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

TL;DR

SFMGNet is a physics-inspired neural network that predicts pedestrian trajectories by integrating social force models with MLPs, achieving realistic, interpretable, and accurate predictions even trained on synthetic data.

Contribution

The paper introduces SFMGNet, a hybrid neural network combining social force models with MLPs for improved pedestrian trajectory prediction and interpretability.

Findings

Predicts realistic trajectories with state-of-the-art accuracy.

Operates effectively even when trained on synthetic data.

Enhances interpretability of pedestrian movement predictions.

Abstract

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.