# Back to square one: probabilistic trajectory forecasting without bells and whistles

arXiv: https://arxiv.org/abs/1812.02984 · HTML: https://mlsanity.com/paper/1812.02984/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2018.

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

- [Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction](https://mlsanity.com/paper/2403.18447/) (2024)
- [FloMo: Tractable Motion Prediction with Normalizing Flows](https://mlsanity.com/paper/2103.03614/) (2021)
- [TrajFlow: A Generative Framework for Occupancy Density Estimation Using Normalizing Flows](https://mlsanity.com/paper/2501.14266/) (2025)
- [Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction](https://mlsanity.com/paper/2509.13914/) (2025)

## Abstract (arXiv)

We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probability distribution over continuations of a given initial trajectory segment. We discuss it in relation to (more complicated) existing work and report on experiments on two standard datasets for trajectory forecasting: MNISTseq and Stanford Drones, achieving results on-par with or better than previous methods.