A novel evaluation method for vehicle and traffic performance of different decision control of automatic lane change based on miniature model

Abstract

Research on automatic lane-change decision is mainly limited to simulation validation and lacks real vehicle validation methods because it is limited by experimental site and automatic driving technology on real vehicles. This paper puts forward a miniature traffic model to simulate the actual traffic scene and achieves to verify the decision control of automatic lane-change scene. The miniature intelligent traffic scene contains miniature vehicles, simplified miniature road traffic environment, and wireless network communication. After testing the basic functions of the miniature traffic scene model, such as automatic lane change, lane keeping, and automatic following, a semi-physical simulation test of the traffic flow composed of the model vehicle and the virtual vehicle is carried out. The semi-physical simulation test includes vehicle-following test of hybrid-condition intelligent driver model, lane-change test of lane-change decision two-vehicle gaming model, and minimizing overall braking induced by lane changes. The results show that the feasibility of the method and of the lane-change decision two-vehicle gaming model of automatic lane change is better in terms of traffic safety, traffic efficiency, and homogeneity. Compared to the minimizing overall braking induced by lane-change model test, the test of lane-change decision two-vehicle gaming model improves 2.26% and 1.5% in the average speed and total driving distance, respectively. The standard deviation of the traffic speed of the lane-change decision two-vehicle gaming model was 28.57% lower than the minimizing overall braking induced by lane changes. Compared to pure simulation verification, the method considers the effects of actual sensor signals and actuator control, which is closer to the actual application.