Yue, J, Manocha, D and Wang, H orcid.org/0000-0002-2281-5679 (2022) Human Trajectory Prediction via Neural Social Physics. In: Lecture Notes in Computer Science. The European Conference on Computer Vision 2022, 23-27 Oct 2022, Tel Aviv. Springer , pp. 376-394. ISBN 978-3-031-19829-8
Abstract
Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. The former include rule-based, geometric or optimization-based models, and the latter are mainly comprised of deep learning approaches. In this paper, we propose a new method combining both methodologies based on a new Neural Differential Equation model. Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters. The explicit physics model serves as a strong inductive bias in modeling pedestrian behaviors, while the rest of the network provides a strong data-fitting capability in terms of system parameter estimation and dynamics stochasticity modeling. We compare NSP with 15 recent deep learning methods on 6 datasets and improve the state-of-the-art performance by 5.56%–70%. Besides, we show that NSP has better generalizability in predicting plausible trajectories in drastically different scenarios where the density is 2–5 times as high as the testing data. Finally, we show that the physics model in NSP can provide plausible explanations for pedestrian behaviors, as opposed to black-box deep learning. Code is available: https://github.com/realcrane/Human-Trajectory-Prediction-via-Neural-Social-Physics.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author produced version of a conference paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Human trajectory prediction; Neural differential equations |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union 899739 |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jul 2022 12:14 |
Last Modified: | 22 Oct 2023 00:13 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-031-19830-4_22 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189355 |