Computer Science > Robotics
arXiv:2306.14161 (cs)
[Submitted on 25 Jun 2023 (v1), last revised 19 Aug 2023 (this version, v2)]
Title:BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction
View a PDF of the paper titled BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction, by Yiyao Zhu and 2 other authors
View PDFAbstract:Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi-level Future Fusion (BiFF) to explicitly capture future interactions between interactive agents. Concretely, BiFF fuses the high-level future intentions followed by low-level future behaviors. Then the polyline-based coordinate is specifically designed for multi-agent prediction to ensure data efficiency, frame robustness, and prediction accuracy. Experiments show that BiFF achieves state-of-the-art performance on the interactive prediction benchmark of Waymo Open Motion Dataset.
| Comments: | 18 pages, 12 figures |
| Subjects: | Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2306.14161 [cs.RO] |
| (orarXiv:2306.14161v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2306.14161 arXiv-issued DOI via DataCite |
Submission history
From: Yiyao Zhu [view email][v1] Sun, 25 Jun 2023 08:11:43 UTC (16,436 KB)
[v2] Sat, 19 Aug 2023 07:55:10 UTC (17,305 KB)
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View a PDF of the paper titled BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction, by Yiyao Zhu and 2 other authors
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