Comprehensive Competitiveness-based Autonomous Driving Human-imitative Lane-changing Model Under Gravity Theory
To effectively characterize the vehicle lane-changing decision-making mechanism in an automated driving environment on urban expressways,this paper proposes a human-imitative lane-changing decision model based on comprehensive competitiveness.The method considers the impact of the positional,driving style and vehicle motion attributes of the subject vehicle and the adjacent vehicles on the subject vehicle's lane-changing behavior.The three factors of adjacent front vehicle distance,speed difference and driving style were used as the human-imitative lane-changing willingness attributes of the autonomous vehicle to quantitatively characterize the subject vehicle's lane-changing willingness.Then,based on the pessimistic criterion in human decision-making,the potential competitive behavior between the adjacent vehicles and the subject vehicle in the process of changing lanes was analyzed.The concept of potential competitive intensity was proposed using the headway ratio and driving style differences.Considering the influence of environmental stability on driving comfort,this study uses the concepts of'velocity pseudo-distance'and'acceleration pseudo-distance'to measure the environmental stability after lane changing.A comprehensive competitiveness lane-changing decision model with vehicle lateral speed as the solution objective was established by combining gravitational theory.In the model calibration,the Ubiquitous Traffic Eyes open-source dataset was screened to obtain the non-forced lane changing segment data,and the parameters of the model were calibrated using the ant colony algorithm.A randomized cross-validation method was used for validation,and the correct rate was used as the evaluation index of model accuracy and generalization ability,which was compared with the traditional model.The results show that when the training-validation ratio is 72％:28％,65％:35％,57％:43％,and 50％:50％,the average correct rate interval is 87.67％to 90.34％,which indicates that the model is robust and feasible.The proposed model shows higher prediction accuracy compared with the traditional model,which can provide a basis for lane selection of the vehicles in the autonomous driving environment.
intelligent transportationlane-changing decisioncomprehensive competitivenessautonomous drivinglateral velocitytrajectory data
万方数据
维普
