predictors are often optimized and evaluated via task-agnostic measures without accounting for the use of predictions in downstream tasks, and thus could result in sub-optimal task performance.
In this paper, we propose a task-informed motion prediction model that better supports the tasks through its predictions, by jointly reasoning about prediction accuracy and the utility of the downstream tasks, which is commonly used to evaluate the task performance. The task utility function does not require the full task information, but rather a specification of the utility of the task, resulting in predictors that serve a wide range of downstream tasks. We demonstrate our approach on two use cases of common decision making tasks and their utility functions, in the context of autonomous driving and parallel autonomy. Experiment results show that our predictor produces accurate predictions that improve the task performance by a large margin in both tasks when compared to task-agnostic baselines on the Waymo Open Motion dataset.
Description
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) October 23-27, 2022, Kyoto, Japan
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros47612.2022.9982100
