Decision Making for Intelligent Vehicles Based on Driver Type Analyzing in an Intersection

A two-stage method of intelligent vehicle(IV) decision making for traffic interaction in an intersection was proposed in this paper. In the first stage, a driver type recognition model was presented based on fuzzy logic to get the aggressive rate of social vehicles. In the second stage, a rule-based decision-making algorithm was used to generate the best behavior for IV based on vehicle's aggressive ratio and time to collision(TTC). Finally, a Co-simulation with Prescan and Matlab/Simulink was performed to verify the algorithm. The results show that the method could lead IV get through intersection safely and efficiently.