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.
@article{arxiv.2402.12238,
title = {MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction},
author = {Jiahe Chen and Jinkun Cao and Dahua Lin and Kris Kitani and Jiangmiao Pang},
journal= {arXiv preprint arXiv:2402.12238},
year = {2025}
}
Comments
Accepted by Neurips 2024. Code: https://github.com/mulplue/MGF