MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction
Jiahe Chen, Jinkun Cao, Dahua Lin, Kris M. Kitani, Jiangmiao Pang
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.
BibTeX
@inproceedings{
chen2024mgf,
title={{MGF}: Mixed Gaussian Flow for Diverse Trajectory Prediction},
author={Jiahe Chen and Jinkun Cao and Dahua Lin and Kris M. Kitani and Jiangmiao Pang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=muYhNDlxWc}
}