Modelling Lane-changing Decision For Connected And Autonomous Vehicles Of Expressway Merging Area

Connected and Autonomous Vehicle(CAV)has received unprecedented attention from industry,government and academia due to its potential advantages.With the gradual popularization of CAV,road traffic flow will go through a phase where CAVs are mixed with Human-driving Vehicles(HV)and a purely autonomous driving phase with only CAVs.The lane-changing decision is the basis of the lane-changing behavior,which affects the efficient operation of the traffic system in the expressway merging area.Therefore,analyzing the lanechanging decision of CAVs in the merging area and constructing a lane-changing decision model in multiple environments has guiding significance for the management and control of road traffic flow with CAVs.The main research work of this paper is as follows:(1)Firstly,the lane-changing decision-making environment of the merging area was analyzed from the aspects of road conditions,vehicle conditions,lane-changing decisionmaking elements and simulation environment.Through the analysis of different merging area forms in the current expressway,the appropriate form of merging area was selected to carry out the following research;considering the driving differences of different types of vehicles,the car-following rules were studied according to the Na Sch model;based on the overall consideration of the influencing factors of the lane-changing decision model in the merging area,the operating characteristics and decision types of CAVs under different mixing rates were studied.(2)Secondly,based on optimization theory,a CAV lane-changing decision model in pure CAV traffic flow environment was constructed.Through the analysis of the lanechanging decision conditions in the pure CAV traffic flow environment,an optimization model for the CAV was established based on the influencing factors of the lane-changing decision;the feasibility of grouping optimization was analyzed by taking four vehicles in the merging area as an example,and the genetic algorithm was used to solve the problem;simulation evaluation was carried out to explore the traffic efficiency and environmental efficiency of optimization model.The results show that the proposed model can reduce the delay of CAVs in purely CAV traffic flow,improve the operating efficiency of the merging area,and reduce fuel consumption and exhaust emissions.(3)Finally,a CAV lane-changing decision model in the human-machine traffic flow environment was constructed based on game theory.On the basis of game theory,the interaction of lane-changing behavior was analyzed;the type of game theory lane-changing and game strategy were determined according to the type of interaction object,and the payment function and lane-changing decision model of CAVs were constructed;on this basis,a cooperative factor was introduced to describe the cooperative lane-changing characteristics of CAVs-CAVs;the constructed model parameters were calibrated by macroscopic traffic flow characteristics;taking the Minimizing Overall Braking Induced by Lane Change(MOBIL)model as the benchmark strategy,the proposed game decision model was simulated and evaluated in terms of traffic efficiency,homogeneity and safety.The simulation results show that,the optimal value determined for is 0.4;compared with the baseline model,the number and amplitude of vehicle speed fluctuations under the game theory lane-changing decision model are significantly reduced,and the cumulative frequency of the reciprocal of Time-To-Collision(TTC)is significantly increased,which can effectively improve traffic stability,traffic efficiency and reduce collision risks.