  Predicting the future behavior of human road users is an important aspect for
the development of risk-aware autonomous vehicles. While many models have been
developed towards this end, effectively capturing and predicting the
variability inherent to human behavior still remains an open challenge. This
paper proposes TrajFlow - a new approach for probabilistic trajectory
prediction based on Normalizing Flows. 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 outperforms state-of-the-art behavior prediction
models in capturing full trajectory distributions in two synthetic benchmarks
with known true distributions, and is competitive on the naturalistic datasets
ETH/UCY, rounD, and nuScenes. Our results demonstrate the effectiveness of
TrajFlow in probabilistic prediction of human behavior.
