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TrajFlow: Multi-modal Motion Prediction via Flow Matching
Created by
Haebom
Author
Qi Yan, Brian Zhang, Yutong Zhang, Daniel Yang, Joshua White, Di Chen, Jiachao Liu, Langechuan Liu, Binnan Zhuang, Shaoshuai Shi, Renjie Liao
Outline
TrajFlow is a novel flow matching-based motion prediction framework that provides efficient and accurate motion prediction for safe and informed decision-making in autonomous driving. It addresses the scalability and efficiency issues of existing generative prediction methods, and unlike existing generative approaches that require iid sampling and multiple inference passes to capture diverse outcomes, it predicts multiple possible future paths in a single pass, significantly reducing computational overhead while maintaining consistency between predictions. We propose a ranking loss based on the Plackett-Luce distribution to improve uncertainty estimation of predicted paths, and design a self-conditioning training technique that reuses the model’s own predictions to construct noisy inputs during the second forward pass, improving generalization performance and speeding up inference. Through extensive experiments on the large-scale Waymo Open Motion Dataset (WOMD), we demonstrate that TrajFlow achieves state-of-the-art performance across a variety of key metrics. Code and other details are available on the project website ( https://traj-flow.github.io/) .
Takeaways, Limitations
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Takeaways:
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It significantly improves computational efficiency by enabling multi-modal path prediction in a single pass.
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We improved uncertainty estimation of predicted paths using ranking loss based on Plackett-Luce distribution.
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Improved generalization performance and inference speed using self-conditioning training techniques.
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It achieves state-of-the-art performance on the WOMD dataset, demonstrating practical applicability in autonomous driving fields.
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Limitations:
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Although not explicitly mentioned in the paper, given the excellent performance on a specific dataset (WOMD), generalization performance on other datasets or environments requires further validation.
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A more detailed discussion and comparative analysis of the choice and performance of ranking loss functions using the Plackett-Luce distribution may be needed.
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A more in-depth analysis of the effectiveness of self-conditioning techniques may be needed.
