# SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories

arXiv: https://arxiv.org/abs/2202.02791 · HTML: https://mlsanity.com/paper/2202.02791/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 6 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [Reciprocal Learning Networks for Human Trajectory Prediction](https://mlsanity.com/paper/2004.04340/) (2020)
- [OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets](https://mlsanity.com/paper/2010.00890/) (2020)
- [AMENet: Attentive Maps Encoder Network for Trajectory Prediction](https://mlsanity.com/paper/2006.08264/) (2020)
- [Deep Social Force](https://mlsanity.com/paper/2109.12081/) (2021)

## Built on by (most cited first, 2 of 2)

- [Human Trajectory Prediction via Neural Social Physics](https://mlsanity.com/paper/2207.10435/) (2022)
- [Human Trajectory Forecasting with Explainable Behavioral Uncertainty](https://mlsanity.com/paper/2307.01817/) (2023)

## Abstract (arXiv)

Autonomous robots and vehicles are expected to soon become an integral part of our environment. Unsatisfactory issues regarding interaction with existing road users, performance in mixed-traffic areas and lack of interpretable behavior remain key obstacles. To address these, we present a physics-based neural network, based on a hybrid approach combining a social force model extended by group force (SFMG) with Multi-Layer Perceptron (MLP) to predict pedestrian trajectories considering its interaction with static obstacles, other pedestrians and pedestrian groups. We quantitatively and qualitatively evaluate the model with respect to realistic prediction, prediction performance and prediction "interpretability". Initial results suggest, the model even when solely trained on a synthetic dataset, can predict realistic and interpretable trajectories with better than state-of-the-art accuracy.