  The future motion of traffic participants is inherently uncertain. To plan
safely, therefore, an autonomous agent must take into account multiple possible
trajectory 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 predictions to be associated with likelihoods. In our work, we model
motion prediction directly as a density estimation problem with a normalizing
flow between a noise distribution 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.
