Lane Changing Identification and Trajectory Analysis in Confluence Area of Multi-lane Expressway
In order to improve the traffic safety in the expressway confluence area,the characteristics of vehicle lane changing trajectory are studied and the intention of lane changing is identified.Firstly,the UAV is used to capture the running track video of vehicles in the expressway confluence area under natural state.The time and position information of each track are extracted with Tracker kinematics track software,the vehicle running track is obtained,and the number of natural driving trajectories is accumulated.The segments of car-following and lane-changing are obtained according to the data of time distance,speed and acceleration,and the time and distance distributions of 2 types of driving behaviors are analyzed.Secondly,combining with vehicle speed,acceleration and variable acceleration,K-means++clustering is used to classify the driving styles into'conventional','radical'and'conservative'.Stochastic forest model is determined to identify the trajectories of drivers with different styles,and then Stacking fusion model based on XGBoost and LightGBM is selected to identify the intention of vehicle lane changing.Finally,a machine learning CNN-LSTM model is constructed to predict the trajectories.The result shows that(1)the comprehensive effect of clustering driving styles into 3 categories by using K-means++ is the best,the clustering result is selected as the label value of driving style in trajectory segments,and the accuracy of random forest is good;(2)the accuracy of using the Stacking fusion model is suitable for driving trajectory recognition,R2 is determined to be at the level of 0.62 from the perspective of lane changing trajectory prediction accuracy,when the time window is 2 s,the model can make a more accurate prediction on lane changing trajectory prediction;(3)the study has realized the purpose of predicting the trajectory through driving behavior recognition,providing an application basis for realizing real-time collision risk identification of vehicles.At the same time,it can optimize the driving trajectory of autonomous vehicles,and improve the safety of expressway entrances under complex traffic flow conditions.
traffic engineeringtransport system safetycluster analysislane changing behaviorconfluence area
NETL
NSTL
万方数据
