A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction

TL;DR

This paper introduces PERL, a hybrid framework combining physics-based and data-driven models for vehicle trajectory prediction, achieving better accuracy, interpretability, and efficiency with limited data.

Contribution

The paper presents a novel PERL framework that integrates physics models with residual learning, enhancing prediction accuracy and interpretability in vehicle trajectory forecasting.

Findings

PERL outperforms physics, data-driven, and PINN models in accuracy with small datasets.

PERL converges faster during training, requiring fewer samples.

Sensitivity analysis confirms robustness across different residual and physics models.

Abstract

In vehicle trajectory prediction, physics models and data-driven models are two predominant methodologies. However, each approach presents its own set of challenges: physics models fall short in predictability, while data-driven models lack interpretability. Addressing these identified shortcomings, this paper proposes a novel framework, the Physics-Enhanced Residual Learning (PERL) model. PERL integrates the strengths of physics-based and data-driven methods for traffic state prediction. PERL contains a physics model and a residual learning model. Its prediction is the sum of the physics model result and a predicted residual as a correction to it. It preserves the interpretability inherent to physics-based models and has reduced data requirements compared to data-driven methods. Experiments were conducted using a real-world vehicle trajectory dataset. We proposed a PERL model, with the Intelligent Driver Model (IDM) as its physics car-following model and Long Short-Term Memory (LSTM) as its residual learning model. We compare this PERL model with the physics car-following model, data-driven model, and other physics-informed neural network (PINN) models. The result reveals that PERL achieves better prediction with a small dataset, compared to the physics model, data-driven model, and PINN model. Second, the PERL model showed faster convergence during training, offering comparable performance with fewer training samples than the data-driven model and PINN model. Sensitivity analysis also proves comparable performance of PERL using another residual learning model and a physics car-following model.