[Paper Review] Generating Reliable and Efficient Predictions of Human Motion: A Promising Encounter between Physics and Neural Networks

Alessandro Antonucci, Gastone Pietro Rosati Papini+2 more|arXiv (Cornell University)|Jun 15, 2020

Autonomous Vehicle Technology and Safety17 references4 citations

TL;DR

This paper proposes a physics-informed neural network that embeds the Social Force Model (SFM) into its architecture to predict human motion reliably and efficiently. By learning only the SFM's parameters via a small synthetic dataset, the model achieves high accuracy and explainability, outperforming standard deep networks in generalization and interpretability while maintaining low complexity.

ABSTRACT

Generating accurate and efficient predictions for the motion of the humans present in the scene is key to the development of effective motion planning algorithms for robots moving in promiscuous areas, where wrong planning decisions could generate safety hazard or simply make the presence of the robot "socially" unacceptable. Our approach to predict human motion is based on a neural network of a peculiar kind. Contrary to conventional deep neural networks, our network embeds in its structure the popular Social Force Model, a dynamic equation describing the motion in physical terms. This choice allows us to concentrate the learning phase in the aspects, which are really unknown (i.e., the model's parameters) and to keep the structure of the network simple and manageable. As a result, we are able to obtain a good prediction accuracy with a small synthetically generated training set, and the accuracy remains acceptable even when the network is applied in scenarios quite different from those for which it was trained. Finally, the choices of the network are "explainable", as they can be interpreted in physical terms. Comparative and experimental results prove the effectiveness of the proposed approach.

Motivation & Objective

• To improve human motion prediction for robot navigation in human-populated environments.
• To address the limitations of pure deep neural networks (DNNs), such as poor generalization, high data needs, and lack of interpretability.
• To overcome the inflexibility of physics-based models like SFM by learning unknown parameters through neural networks.
• To create a hybrid approach that retains model interpretability while enabling flexible, data-driven parameter learning.

Proposed method

• The neural network architecture is explicitly designed to reflect the dynamics of the Social Force Model (SFM), embedding physical laws directly into its structure.
• The network learns only the unknown parameters of the SFM—such as force magnitudes and directions—through training on a small synthetic dataset.
• A multi-goal prediction strategy is employed, where the network evaluates trajectory hypotheses based on candidate waypoints (e.g., corridor exits).
• The model uses a classifier to rank hypotheses over time, selecting the most likely goal based on observed motion.
• The network is trained and validated using both synthetic trajectories and real-world LIDAR data from a hallway environment.
• The approach enables open-loop motion prediction with only one second of observed trajectory data.

Experimental results

Research questions

• RQ1 Can a neural network that embeds a physics-based model like SFM achieve better generalization than a standard DNN with limited training data?
• RQ2 How well can a physics-informed neural network predict human motion in scenarios different from the training distribution?
• RQ3 To what extent does embedding a physical model improve the explainability and interpretability of neural network decisions in human motion prediction?
• RQ4 Can the network accurately infer the intended goal of a pedestrian using only a short history of motion?
• RQ5 How does the performance of the hybrid model compare to pure DNNs and traditional SFM in real-world conditions?

Key findings

• The model achieves accurate motion predictions with only one second of observed trajectory data, demonstrating strong temporal generalization.
• After approximately 4 seconds of observation, the network correctly identifies the pedestrian’s intended goal (e.g., left or right exit) with high confidence.
• The network correctly discards incorrect goal hypotheses (e.g., the left exit) after about 4 seconds, indicating effective dynamic reasoning.
• The model maintains high prediction accuracy even in scenarios different from the training set, indicating robust generalization.
• The use of a small synthetic dataset is sufficient for training, reducing data requirements by one to two orders of magnitude compared to standard DNNs.
• The decisions of the network are explainable in physical terms, as they are grounded in the SFM’s force dynamics, enabling interpretability and error diagnosis.