基于道路监控的高速公路作业区碰撞风险预警

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万方数据
维普

中文摘要：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。

外文标题：Crash risk early warning in highway work zone based on road surveillance camera

外文摘要：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.

外文关键词：

highway engineeringwork zonecomputer visionimproved accident time indexcrash risk early warning

作者：

王博、刘昌赫、张驰、张敏、邬贵冬

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作者单位：

长安大学公路学院,陕西西安 710064

教育部公路基础设施数字化工程研究中心,陕西西安 710000

南洋理工大学土木与环境学院,新加坡 639789

长安大学运输工程学院,陕西西安 710064

四川交通职业技术学院四川交通运输研究院,四川成都 611130

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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

基金：

陕西省自然科学基础研究计划国家重点研发计划四川省科技计划四川省交通厅科技项目山西省重点研发计划

项目编号：

2023-JC-YB-3912020YFC15120052022YFG00482022-ZL-04202102020101014

出版年：

2024

DOI：

10.3785/j.issn.1008-973X.2024.06.012

浙江大学学报(工学版)

浙江大学

浙江大学学报(工学版)

CSTPCD北大核心

影响因子：0.625

ISSN：1008-973X

年,卷(期)：2024.58(6)