The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Yanan Zhao - One of the best experts on this subject based on the ideXlab platform.
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Research on the Quantitative Evaluation of the Traffic Environment Complexity for Unmanned Vehicles in Urban Roads
IEEE Access, 2021Co-Authors: Shijuan Yang, Li Gao, Yanan ZhaoAbstract:The primary goal of the paper is to explore the human-vehicle-road interaction mechanism in the Traffic Environment and evaluate the Traffic Environment complexity for unmanned vehicles in urban roads. In particular, we propose the quantitative evaluation models of the Traffic Environment complexity for unmanned vehicles in urban roads in the paper. Specifically, the structure system of the complex Traffic Environment in urban roads is dissected from the aspect of human-vehicle-road, laying the basis for proposing influencing factors of Traffic Environment complexity. We divide the complex Traffic Environment into the static Traffic Environment and the dynamic Traffic Environment in light of relative static and dynamic characteristics of various Environmental elements. For the complexity of the static Traffic Environment, the quantitative evaluation model is established by the grey relation analysis method that converts static Environment complexity into the relation degree of static complexity’s influencing factors. For the complexity of the dynamic Traffic Environment, the quantitative evaluation model is established based on the improved gravitation model that introduces the concepts of equivalent mass and the contribution degree of the unmanned vehicles’ driving strategy. Besides, we evaluate the Traffic Environment complexity in the designed scenario by quantitative models proposed in the paper and existing evaluation models of Traffic Environment complexity in urban roads. The calculating process and results show that the proposed quantitative models of Traffic Environment complexity are more convenient and more reasonable, which provide a new idea and a method to evaluate the Traffic Environment complexity.
Guoqiang Mao - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM Workshops - Roadside Sensor Based Vehicle Counting Incomplex Traffic Environment
2019 IEEE Globecom Workshops (GC Wkshps), 2019Co-Authors: Chen Zhiqiang, Zhen Liu, Yilong Hui, Tom H. Luan, Guoqiang MaoAbstract:The 5G networks are expected to support autonomous driving to enhance driving experience and travel efficiency. Toward this goal, the valuable data generated by the complex and dynamic transportation system need to be collected. In this paper, we propose a roadside sensor-based vehicle counting scheme for collecting Traffic flow information in complex Traffic Environment. In the scheme, the roadside sensor can sense the magnetic data, where the magnetic flux magnitude will be changed if a vehicle passes though the sense coverage of the sensor. Based on this, we first analyze the change of the magnetic signals in the complex Traffic Environment and process the magnetic signals collected by the roadside sensor. Then, an integrated algorithm is designed to detect and count the Traffic flow by considering the features of the collected signals. After this, we carry out experiments to evaluate the performance of the proposed vehicle counting scheme and analyze the vehicle counting error. According to the features of the error, we further design the error compensation strategy to correct the experiment results. Experimental verification results show that the vehicle counting accuracy before and after the error compensation in the complex Traffic Environment are 97.07% and 98.5%, respectively.
Shijuan Yang - One of the best experts on this subject based on the ideXlab platform.
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Research on the Quantitative Evaluation of the Traffic Environment Complexity for Unmanned Vehicles in Urban Roads
IEEE Access, 2021Co-Authors: Shijuan Yang, Li Gao, Yanan ZhaoAbstract:The primary goal of the paper is to explore the human-vehicle-road interaction mechanism in the Traffic Environment and evaluate the Traffic Environment complexity for unmanned vehicles in urban roads. In particular, we propose the quantitative evaluation models of the Traffic Environment complexity for unmanned vehicles in urban roads in the paper. Specifically, the structure system of the complex Traffic Environment in urban roads is dissected from the aspect of human-vehicle-road, laying the basis for proposing influencing factors of Traffic Environment complexity. We divide the complex Traffic Environment into the static Traffic Environment and the dynamic Traffic Environment in light of relative static and dynamic characteristics of various Environmental elements. For the complexity of the static Traffic Environment, the quantitative evaluation model is established by the grey relation analysis method that converts static Environment complexity into the relation degree of static complexity’s influencing factors. For the complexity of the dynamic Traffic Environment, the quantitative evaluation model is established based on the improved gravitation model that introduces the concepts of equivalent mass and the contribution degree of the unmanned vehicles’ driving strategy. Besides, we evaluate the Traffic Environment complexity in the designed scenario by quantitative models proposed in the paper and existing evaluation models of Traffic Environment complexity in urban roads. The calculating process and results show that the proposed quantitative models of Traffic Environment complexity are more convenient and more reasonable, which provide a new idea and a method to evaluate the Traffic Environment complexity.
Chen Zhiqiang - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM Workshops - Roadside Sensor Based Vehicle Counting Incomplex Traffic Environment
2019 IEEE Globecom Workshops (GC Wkshps), 2019Co-Authors: Chen Zhiqiang, Zhen Liu, Yilong Hui, Tom H. Luan, Guoqiang MaoAbstract:The 5G networks are expected to support autonomous driving to enhance driving experience and travel efficiency. Toward this goal, the valuable data generated by the complex and dynamic transportation system need to be collected. In this paper, we propose a roadside sensor-based vehicle counting scheme for collecting Traffic flow information in complex Traffic Environment. In the scheme, the roadside sensor can sense the magnetic data, where the magnetic flux magnitude will be changed if a vehicle passes though the sense coverage of the sensor. Based on this, we first analyze the change of the magnetic signals in the complex Traffic Environment and process the magnetic signals collected by the roadside sensor. Then, an integrated algorithm is designed to detect and count the Traffic flow by considering the features of the collected signals. After this, we carry out experiments to evaluate the performance of the proposed vehicle counting scheme and analyze the vehicle counting error. According to the features of the error, we further design the error compensation strategy to correct the experiment results. Experimental verification results show that the vehicle counting accuracy before and after the error compensation in the complex Traffic Environment are 97.07% and 98.5%, respectively.
S.f. Lan - One of the best experts on this subject based on the ideXlab platform.
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Responses of the urban roadside trees to Traffic Environment
International Journal of Environmental Technology and Management, 2010Co-Authors: S.f. LanAbstract:Purpose: The study is understand the relation between urban roadside tree growth and the Traffic Environment and an improvement to the Traffic Environment by using urban roadside trees. Design/methodology/approach: Eight common urban roadside trees subjected to many pollutants from automobile emissions were selected for research. The comparatively pollution-free parks far from the Traffic Environment were used as a control. Findings: The nearer the sampling location is to the Traffic Environment, the higher is the absorption of Pb, Cd, and S; the higher, the electric conductivity; and the lower the pH value in the leaves and bark of urban trees.
