Fuzzy Control Model of Intelligent Lane-Changing Decision Based on Genetic Algorithm Optimization

Based on the fuzzy inference system, it constructs a discretionary lane-changing decision model for different types of preceding vehicles and compares and analyzes the parameter differences of their input membership functions. According to the driver questionnaire survey, the model uses three parameters that drivers can easily percept as the model input—preceding vehicle distance in the current lane, preceding vehicle distance in the target lane, and following-vehicle distance in the target lane—uses Next-Generation Simulation (NGSIM) vehicle trajectory data to optimize the input membership functions of models based on genetic algorithm according to different vehicle lane-changing trajectory data to analyze the impact of the preceding vehicle type before lane change to the intelligent lane-changing decision. Results show that the membership function parameters of fuzzy control systems are quite different under different preceding vehicle sizes, that is, when the driver makes a lane-changing decision, the subjective judgment of the three-vehicle distance is obviously different, which indicates that the preceding vehicle type will greatly influence the driver’s lane-changing decision.