Decision-making model of lane-change behavior based on integrated cognitive vehicle cluster situations

Abstract

Lane-change behavior is the most important factor that explains changes in vehicle cluster situations, and it is of great importance to traffic safety. It plays a significant role in research pertaining to traffic flow and theoretical studies about active vehicle safety. This is particularly prevalent in intelligent (auto- and assisted-) driving systems, where decision-making processes with respect to lane-change behavior can be identified. Based on previous studies and by considering the impact of vehicle cluster situations on task prioritization with regard to comprehensive cognitive reactions of lane-change processes, a new lane-changing decision-making model was developed. This model considers a range of factors, including the propensity of the driver of the target vehicle and the characteristics of surrounding vehicles (e.g., decision-making, distance, and speed). This model supports the recreation of the complex system wherein vehicle clusters are found. The field data, virtual reality data, and simulation data were used to validate the aforementioned model. It was found that the modeled results can provide guidance on whether drivers will decide to change lanes.