Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions

Abstract

To reduce the risk of traffic conflicts in merging area, driver’s behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings.

Driving Style Recognition Method Based on Risk Field and Masked Learning Techniques

“…Driver style recognition methods are primarily categorized into unsupervised learning, semi-supervised learning, and supervised learning. Unsupervised learning [2][3][4][5][6] and semi-supervised learning [7][8][9] methods require a smaller amount of data but face challenges in obtaining reliable sample features within limited data. In situations where data are sufficiently abundant, researchers opt for supervised learning for driver style recognition [10][11][12][13][14][15][16].…”