  Predicting the future behavior of human road users remains an open challenge
for the development of risk-aware autonomous vehicles. An important aspect of
this challenge is effectively capturing the uncertainty inherent to human
behavior. This paper proposes an approach for probabilistic trajectory
prediction based on normalizing flows, which provides an analytical expression
of the learned distribution. We reformulate the problem of capturing
distributions over trajectories into capturing distributions over abstracted
trajectory features using an autoencoder, simplifying the learning task of the
normalizing flows. TrajFlow improves the calibration of the learned
distributions while achieving predictive performance on par with or superior to
state-of-the-art methods on the ETH/UCY and the rounD data set.

Robotics