GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network

To provide coordinate information for the use of intelligent transportation systems (ITSs) and autonomous vehicles (AVs), the global positioning system (GPS) is commonly used in vehicle localization as a cheap and easily accessible solution for global positioning. However, several factors contribute...

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Autores principales:	Chia-Hung Lin, Yo-Hui Fang, Hsin-Yuan Chang, Yu-Chien Lin, Wei-Ho Chung, Shih-Chun Lin, Ta-Sung Lee
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Cooperative vehicle localization data fusion deep neural network (DNN) graph convolution network (GCN) long short-term memory (LSTM) vehicle-to-vehicle (V2V) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8de3aca762c8407ba6ee7676c4adb78f
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id	oai:doaj.org-article:8de3aca762c8407ba6ee7676c4adb78f
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spelling	oai:doaj.org-article:8de3aca762c8407ba6ee7676c4adb78f2021-11-24T00:02:16ZGCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network2169-353610.1109/ACCESS.2021.3127914https://doaj.org/article/8de3aca762c8407ba6ee7676c4adb78f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9614197/https://doaj.org/toc/2169-3536To provide coordinate information for the use of intelligent transportation systems (ITSs) and autonomous vehicles (AVs), the global positioning system (GPS) is commonly used in vehicle localization as a cheap and easily accessible solution for global positioning. However, several factors contribute to GPS errors, decreasing the safety and precision of AV and ITS applications, respectively. Extensive research has been conducted to address this problem. More specifically, several optimization-based cooperative vehicle localization algorithms have been developed to improve the localization results by exchanging information with neighboring vehicles to acquire additional information. Nevertheless, existing optimization-based algorithms still suffer from an unacceptable performance and poor scalability. In this study, we investigated the development of deep learning (DL) based cooperative vehicle localization algorithms to provide GPS refinement solutions with low complexity, high performance, and flexibility. Specifically, we propose three DL models to address the problem of interest by emphasizing the temporal and spatial correlations of the extra given information. The simulation results confirm that the developed algorithms outperform existing optimization-based algorithms in terms of refined error statistics. Moreover, a comparison of the three proposed algorithms also demonstrates that the proposed graph convolution network-based cooperative vehicle localization algorithm can effectively utilize temporal and spatial correlations in the extra information, leading to a better performance and lower training overhead.Chia-Hung LinYo-Hui FangHsin-Yuan ChangYu-Chien LinWei-Ho ChungShih-Chun LinTa-Sung LeeIEEEarticleCooperative vehicle localizationdata fusiondeep neural network (DNN)graph convolution network (GCN)long short-term memory (LSTM)vehicle-to-vehicle (V2V)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153429-153441 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cooperative vehicle localization data fusion deep neural network (DNN) graph convolution network (GCN) long short-term memory (LSTM) vehicle-to-vehicle (V2V) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Cooperative vehicle localization data fusion deep neural network (DNN) graph convolution network (GCN) long short-term memory (LSTM) vehicle-to-vehicle (V2V) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Chia-Hung Lin Yo-Hui Fang Hsin-Yuan Chang Yu-Chien Lin Wei-Ho Chung Shih-Chun Lin Ta-Sung Lee GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network
description	To provide coordinate information for the use of intelligent transportation systems (ITSs) and autonomous vehicles (AVs), the global positioning system (GPS) is commonly used in vehicle localization as a cheap and easily accessible solution for global positioning. However, several factors contribute to GPS errors, decreasing the safety and precision of AV and ITS applications, respectively. Extensive research has been conducted to address this problem. More specifically, several optimization-based cooperative vehicle localization algorithms have been developed to improve the localization results by exchanging information with neighboring vehicles to acquire additional information. Nevertheless, existing optimization-based algorithms still suffer from an unacceptable performance and poor scalability. In this study, we investigated the development of deep learning (DL) based cooperative vehicle localization algorithms to provide GPS refinement solutions with low complexity, high performance, and flexibility. Specifically, we propose three DL models to address the problem of interest by emphasizing the temporal and spatial correlations of the extra given information. The simulation results confirm that the developed algorithms outperform existing optimization-based algorithms in terms of refined error statistics. Moreover, a comparison of the three proposed algorithms also demonstrates that the proposed graph convolution network-based cooperative vehicle localization algorithm can effectively utilize temporal and spatial correlations in the extra information, leading to a better performance and lower training overhead.
format	article
author	Chia-Hung Lin Yo-Hui Fang Hsin-Yuan Chang Yu-Chien Lin Wei-Ho Chung Shih-Chun Lin Ta-Sung Lee
author_facet	Chia-Hung Lin Yo-Hui Fang Hsin-Yuan Chang Yu-Chien Lin Wei-Ho Chung Shih-Chun Lin Ta-Sung Lee
author_sort	Chia-Hung Lin
title	GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network
title_short	GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network
title_full	GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network
title_fullStr	GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network
title_full_unstemmed	GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network
title_sort	gcn-cnvps: novel method for cooperative neighboring vehicle positioning system based on graph convolution network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8de3aca762c8407ba6ee7676c4adb78f
work_keys_str_mv	AT chiahunglin gcncnvpsnovelmethodforcooperativeneighboringvehiclepositioningsystembasedongraphconvolutionnetwork AT yohuifang gcncnvpsnovelmethodforcooperativeneighboringvehiclepositioningsystembasedongraphconvolutionnetwork AT hsinyuanchang gcncnvpsnovelmethodforcooperativeneighboringvehiclepositioningsystembasedongraphconvolutionnetwork AT yuchienlin gcncnvpsnovelmethodforcooperativeneighboringvehiclepositioningsystembasedongraphconvolutionnetwork AT weihochung gcncnvpsnovelmethodforcooperativeneighboringvehiclepositioningsystembasedongraphconvolutionnetwork AT shihchunlin gcncnvpsnovelmethodforcooperativeneighboringvehiclepositioningsystembasedongraphconvolutionnetwork AT tasunglee gcncnvpsnovelmethodforcooperativeneighboringvehiclepositioningsystembasedongraphconvolutionnetwork
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GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network

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