SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories(Accepted to AAAI-MAKE '22)

Sakif Hossain, Fatema T. Johora, Jörg P. Müller, Sven Hartmann and Andreas Reinhardt

Technical University of Clausthal, Germany

[Paper]

Abstract

Autonomous robots and vehicles are expected to become an integral part of our environment soon. Unsatisfactory issues (esp. for path planning) 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 concerning realistic prediction, prediction performance, and prediction "interpretability". Initial results suggest that, even when solely trained on a synthetic dataset, the model can predict realistic and interpretable trajectories with better than state-of-the-art accuracy.

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@inproceedings{hossain22sfmgnet,
author = {Sakif Hossain and Fatema T. Johora and Jörg P. Müller and Sven Hartmann and Andreas Reinhardt},
booktitle = {Proceedings of the AAAI Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE)},
pages = {1--16},
title = {{SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories}},
url = {https://proceedings.aaai-make.info/AAAI-MAKE-PROCEEDINGS-2022/paper14.pdf},
year = {2022}
}