  Predicting multiple plausible future trajectories of the nearby vehicles is
crucial for the safety of autonomous driving. Recent motion prediction
approaches attempt to achieve such multimodal motion prediction by implicitly
regularizing the feature or explicitly generating multiple candidate proposals.
However, it remains challenging since the latent features may concentrate on
the most frequent mode of the data while the proposal-based methods depend
largely on the prior knowledge to generate and select the proposals. In this
work, we propose a novel transformer framework for multimodal motion
prediction, termed as mmTransformer. A novel network architecture based on
stacked transformers is designed to model the multimodality at feature level
with a set of fixed independent proposals. A region-based training strategy is
then developed to induce the multimodality of the generated proposals.
Experiments on Argoverse dataset show that the proposed model achieves the
state-of-the-art performance on motion prediction, substantially improving the
diversity and the accuracy of the predicted trajectories. Demo video and code
are available at https://decisionforce.github.io/mmTransformer.
