A physics-informed temporal convolutional network model for vehicle lane-changing trajectory prediction on highways

The trajectory prediction of the subject vehicle contributes to the autonomous vehicle’s recognition and evaluation of potentially risky situations in advance, serving as an essential basis for subsequent decision-making and planning. Accurately predicting future trajectories of traffic participants has become a significant issue in enhancing autonomous vehicles’ efficiency and safety. Purely physics-driven or data-driven methods are the main research direction for vehicle lane-changing trajectory prediction on highways at present. However, these methods are either not applicable to complex and highly interactive driving environments or raise concerns about model interpretability and physical implications. To fully utilize the strengths and mitigate the disadvantages of both data-driven and physics-driven models, a physics-informed deep learning (PIDL) model that integrates physical information into a deep learning model is proposed to accurately predict vehicle lane-changing trajectories in this paper. An attention-temporal convolutional network model and a kinematic model are utilized as the data-driven and physics-driven components of the PIDL model, respectively. The HighD dataset is utilized to validate the outstanding performance of the model. The comparison results with baseline models indicate that the model can achieve more precise and interpretable predictions of vehicle lane-changing trajectories. The results of the multi-feature input experiments demonstrate that inputting more feature information on vehicles into the model can improve its prediction performance. The excellent generalization of the model in both temporal and spatial dimensions is verified by the transfer experiments. This PIDL model offers a promising avenue for enhancing the accuracy, interpretability, and generalization of trajectory prediction for autonomous vehicles.