  The future motion of traffic participants is inherently uncertain. To plan
safely, therefore, an autonomous agent must take into account multiple possible
outcomes and prioritize them. Recently, this problem has been addressed with
generative neural networks. However, most generative models either do not learn
the true underlying trajectory distribution reliably, or do not allow
likelihoods to be associated with predictions. In our work, we model motion
prediction directly as a density estimation problem with a normalizing flow
between a noise sample and the future motion distribution. Our model, named
FloMo, allows likelihoods to be computed in a single network pass and can be
trained directly with maximum likelihood estimation. Furthermore, we propose a
method to stabilize training flows on trajectory datasets and a new data
augmentation transformation that improves the performance and generalization of
our model. Our method achieves state-of-the-art performance on three popular
prediction datasets, with a significant gap to most competing models.
