Yan Li, Han Zhang, Qi Wang, Zijian Wang, Xinpeng Yao
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引用次数: 0
摘要
为降低并线区域的交通冲突风险,对驾驶员的行为模式进行了分析,为交通管制和冲突风险预警提供理论依据。研究人员使用无人驾驶飞行器(UAV)在两种不同类型的并线区域(高速公路交汇处和服务区)采集视频。基于 YOLOv5(You Only Look Once 第五版)和 Deep SORT,构建了车辆跟踪检测模型,以提取交通流量、速度、车辆类型和行驶轨迹。分析了加速/减速分布和车辆变道行为。总结了不同车型对车速和变道行为的影响。根据这些数据,选择速度、加速度和变加速度的平均值和标准偏差作为驾驶风格聚类的特征变量。为避免特征间的冗余信息,进行了主成分降维,并利用降维后的数据进行 K-means 和 K-means++ 聚类,得到三种驾驶风格。结果表明,不同类型并线区域的车辆驾驶行为存在明显差异,在进行交通冲突预警时应充分考虑不同区域的特点。
Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions
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.