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Impact of Qinghai-Tibet Plateau's Climate on Strength and Permeability of Concrete 青藏高原气候对混凝土强度和渗透性的影响
Q2 Engineering Pub Date : 2020-07-20 DOI: 10.19721/J.CNKI.1001-7372.2020.07.003
He Rui, Wang Tong, Cheng Hua-xin, Xue Cheng, Bai Yong-hou
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引用次数: 11
International Research Progress on Driving Behavior and Driving Risks 驾驶行为与驾驶风险的国际研究进展
Q2 Engineering Pub Date : 2020-06-20 DOI: 10.19721/J.CNKI.1001-7372.2020.06.001
Zhang Xu-xin, Wang Xue-song, Ma Yong, MA Qing-bian
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引用次数: 8
Risky Driving Behavior Recognition Based on Trajectory Data 基于轨迹数据的危险驾驶行为识别
Q2 Engineering Pub Date : 2020-06-20 DOI: 10.19721/J.CNKI.1001-7372.2020.06.008
Xue Qing-wen, Jiang Yu-ming, Lu Jian
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引用次数: 5
Multi-interference Lane Recognition Based on IPM and Edge Image Filtering 基于IPM和边缘图像滤波的多干扰车道识别
Q2 Engineering Pub Date : 2020-05-20 DOI: 10.19721/J.CNKI.1001-7372.2020.05.014
WU Hua-yue, Zhao Xiangmo
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引用次数: 2
Path Planning of Urban Autonomous Driving Using Laser Point Cloud Data 基于激光点云数据的城市自动驾驶路径规划
Q2 Engineering Pub Date : 2020-04-20 DOI: 10.19721/J.CNKI.1001-7372.2020.04.018
Guo Xiao-min, Liu Bi-jun, Long Jiang-yun, XU Hao-da, Lu Zhi
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引用次数: 0
End-to-end Autonomous Driving-behavior Decision Model Based on MM-STConv 基于mm - stv的端到端自动驾驶行为决策模型
Q2 Engineering Pub Date : 2020-03-20 DOI: 10.19721/J.CNKI.1001-7372.2020.03.015
Zhao Xiangmo, Lian Xin-yu, Liu Zhanwen, Shen Chao, Dong Ming
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引用次数: 5
Uncertainty Analysis of Head Injury via Reconstruction of Electric Two-wheeler Accidents 基于电动两轮车事故重构的头部损伤不确定性分析
Q2 Engineering Pub Date : 2020-01-20 DOI: 10.19721/J.CNKI.1001-7372.2020.01.018
Han Yong, XU Jia-shao, Shi Liang-liang, Gao Xiu-jing, Qian Yu-bin, Yang Zhen
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引用次数: 2
Fingerprint Components and Quantitative Analysis of Volatile Organic Compounds of Asphalt Materials 沥青材料挥发性有机物指纹图谱及定量分析
Q2 Engineering Pub Date : 2020-01-01 DOI: 10.19721/J.CNKI.1001-7372.2020.10.021
Yue Xiao, Xiwen Chang, Qiankun Dong, Zhanping You
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引用次数: 6
Freeway Accident Detection Model Based on Support Vector Machine 基于支持向量机的高速公路事故检测模型
Q2 Engineering Pub Date : 2011-07-13 DOI: 10.1061/41184(419)512
Baizhu Chen
The key technology of freeway accident detection was studied in order to set up a quick and efficient accident detection system and promote the efficiency of accident rescue. On the basis of characteristic analysis of the existing models, the freeway accident detection model based on support vector machine (SVM) theory was put forward. With database established by self-developed EAD-Simulations system, a simulation experiment was applied to the model. The effects of different kernel functions on detection performance were analyzed and the performance indexes, such as upstream input, upstream and downstream input and different input of features combination were studied. The results show that the excellent performances of the model are demonstrated by contrast with California model. The detection rate raises 179%; error detection rate drops at 0.50% and average detection time cuts down 81%. In addition, the optimal input characteristic combined by occupancy and flow rate in upstream is received.
为了建立快速高效的事故检测系统,提高事故救援效率,对高速公路事故检测的关键技术进行了研究。在对现有模型进行特征分析的基础上,提出了基于支持向量机理论的高速公路事故检测模型。利用自主开发的ead - simulation系统建立数据库,对模型进行仿真实验。分析了不同核函数对检测性能的影响,研究了上游输入、上下游输入、特征组合不同输入等性能指标。结果表明,与加利福尼亚模型相比,该模型具有良好的性能。检出率提高179%;错误检测率下降0.50%,平均检测时间缩短81%。此外,还得到了上游占用率和流量相结合的最优输入特性。
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引用次数: 2
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中国公路学报
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