Game theory-based mandatory lane change model in intelligent connected vehicles environment

Abstract

In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions.