雷达与视觉传感器融合的高速公路全域车辆轨迹与交通参数检测方法  

Global vehicle trajectories and traffic parameters detecting method in expressway based on radar and vision sensor fusion

作　　者：戴喆 吴宇轩 董是 王建伟[1,2] 袁长伟[1,2] 左琛 DAI Zhe;WU Yu-xuan;DONG Shi;WANG Jian-wei;YUAN Chang-wei;ZUO Chen(School of Transportation Engineering,Chang'an University,Xi'an 710064,Shaanxi,China;Engineering Research Center of Highway Infrastructure Digitalization of Ministry of Education,Chang'an University,Xi'an 710064,Shaanxi,China)

机构地区：[1]长安大学运输工程学院,陕西西安710064 [2]长安大学道路基础设施数字化教育部工程研究中心,陕西西安710064

出　　处：《交通运输工程学报》2025年第1期197-210,共14页Journal of Traffic and Transportation Engineering

基　　金：国家自然科学基金项目(52402399);陕西省自然科学基础研究计划项目(2024JC-YBQN-0369);中国博士后科学基金项目(2022M710482);浙江省交通运输厅科技计划项目(2023016)。

摘　　要：为满足智慧高速公路在复杂交通环境下对交通参数的大范围检测需求,提出了一种毫米波雷达与视觉传感器数据融合的全域车辆轨迹与交通参数检测方法;利用部署于不同路侧立杆的毫米波雷达与视觉传感器采集原始数据,通过对多源检测目标数据进行时空同步、关联、融合及多目标追踪,设计了局部场景车辆轨迹融合检测算法;通过重构车辆运动时空信息,对多个连续不同场景的车辆轨迹进行合并,设计了连续多场景联动的全域车辆轨迹检测算法;根据全域车辆轨迹中提取的位置、速度等微观运动信息,设计了基于全域车辆轨迹的交通参数检测方法;在智慧高速公路试点建设路段进行试验数据采集与人工标注,对所提出方法进行验证。研究结果表明:在目标检测任务与轨迹追踪任务中,各局部场景与连续多场景的目标检测精度整体大于90%,追踪轨迹位置与车辆实际位置的偏差均值不超过0.2 m;在交通参数检测任务中,车辆在观测区域内检测速度与实际速度的平均绝对误差加权均值为3.41 km·h^(-1),平均绝对百分比误差加权均值为5.00%;区间平均速度、交通流量及交通密度等交通参数的检测精度可达车道级,检测结果与高速公路出口匝道及分流区的真实交通现象相一致。To meet the demand of smart expressways for a wide-range detection of traffic parameters in complex traffic environments,a global vehicle trajectories and traffic parameters detecting method was proposed based on the data fusion of millimeter-wave radar and vision sensor.Raw data was collected by millimeter-wave radars and vision sensors deployed on different roadside poles.Through spatio-temporal synchronization,association,fusion,and multi-object tracking of multi-source detection target data,an algorithm was designed for detecting vehicle trajectories in the local scene.An algorithm for detecting global vehicle trajectories in continuous scenes was designed by reconstructing vehicle spatio-temporal information and merging vehicle trajectories in continuous traffic scenes.A method for detecting traffic parameters based on global vehicle trajectories was designed through position,speed,and other microscopic motion information extracted from global vehicle trajectories.Experimental data was collected and manually labeled in a pilot section of the smart expressways and was used to validate the proposed method.Research results show that in vehicle detection task and trajectory tracking task,the overall vehicle detection accuracy of each local scene and multiple continuous traffic scenes is greater than 90%,and the deviation between the tracking trajectory position and the actual position of the vehicle is less than 0.2 m.In the traffic parameters detection task,the weighted average of mean absolute error between the detected vehicle's speed and the actual speed in the observation area is 3.41 km·h^(-1),and the weighted average of mean absolute percentage error is 5.00%.The detection accuracies of traffic parameters such as space mean speed,traffic volume and traffic density can reach lane level,and the detection results are consistent with the real traffic phenomena in the expressway exit ramp and diverging area.

关 键 词：智能交通 交通检测 多源数据融合 多目标追踪 时空信息重构 轨迹合并 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]