A Classified Driver's Lane-Change Decision-Making Model Based on Fuzzy Inference for Highly Automated Driving

Many efforts have been devoted to modeling drivers' lane-change decision-making process. However, most of them proposed a general model and ignored drivers' various driving habits. In this study, a classified driver's lane-change decision-making model based on fuzzy inference was proposed. A driving experiment was held to determine the membership functions. To meet various driving habits and preferences of drivers, the proposed model was classified into three types, namely aggressive, medium, and conservative. As model validation, a mathematical simulation was run to compare the classified fuzzy model with a conventional model proposed in the previous study. Simulation results showed that the classified fuzzy models could make differentiated lane change decisions. Furthermore, the classified fuzzy models made more stable lane-change decisions than the conventional model. This study suggests the potential of using the proposed model for the design of highly automated driving with different driving types.