Research On Modeling Discretionary Lane-Changing Behaviore Of Vehicles In Freeway

Lane change as one of the most basic micro-driving behavior of the vehicle will disturb the normal operation of the traffic flow,which may lead to traffic congestion.Therefore,the study on lane-changing behavior of vehicle will help traffic flow management and congestion mitigation.Meanwhile,it is important to construct lane change model to simulate lane-changing behavior of vehicle more accurately.The lane-changing behavior is mainly divided into discretionary and mandotary lane-changing behavior.The freeway is one of the most important driving environment of vehicle.The current study on lane-changing behavior of freeway vehicles mainly focused on mandatory lane-changing behavior and paid less attention to discretionary lane-changing behavior.However,discretionary lane-changing behavior also has an important impact on traffic flow in freeway.Therefore,this paper aims at research on the discretionary lane-changing decision-making behavior of vehicles in freeway,and takes manned vehicle and connected and autonomous vehicle as the research target respectively.Lane change research on manned vehicle aims at the moment,and lane change research on connected and autonomous vehicle aims at the future.Firstly,based on US 101 and 180 dataset in the NGSIM project,the key point recognition method of the discretionary lane-changing process of manual vehicle on the freeway is put forward,and the behavioral characteristics of manual vehicle during the discretionary lane-changing process in freeway is further analysed.Then,aiming at the problem that the existing lane-changing decision model of manual vehicle neglects integrating lane-changing preparation process and empirical formula model is too strong subjective,the thesis proposes a data-driven freeway manual vehicle discretionary lane-changing decision-making model based on deep learning and ensemble learning.Model asummption is that all freeway vehicles are manned vehicles.The model divides the discretionary lane-changing decision-making process of freeway manned vehicle into the four sub-processes:targe lane selection,target gap selection,target gap acceptance judgment and lane-changing preparation.The target lane selection problem can be transformed into a three-class classification problem,that is,the target vehicle select the target vehicle lane from the current lane,the left lane and the right lane according to the driving state of itself and its surrounding vehicles in the past several continuous time instant.Therefore,deep feedfoward neural network learner ensemble method is proposed to model target lane selection problem.The essence of the target gap selection problem is that the target vehicle selects the target gap from the current adjacent gap on the selected target lane and its adjacent rear clearance.The essence of the target gap acceptance judgment problem is to determine whether the selected target gap is suitable for target vehicle to insert immediately,and can be converted into a two-class classification problem.Target gap selection problem and target gap acceptability judgment problem are modeled by deep feedforward neural network ensemble method.The core of the lane-changing preparation process is to predict the longitudinal acceleration of the target vehicle and the thesis also uses the deep feedforward neural network learner ensemble method to model it.At last,based on the differences between information sensing,decision making and driving operation,the lane change model based on manned vehicle can not describe the driving behavior of the connected and autonomous vehicle accurately.In this thesis,a distributed discretionary lane-changing decision-making model framework vehicles is proposed based on the characteristics and functional advantages of connected and autonomous vehicles.The core of model framework is composed of the expected lane-changing decision model and the cooperative lane-changing decision model.By introducing the real time V2V information interaction characteristics of connected and autonomous vehicle into the traditional lane chenge model—MOBIL,we proposed a based improved MOBIL expected lane change decision model.According to the potential immediate interactive influence,cooperative lane change conditions of connected and autonomous vehicle are catergorized into four classes.Aimming at the four coorperative lane change conditons,we adopted to construct based game theory discretionary lane-changing decision-making model.Finally,the numerical simulation experiment is designed by using Matlab software to simulate the discretionary lane-changing decision-making model of freeway connected and autonomous vehicle.In order to compare the influence of the expected lane change decision model and the cooperative lane change decision model on the traffic flow,this paper designs four groups of different car-following+lane-changing model combinations to compare.