BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction

Yiyao Zhu, Di Luan, Shaojie Shen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 8260-8271

Abstract


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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Zhu_2023_ICCV, author = {Zhu, Yiyao and Luan, Di and Shen, Shaojie}, title = {BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {8260-8271} }