基于道路监控的高速公路作业区碰撞风险预警  

Crash risk early warning in highway work zone based on road surveillance camera

作　　者：王博 刘昌赫[1,2] 张驰 张敏[4] 邬贵冬[5] WANG Bo;LIU Changhe;ZHANG Chi;ZHANG Min;WU Guidong(School of Highway,Chang’an University,Xi’an 710064,China;Engineering Research Center of Highway Infrastructure Digitalization,Ministry of Education,Xi’an 710000,China;School of Civil and Environmental Engineering,Nanyang Technological University,Singapore 639789,Singapore;College of Transportation Engineering,Chang’an University,Xi’an 710064,China;Sichuan Transportation Research Institute,Sichuan Vocational and Technical College of Communications,Chengdu 611130,China)

机构地区：[1]长安大学公路学院,陕西西安710064 [2]教育部公路基础设施数字化工程研究中心,陕西西安710000 [3]南洋理工大学土木与环境学院,新加坡639789 [4]长安大学运输工程学院,陕西西安710064 [5]四川交通职业技术学院四川交通运输研究院,四川成都611130

出　　处：《浙江大学学报（工学版）》2024年第6期1221-1232,共12页Journal of Zhejiang University：Engineering Science

基　　金：陕西省自然科学基础研究计划资助项目(2023-JC-YB-391);国家重点研发计划资助项目(2020YFC1512005);四川省科技计划资助项目(2022YFG0048);四川省交通运输厅科技资助项目(2022-ZL-04);山西省重点研发计划资助项目(202102020101014)。

摘　　要：为了更及时地掌握高速公路作业区车辆碰撞风险态势,提出基于闭路电视监控的作业区碰撞风险预警方法.采用计算机视觉技术进行车辆检测、坐标转换和车辆3D形态估计,获取作业区交通流和车辆信息.以改进事故时间指数(ITA)为上游过渡段的碰撞风险量化指标,依据警告区起点的交通流特征,实现上游过渡段的碰撞风险预测.通过集成一维卷积神经网络(1D CNN)、长短期记忆网络(LSTM)和注意力机制,构建基于1D CNN+LSTM+Attention (CLA)框架的碰撞风险预测模型.结果表明:所提数据采集方法满足碰撞风险预测的需求.相较其他冲突指标,ITA在风险量化中具有更适宜的敏感度.相较LSTM和1D CNN+LSTM,基于CLA的预警模型准确度更高,其拟合优度确定系数和均方根误差分别为0.805和0.359.所提方法能够提前90 s为作业区提供碰撞风险预警.A crash risk early warning method for work zones based on closed-circuit television system was proposed,in order to improve the timely detection of collision risks within highway work zones.Computer vision techniques were employed for vehicle detection,coordinate transformation,and 3D vehicle shape estimation,enabling the extraction of traffic flow and vehicle information within the work zone.Crash risks in the upstream transition section were predicted based on the traffic features at the starting point of the warning area,utilizing the improved time to accident(ITA)as a quantified indicator for crash risk assessment.By integrating the one-dimensional convolutional neural network(1D CNN),long short-term memory(LSTM)and the attention mechanism,a crash risk prediction model based on the 1D CNN+LSTM+Attention(CLA)framework was constructed.Results showed that the proposed data collection method met the needs for crash risk prediction.The proposed ITA exhibits better sensitivity in risk quantification,compared to other conflict indicators.The CLA-based prediction model demonstrates superior accuracy compared to the LSTM and 1D CNN+LSTM models,with a goodness of fit coefficient and root mean square error of 0.805 and 0.359,respectively.The proposed method can provide crash risk warnings for work zones 90 seconds in advance.

关 键 词：公路工程 作业区 计算机视觉 改进事故时间指数 碰撞风险预警 

分 类 号：U418[交通运输工程—道路与铁道工程]