# TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction

arXiv: https://arxiv.org/abs/2304.05166 · HTML: https://mlsanity.com/paper/2304.05166/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction models](https://mlsanity.com/paper/2509.14801/) (2025)
- [Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction](https://mlsanity.com/paper/2603.10821/) (2026)
- [Emotion in an active inference model of human driving](https://mlsanity.com/paper/2608.07480/) (2026)
- [Clustered Randomized Smoothing for Stochastic Prediction Functions](https://mlsanity.com/paper/2608.12037/) (2026)

## Abstract (arXiv)

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.