Mixed Gaussian Flow for Diverse Trajectory Prediction
Jiahe Chen Jinkun Cao Dahua Lin Kris Kitani Jiangmiao Pang
Existing trajectory prediction studies intensively leverage generative
models. Normalizing flow is one of the genres with the advantage of being
invertible to derive the probability density of predicted trajectories.
However, mapping from a standard Gaussian by a flow-based model hurts the
capacity to capture complicated patterns of trajectories, ignoring the
under-represented motion intentions in the training data. To solve the problem,
we propose a flow-based model to transform a mixed Gaussian prior into the
future trajectory manifold. The model shows a better capacity for generating
diverse trajectory patterns. Also, by associating each sub-Gaussian with a
certain subspace of trajectories, we can generate future trajectories with
controllable motion intentions. In such a fashion, the flow-based model is not
encouraged to simply seek the most likelihood of the intended manifold anymore
but a family of controlled manifolds with explicit interpretability. Our
proposed method is demonstrated to show state-of-the-art performance in the
quantitative evaluation of sampling well-aligned trajectories in top-M
generated candidates. We also demonstrate that it can generate diverse,
controllable, and out-of-distribution trajectories. Code is available at
https://github.com/mulplue/MGF.