Human Trajectory Prediction via Neural Social Physics

TL;DR

This paper introduces Neural Social Physics, a novel trajectory prediction model combining physics-based and deep learning methods, achieving state-of-the-art results and better generalization in diverse pedestrian scenarios.

Contribution

The paper presents NSP, a new neural differential equation model that integrates explicit physics with deep learning for improved pedestrian trajectory prediction.

Findings

Outperforms 15 recent methods on 6 datasets with 5.56%-70% accuracy improvement.

Demonstrates better generalization in high-density, different scenarios.

Provides interpretable physics-based explanations for pedestrian behaviors.

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.