Lane-Changing Recognition of Urban Expressway Exit Using Natural Driving Data

Abstract

The traffic environment at the exit of the urban expressway is complex, and vehicle lane-changing behavior occurs frequently, making it prone to traffic conflict and congestion. To study the traffic conditions at the exit of the urban expressway and improve the road operation capacity, this paper analyzes the characteristics of lane-changing behaviors at the exit, adds driving style into the influencing factors of lane-changing, and recognizes one’s lane-changing intention based on driving data. A UAV (unmanned aerial vehicle) is used to collect the natural driving track data of the urban expressway diverge area, the track segments of vehicle lane-changing that meet the standards are extracted, and 374 lane-changing segments are obtained. K-means++ is used to cluster the driving style of the lane-changing segments which is grouped into three clusters, corresponding to “ordinary”, “radical”, and “conservative”. Through the random forest model used to identify and predict driving style, the accuracy reaches 93%. Considering the characteristics of a single time point and the characteristics of the historical time window, XGBoost, LightGBM, and the Stacking fusion model are established to recognize one’s lane-changing intention. The results show that the models can well recognize the lane-changing intention of drivers. The Stacking fusion model has the highest accuracy, while the LightGBM model takes less time; the model considering the characteristics of the historical time window performs better than the other one, which can better improve the prediction accuracy of lane-changing behavior.

Keywords

• traffic safety
• urban expressway exit
• driving style
• data driving
• lane-changing intention
• machine learning