Highway Main Lane Vehicles Driving Behavior Prediction Based on Residual-Transformer

Highways, as a type of high-grade road, are an essential component of intelligent connected vehicle testing and a crucial aspect in achieving fully autonomous driving technology. To effectively predict the driver behaviors when encountering merging vehicles in high-speed merging areas, this paper proposes a high-speed main lane vehicle driving behavior model that combines Convolutional Neural Network (CNN) based on Residual Structure with a Transformer network. This model takes input in the form of the target vehicle’s motion state information and surrounding vehicle interaction data, and predicts the current driving behavior state of the target vehicle as well as the next-stage driving behavior. Finally, the effectiveness of this model is validated on the Next-Generation Simulation (NGSIM) dataset and the Exits and Entries Drone (exiD) dataset. Comparative experiments with the Time Series Transformer (TST) model and the Multi-head Attention CNN-LSTM (MCNN-LSTM) model are conducted. The results indicate that the proposed model outperforms other models in aspects such as driving behavior recognition, with a correct identification rate of 94% and 95% in the two major datasets, respectively. The driving behavior prediction model presented in this paper can assist intelligent connected vehicles in high-speed ramp merging decision-making and planning.