  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.
