Human Trajectory Prediction via Neural Social Physics (2207.10435v2)

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.

• The paper introduces Neural Social Physics (NSP), a framework that integrates deterministic social force models with neural networks to enhance trajectory prediction.
• It employs a neural differential equation and variational autoencoder to automatically learn parameters governing pedestrian movement.
• Tests on multiple datasets show NSP outperforms state-of-the-art methods by up to 70%, offering improved accuracy and interpretability.

Summary of "Human Trajectory Prediction via Neural Social Physics"

The paper "Human Trajectory Prediction via Neural Social Physics" authored by Jiangbei Yue, Dinesh Manocha, and He Wang introduces a novel methodology for predicting human trajectories by integrating prevalent model-based and model-free approaches. The approach, termed Neural Social Physics (NSP), is defined within a neural differential equation framework that combines explicit physics-based models, particularly a variant of the social force model, with deep neural networks. This fusion is designed to bolster both the interpretability of pedestrian movement and the model's ability to fit data effectively.

Core Methodology

1. Model Architecture: The NSP model involves a neural differential equation designed to predict human movement by integrating deterministic physics-inspired components with stochastic elements modeled using a Variational Autoencoder (VAE). The deterministic force model draws from concepts of social force models to define interactions between individuals and their environments.
2. Parameters and Learning: Key parameters governing pedestrian behaviors in the deterministic model, such as parameters related to goal-directed motion, collision avoidance, and environmental repulsion, are learned using neural networks rather than being manually set. This introduces flexibility and adaptability in representation.
3. Data and Results: Evaluation of the NSP was conducted on six widely acknowledged datasets, including the Stanford Drone Dataset and ETH/UCY datasets. Compared to fifteen recent deep learning methods, NSP demonstrated improved performance, with increases in predictive accuracy ranging from 5.56% to 70%, depending on the dataset.

Results and Implications

• Predictive Performance: NSP sets a new benchmark in trajectory prediction tasks across multiple datasets. It consistently outperformed existing state-of-the-art models in terms of both average displacement error (ADE) and final displacement error (FDE).
• Generalizability: One notable strength of the NSP model is its ability to generalize to scenarios with differing pedestrian densities—2-5 times the density presented in training data—while maintaining plausible trajectory predictions.
• Explainability: Unlike typical black-box deep learning models, the physics model aspect of NSP lends interpretability to the predictions, potentially elucidating pedestrian behaviors based on enforceable behaviors and interaction forces. This stands in stark contrast to purely statistical models that might lack clear interpretational avenues regarding pedestrian interaction dynamics.

Future Directions

The integration of physics-informed modeling with neural architectures represents a promising corridor for understanding and predicting complex human behaviors. The design of NSP can inspire further studies that seek to enhance model generalizability and explainability in domains extending beyond human trajectory prediction, such as autonomous driving systems or robotic motion planning.

Further exploration should also consider enhancing the granularity and fidelity of the simulated physics environments to accommodate a broader range of human behaviors, particularly under varying environmental contexts and constraints, which this paper has highlighted as a limitation with its 2D particle assumptions.

Overall, the NSP model's combination of physics-based modeling constraints with the learning capability of neural networks denotes a substantial step toward improving the predictive accuracy and interpretability of complex human trajectories.