MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction

• URL: http://arxiv.org/abs/2402.12238v2
• Date: Wed, 15 Jan 2025 11:52:13 GMT
• Title: MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction
• Authors: Jiahe Chen, Jinkun Cao, Dahua Lin, Kris Kitani, Jiangmiao Pang,
• Abstract summary: We propose constructing a mixed Gaussian prior for a normalizing flow model for trajectory prediction. Our method achieves state-of-the-art performance in the evaluation of both trajectory alignment and diversity on the popular UCY/ETH and SDD datasets.
• Score: 72.70572835589158
• License:
• Abstract: To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity. The ineffectiveness comes from the conflict between the fact of asymmetric and multi-modal distribution of likely outcomes and symmetric and single-modal original distribution and supervision losses. Instead, we propose constructing a mixed Gaussian prior for a normalizing flow model for trajectory prediction. The prior is constructed by analyzing the trajectory patterns in the training samples without requiring extra annotations while showing better expressiveness and being multi-modal and asymmetric. Besides diversity, it also provides better controllability for probabilistic trajectory generation. We name our method Mixed Gaussian Flow (MGF). It achieves state-of-the-art performance in the evaluation of both trajectory alignment and diversity on the popular UCY/ETH and SDD datasets. Code is available at https://github.com/mulplue/MGF.

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