MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction

TL;DR

The paper introduces a novel mixed Gaussian prior for normalizing flow models to improve diversity and expressiveness in trajectory prediction, outperforming existing methods on standard datasets.

Contribution

It proposes the Mixed Gaussian Flow (MGF) model that constructs a multi-modal, asymmetric prior for better trajectory diversity without extra annotations.

Findings

Achieves state-of-the-art results on UCY/ETH and SDD datasets.

Improves trajectory diversity and alignment.

Provides better controllability in probabilistic predictions.

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.