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Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems

Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of we...

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Detalles Bibliográficos
Autores principales: Jia, Zhixin, Fu, Kaiya, Lin, Mengxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540145/
https://www.ncbi.nlm.nih.gov/pubmed/31052209
http://dx.doi.org/10.3390/s19092027
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author Jia, Zhixin
Fu, Kaiya
Lin, Mengxiang
author_facet Jia, Zhixin
Fu, Kaiya
Lin, Mengxiang
author_sort Jia, Zhixin
collection PubMed
description Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of weigh-in-motion (WIM) systems, this paper proposes a neural network-based method integrating identification and predication. A backpropagation neural network for signal classification (BPNN-i) was designed to identify ideal samples acquired by load sensors closest to the tire-pavement contact area. After that, ideal samples were used to predict the gross vehicle weight by using another backpropagation neural network (BPNN-e). The dataset for training and evaluation was collected from a multiple-sensor WIM (MS-WIM) system deployed in a public road. In our experiments, 96.89% of samples in the test set had an estimation error of less than 5%.
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spelling pubmed-65401452019-06-04 Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems Jia, Zhixin Fu, Kaiya Lin, Mengxiang Sensors (Basel) Article Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of weigh-in-motion (WIM) systems, this paper proposes a neural network-based method integrating identification and predication. A backpropagation neural network for signal classification (BPNN-i) was designed to identify ideal samples acquired by load sensors closest to the tire-pavement contact area. After that, ideal samples were used to predict the gross vehicle weight by using another backpropagation neural network (BPNN-e). The dataset for training and evaluation was collected from a multiple-sensor WIM (MS-WIM) system deployed in a public road. In our experiments, 96.89% of samples in the test set had an estimation error of less than 5%. MDPI 2019-04-30 /pmc/articles/PMC6540145/ /pubmed/31052209 http://dx.doi.org/10.3390/s19092027 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jia, Zhixin
Fu, Kaiya
Lin, Mengxiang
Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems
title Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems
title_full Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems
title_fullStr Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems
title_full_unstemmed Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems
title_short Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems
title_sort tire–pavement contact-aware weight estimation for multi-sensor wim systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540145/
https://www.ncbi.nlm.nih.gov/pubmed/31052209
http://dx.doi.org/10.3390/s19092027
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