Research on vehicle intelligent wireless location algorithm based on convolutional neural network

Vehicle positioning and vehicle identification of natural scene images are an important part of intelligent transportation systems and unmanned driving research. In current situation, there are still some problems in vehicle intelligent wireless positioning. In order to improve the intelligent wirel...

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Vydáno v:	Neural computing & applications Ročník 33; číslo 14; s. 8131 - 8141
Hlavní autoři:	Wang, Yazi, Feng, Yuehong, Sun, Huaibo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.07.2021 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Artificial neural networks Autonomous cars Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Intelligent transportation systems Machine learning Neural networks Probability and Statistics in Computer Science S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications Transportation networks Vehicle identification Vehicle positioning Intelligence Convolutional neural network Algorithm improvement Wireless positioning
ISSN:	0941-0643, 1433-3058
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Abstract	Vehicle positioning and vehicle identification of natural scene images are an important part of intelligent transportation systems and unmanned driving research. In current situation, there are still some problems in vehicle intelligent wireless positioning. In order to improve the intelligent wireless positioning efficiency of vehicles, based on the convolutional neural network, this research combines the concept of deep learning to carry out algorithm innovation in the research. Moreover, this paper combines the actual vehicle positioning problem points to collect data, simulates the vehicle positioning situation in a variety of complex situations, and designs a controlled test to verify. The results show that the algorithm of this study has certain effects, which can provide reference for subsequent related research and has certain practical significance.
AbstractList	Vehicle positioning and vehicle identification of natural scene images are an important part of intelligent transportation systems and unmanned driving research. In current situation, there are still some problems in vehicle intelligent wireless positioning. In order to improve the intelligent wireless positioning efficiency of vehicles, based on the convolutional neural network, this research combines the concept of deep learning to carry out algorithm innovation in the research. Moreover, this paper combines the actual vehicle positioning problem points to collect data, simulates the vehicle positioning situation in a variety of complex situations, and designs a controlled test to verify. The results show that the algorithm of this study has certain effects, which can provide reference for subsequent related research and has certain practical significance.
Author	Feng, Yuehong Wang, Yazi Sun, Huaibo
Author_xml	– sequence: 1 givenname: Yazi surname: Wang fullname: Wang, Yazi organization: School of Mathematics and Statistics, ZhouKou Normal University – sequence: 2 givenname: Yuehong surname: Feng fullname: Feng, Yuehong organization: College of Applied Sciences, Beijing University of Technology – sequence: 3 givenname: Huaibo surname: Sun fullname: Sun, Huaibo email: 2004112@muc.edu.cn organization: School of Mathematics and Statistics, Fuyang Normal University
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Cites_doi	10.1109/LSP.2016.2569666 10.3390/s18010169 10.1016/j.procir.2017.02.031 10.3390/s17040848 10.15302/J-FEM-2017008 10.1002/dac.2352 10.3390/s18051442 10.1109/TVT.2018.2843459 10.1002/wcm.2678 10.3390/s18030711 10.1109/I4CT.2014.6914143 10.1109/VPPC.2015.7352903 10.1109/ITSC.2016.7795533
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SubjectTerms	Algorithms Artificial Intelligence Artificial neural networks Autonomous cars Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Intelligent transportation systems Machine learning Neural networks Probability and Statistics in Computer Science S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications Transportation networks Vehicle identification
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