FloMo: Tractable Motion Prediction with Normalizing Flows

by   Christoph Schöller, et al.

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.


page 1

page 3


Residual Flows for Invertible Generative Modeling

Flow-based generative models parameterize probability distributions thro...

TPNet: Trajectory Proposal Network for Motion Prediction

Making accurate motion prediction of the surrounding traffic agents such...

Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation

Auto-regressive sequence generative models trained by Maximum Likelihood...

DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution

In autonomous driving, goal-based multi-trajectory prediction methods ar...

InFlow: Robust outlier detection utilizing Normalizing Flows

Normalizing flows are prominent deep generative models that provide trac...

Uncertainty estimation for Cross-dataset performance in Trajectory prediction

While a lot of work has been done on developing trajectory prediction me...

Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge

Predicting the future motion of vehicles has been studied using various ...