Lane Changing Model Of Connected Autonomous Vehicle In Mixed Flow Environment

With the development of automobile industry to the direction of intelligent and networked,the traffic flow composition will present a common situation of manual driving vehicles,automatic driving vehicles,networked vehicles and connected autonomous vehicles,which is called mixed flow environment.As the future development form of automobile,connected autonomous vehicle plays an important role in the future transportation system.The establishment of a scientific and reasonable lane changing model plays an important role in ensuring traffic safety and promoting the development of future traffic system.However,there are few researches on the lane changing model of connected autonomous vehicles in the mixed flow environment,and many researches only consider the lane changing scene in which there is one or no vehicle in the target lane,do not consider the competitiveness of vehicles in the target lane,and lack the lane changing model applicable to the complex traffic scene.Therefore,this paper will analyze the lane changing characteristics of vehicles in the mixed flow environment,analyze the interaction behavior between connected autonomous vehicles and other vehicles in the process of lane changing,build a lane changing scene in line with the actual driving environment,establish the lane changing track equation based on the quintic polynomial,generate the track set,and determine the optimal lane changing with the goal of higher efficiency,better comfort and lower fuel consumption in the lane changing process The trajectory provides theoretical support for the study of lane changing model of connected autonomous vehicles in mixed flow environment.This paper first analyzes the lane changing characteristics and scene of connected autonomous vehicles in the mixed flow environment.Based on the study of traditional vehicle lane changing behavior,this paper discusses the lane changing types of vehicles in mixed flow environment and the lane changing behavior characteristics of connected autonomous connected vehicles,analyzes the interaction behavior in the process of lane changing of connected autonomous vehicles,aiming at minimizing the impact on traffic flow,and analyzes the lane changing decision of connected autonomous vehicles in combination with different lane changing scenarios.This paper establishes a multi-agent dynamic lane changing model.The coordinate system is established.Based on the quintic polynomial,the lane change trajectory and collaborative lane change trajectory are planned to generate a trajectoryset.Aiming at the higher efficiency,the better comfort and the lower fuel consumption in the lane change process,the trajectory optimization algorithm considering multi-agent is established to obtain the lane change trajectory and collaborative trajectory,and the dynamic update mechanism of lane change trajectory is studied.This paper carries out simulations of typical lane changing scenes.A typical lane changing scene is built to simulate the multi-agent dynamic lane changing model proposed in this paper,and a comparative analysis is made with the lane changing trajectory based on the static planning mechanism.The results show that the multi-agent dynamic lane changing model established in this paper can make the vehicle adjust its lane changing trajectory according to the changes of other vehicles' driving behaviors during the lane changing process,and improve the safety of the lane changing process.In this paper,when analyzing the lane changing behavior of connected autonomous vehicles in the mixed flow environment,it is proposed that the lane changing process of connected autonomous vehicles should first identify the types of vehicles participating in lane changing,and consider the influence of driving behavior of collaborative lane changing vehicles on traffic flow from the Perspective of multi-agent,which is innovative compared with the existing research.The research results of this paper can be used to guide the driving of connected autonomous vehicles,and can also be used in simulation software to simulate the micro driving behavior in mixed flow environment,as the basis for future traffic policy-making.