Stackelberg-Game-Based Vehicle Lane-Changing Model Considering Driving Style

At present, most of the game decision lane-changing models only consider the state data of the vehicle at the current moment. However, the driving style has a significant impact on the vehicle trajectories, which should be taken into account in the lane-changing process. Moreover, most of the game models are static and do not take into account the sequence of the vehicle lane-changing. This paper proposed a Stackelberg-game-based vehicle lane-changing model considering driving style. Firstly, the NGSIM public dataset is selected for this research and the data screen flow is processed. The K-means algorithm is applied to exchange data clustering. Based on the analysis of vehicle lane changing features under different driving style, the characteristics of the corresponding data under different style are extracted. The quantic-polynomial programming algorithm is used to generate a vehicle lane changing trajectory under different driving styles. Then, based on Stackelberg-game theory and lane changing trajectory considering vehicle driving style, the decision-making benefits is designed to establish the vehicle lane change decision model. At last, the experimental verification is carried out to compare the lane-changing trajectories of vehicles with different driving styles and the actual data. The reasons for the errors are analyzed and the calculation results of the model with the results without considering the driving style are compared. The experimental results show that the prediction accuracy of the model is improved after considering the vehicle driving style. The advantages of the model established in this paper are explained.