Exploring the Interaction Mechanism of Drivers’ Lane-Changing Behavior Based on Two-Vehicle Driving Simulation: A Recurrent Multi-Time Step Dynamic Game Model Considering Driver Styles

Lane-changing is a typical scenario of driver interaction, which causes a multitude of traffic crashes as well. Therefore, clarifying the lane-changing interaction process is of great significance for driving safety. Although extensive research has been carried out in this respect over the past decades, there are still unsolved challenges ranging from data collection to model construction. Moreover, the influence of human factors on the interactive decision-making and behavior is not fully taken into account. In this study, we start with behavioral data collection based on a two-vehicle simulated driving experiment, followed by the construction of a recurrent multi-time step game modeling framework, in which driving styles of both drivers are considered. More specifically, we design 8 lane-changing experimental scenarios at urban expressways, and conduct a two-vehicle simulated driving experiment and combine it with a driving style questionnaire (MDSI) to collect the interactive driving behavior data and driving style information of 40 experimenters, based on which we analyze the influence of different factors on these drivers' lane-changing interactive behavior. Thereafter, we select the significant influencing factors to construct a payoff function and propose a dynamic game model that can explain the mechanism of the interactive decision from a microscopic perspective. Compared with previous lane-changing game models, the proposed model incorporates driver styles into the game payoff function and establishes a recurrent multi-time step interactive decision-making framework by a dynamic game tree to capture the interdependence of drivers' interactive decisions. The model is solved by parallel computing methods. Comparing the vehicle trajectory prediction results calculated by different models, we find that the error of the multi-time step model considering driver styles is 57.94% lower than that of the single-time step model without driver styles, which implies that the model proposed in this study can better simulate the process of drivers' actual lane-changing interaction behavior by considering the drivers' prediction planning cycle and driving styles. Especially, the driver styles have a more significant effect on reducing the model errors (42.87%), which however, was often ignored in the existing studies.