A physics-informed Transformer model for vehicle trajectory prediction on highways

Abstract

Autonomous Vehicles (AVs) have made remarkable developments and are anticipated to replace human drivers. In transitioning from human-driven vehicles to fully AVs, one crucial task is to predict the trajectories of the subject vehicle and its surrounding vehicles in real time. Most existing methods of vehicle trajectory prediction on highways are based on physical models or purely data-driven models. However, they either yield unsatisfactory prediction performance or lack model interpretability and physical implications. This paper proposes a Physics-Informed Deep Learning framework that fully leverages the advantages of data-driven and physics-based models to go beyond the existing models. We use the Transformer neural network architecture with self-attention as Physics-Uninformed Neural Network (PUNN) and Intelligent Driver Model (IDM) as physical model to construct of Physics-Informed Transformer-Intelligent Driver Model (PIT-IDM). Extensive experiments have been conducted on two datasets with different traffic environments, i.e., Next Generation SIMulation (NGSIM) data in the US and the Ubiquitous Traffic Eyes (UTE) data in China, to verify model accuracy and efficiency. Compared with the three kinds of baselines by relative and absolute measures of effectiveness, the best performing PIT-IDM reduces longitudinal trajectory prediction errors for long horizons by 5%-50%, some even reduced up to 70%. Extensive empirical analyses have been carried out to verify its excellent spatio-temporal transferability and explore the physics-informed mechanism underlying this deep learning method. The training and inference time analysis indicates that although it takes longer to train PIT-IDM, it requires fewer calls and accumulates fewer errors with less computation time in real-world applications. The overall results further validate the efficacy of this Physics-Informed Deep Learning framework in enhancing model accuracy, interpretability, and transferability.