Digital Twin-Based Cloud-Native Vehicular Networks Architecture for Intelligent Driving

As a key technology in intelligent driving, Cooperative Vehicle Infrastructure System is an advanced communication framework that enables cooperative and information exchange between vehicles and infrastructure. However, this system encounters challenges in meeting the low latency, ultra reliability...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE network 2024-01, Vol.38 (1), p.69-76
Hauptverfasser: Tan, Xiaobin, Meng, Qiushi, Wang, Mingyang, Zheng, Quan, Wu, Jun, Yang, Jian
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 76
container_issue 1
container_start_page 69
container_title IEEE network
container_volume 38
creator Tan, Xiaobin
Meng, Qiushi
Wang, Mingyang
Zheng, Quan
Wu, Jun
Yang, Jian
description As a key technology in intelligent driving, Cooperative Vehicle Infrastructure System is an advanced communication framework that enables cooperative and information exchange between vehicles and infrastructure. However, this system encounters challenges in meeting the low latency, ultra reliability, and high efficiency requirements of task execution of intelligent driving applications. Meanwhile, the deployment and combination of some key technologies supporting this system are not well-established. To address these issues, this article proposes a Digital Twin-based Cloud-native Vehicular Networks (DT-CVN) architecture to enhance the efficiency of virtual-reality integration in real-world vehicle traffic scenarios. In DT-CVN, the digital twins, which can bridge the physical space and cyberspace gaps in real-time, are implemented and deployed in a distributed manner by leveraging the distributed features of microservices based on cloud-native technology. DT-CVN employs the cybertwin as a smart communication agent in cloud-native vehicular networks, enabling efficient communication between cyberspace and physical spaces. Moreover, a case study is presented to demonstrate the effectiveness of DT-CVN. Simulation result shows the potential to address the challenges of integrating resources in vehicular networks with our proposed DT-CVN.
doi_str_mv 10.1109/MNET.2023.3337271
format Article
fullrecord <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_MNET_2023_3337271</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10329347</ieee_id><sourcerecordid>3041499638</sourcerecordid><originalsourceid>FETCH-LOGICAL-c246t-38284b311ef2178e46b6dd79564949a8439a68988ccb94af2929f31462a1450d3</originalsourceid><addsrcrecordid>eNpNkEtPAjEUhRujiYj-ABMXTVwP9jWddomISoK4wceuKTN3oDjOYNuB-O8dAgtXZ_Ode3M-hK4pGVBK9N3LbDwfMML4gHOesYyeoB5NU5XQVH6eoh5RmiSKCHGOLkJYE0JFylkPfTy4pYu2wvOdq5N7G6DAo6ppi2Rmo9sCfoeVy9vKejyDuGv8V8BDn69chDy2HnDZeDypI1SVW0Id8YN3W1cvL9FZaasAV8fso7fH8Xz0nExfnyaj4TTJmZAx4YopseCUQslopkDIhSyKTKdSaKGtElxbqbRSeb7QwpZMM11yKiSz3QBS8D66Pdzd-OanhRDNuml93b00nAgqtJZcdRQ9ULlvQvBQmo1339b_GkrM3p_Z-zN7f-bor-vcHDoOAP7xnGkuMv4HatNrMA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3041499638</pqid></control><display><type>article</type><title>Digital Twin-Based Cloud-Native Vehicular Networks Architecture for Intelligent Driving</title><source>IEEE Electronic Library (IEL)</source><creator>Tan, Xiaobin ; Meng, Qiushi ; Wang, Mingyang ; Zheng, Quan ; Wu, Jun ; Yang, Jian</creator><creatorcontrib>Tan, Xiaobin ; Meng, Qiushi ; Wang, Mingyang ; Zheng, Quan ; Wu, Jun ; Yang, Jian</creatorcontrib><description>As a key technology in intelligent driving, Cooperative Vehicle Infrastructure System is an advanced communication framework that enables cooperative and information exchange between vehicles and infrastructure. However, this system encounters challenges in meeting the low latency, ultra reliability, and high efficiency requirements of task execution of intelligent driving applications. Meanwhile, the deployment and combination of some key technologies supporting this system are not well-established. To address these issues, this article proposes a Digital Twin-based Cloud-native Vehicular Networks (DT-CVN) architecture to enhance the efficiency of virtual-reality integration in real-world vehicle traffic scenarios. In DT-CVN, the digital twins, which can bridge the physical space and cyberspace gaps in real-time, are implemented and deployed in a distributed manner by leveraging the distributed features of microservices based on cloud-native technology. DT-CVN employs the cybertwin as a smart communication agent in cloud-native vehicular networks, enabling efficient communication between cyberspace and physical spaces. Moreover, a case study is presented to demonstrate the effectiveness of DT-CVN. Simulation result shows the potential to address the challenges of integrating resources in vehicular networks with our proposed DT-CVN.</description><identifier>ISSN: 0890-8044</identifier><identifier>EISSN: 1558-156X</identifier><identifier>DOI: 10.1109/MNET.2023.3337271</identifier><identifier>CODEN: IENEET</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Advanced driver assistance systems ; Cloud computing ; Communication ; Cyberspace ; Digital twins ; Driving ; Infrastructure ; Network latency ; Real-time systems ; Servers ; Task analysis ; Vehicle dynamics ; Vehicles ; Vehicular ad hoc networks ; Virtual reality</subject><ispartof>IEEE network, 2024-01, Vol.38 (1), p.69-76</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c246t-38284b311ef2178e46b6dd79564949a8439a68988ccb94af2929f31462a1450d3</cites><orcidid>0000-0001-7489-2839 ; 0009-0003-3846-4919 ; 0000-0001-7090-8653 ; 0009-0002-4710-6569 ; 0000-0002-8736-1161 ; 0000-0002-7329-4738</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10329347$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27928,27929,54762</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10329347$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tan, Xiaobin</creatorcontrib><creatorcontrib>Meng, Qiushi</creatorcontrib><creatorcontrib>Wang, Mingyang</creatorcontrib><creatorcontrib>Zheng, Quan</creatorcontrib><creatorcontrib>Wu, Jun</creatorcontrib><creatorcontrib>Yang, Jian</creatorcontrib><title>Digital Twin-Based Cloud-Native Vehicular Networks Architecture for Intelligent Driving</title><title>IEEE network</title><addtitle>NET-M</addtitle><description>As a key technology in intelligent driving, Cooperative Vehicle Infrastructure System is an advanced communication framework that enables cooperative and information exchange between vehicles and infrastructure. However, this system encounters challenges in meeting the low latency, ultra reliability, and high efficiency requirements of task execution of intelligent driving applications. Meanwhile, the deployment and combination of some key technologies supporting this system are not well-established. To address these issues, this article proposes a Digital Twin-based Cloud-native Vehicular Networks (DT-CVN) architecture to enhance the efficiency of virtual-reality integration in real-world vehicle traffic scenarios. In DT-CVN, the digital twins, which can bridge the physical space and cyberspace gaps in real-time, are implemented and deployed in a distributed manner by leveraging the distributed features of microservices based on cloud-native technology. DT-CVN employs the cybertwin as a smart communication agent in cloud-native vehicular networks, enabling efficient communication between cyberspace and physical spaces. Moreover, a case study is presented to demonstrate the effectiveness of DT-CVN. Simulation result shows the potential to address the challenges of integrating resources in vehicular networks with our proposed DT-CVN.</description><subject>Advanced driver assistance systems</subject><subject>Cloud computing</subject><subject>Communication</subject><subject>Cyberspace</subject><subject>Digital twins</subject><subject>Driving</subject><subject>Infrastructure</subject><subject>Network latency</subject><subject>Real-time systems</subject><subject>Servers</subject><subject>Task analysis</subject><subject>Vehicle dynamics</subject><subject>Vehicles</subject><subject>Vehicular ad hoc networks</subject><subject>Virtual reality</subject><issn>0890-8044</issn><issn>1558-156X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkEtPAjEUhRujiYj-ABMXTVwP9jWddomISoK4wceuKTN3oDjOYNuB-O8dAgtXZ_Ode3M-hK4pGVBK9N3LbDwfMML4gHOesYyeoB5NU5XQVH6eoh5RmiSKCHGOLkJYE0JFylkPfTy4pYu2wvOdq5N7G6DAo6ppi2Rmo9sCfoeVy9vKejyDuGv8V8BDn69chDy2HnDZeDypI1SVW0Id8YN3W1cvL9FZaasAV8fso7fH8Xz0nExfnyaj4TTJmZAx4YopseCUQslopkDIhSyKTKdSaKGtElxbqbRSeb7QwpZMM11yKiSz3QBS8D66Pdzd-OanhRDNuml93b00nAgqtJZcdRQ9ULlvQvBQmo1339b_GkrM3p_Z-zN7f-bor-vcHDoOAP7xnGkuMv4HatNrMA</recordid><startdate>202401</startdate><enddate>202401</enddate><creator>Tan, Xiaobin</creator><creator>Meng, Qiushi</creator><creator>Wang, Mingyang</creator><creator>Zheng, Quan</creator><creator>Wu, Jun</creator><creator>Yang, Jian</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-7489-2839</orcidid><orcidid>https://orcid.org/0009-0003-3846-4919</orcidid><orcidid>https://orcid.org/0000-0001-7090-8653</orcidid><orcidid>https://orcid.org/0009-0002-4710-6569</orcidid><orcidid>https://orcid.org/0000-0002-8736-1161</orcidid><orcidid>https://orcid.org/0000-0002-7329-4738</orcidid></search><sort><creationdate>202401</creationdate><title>Digital Twin-Based Cloud-Native Vehicular Networks Architecture for Intelligent Driving</title><author>Tan, Xiaobin ; Meng, Qiushi ; Wang, Mingyang ; Zheng, Quan ; Wu, Jun ; Yang, Jian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c246t-38284b311ef2178e46b6dd79564949a8439a68988ccb94af2929f31462a1450d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Advanced driver assistance systems</topic><topic>Cloud computing</topic><topic>Communication</topic><topic>Cyberspace</topic><topic>Digital twins</topic><topic>Driving</topic><topic>Infrastructure</topic><topic>Network latency</topic><topic>Real-time systems</topic><topic>Servers</topic><topic>Task analysis</topic><topic>Vehicle dynamics</topic><topic>Vehicles</topic><topic>Vehicular ad hoc networks</topic><topic>Virtual reality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tan, Xiaobin</creatorcontrib><creatorcontrib>Meng, Qiushi</creatorcontrib><creatorcontrib>Wang, Mingyang</creatorcontrib><creatorcontrib>Zheng, Quan</creatorcontrib><creatorcontrib>Wu, Jun</creatorcontrib><creatorcontrib>Yang, Jian</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE network</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tan, Xiaobin</au><au>Meng, Qiushi</au><au>Wang, Mingyang</au><au>Zheng, Quan</au><au>Wu, Jun</au><au>Yang, Jian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Digital Twin-Based Cloud-Native Vehicular Networks Architecture for Intelligent Driving</atitle><jtitle>IEEE network</jtitle><stitle>NET-M</stitle><date>2024-01</date><risdate>2024</risdate><volume>38</volume><issue>1</issue><spage>69</spage><epage>76</epage><pages>69-76</pages><issn>0890-8044</issn><eissn>1558-156X</eissn><coden>IENEET</coden><abstract>As a key technology in intelligent driving, Cooperative Vehicle Infrastructure System is an advanced communication framework that enables cooperative and information exchange between vehicles and infrastructure. However, this system encounters challenges in meeting the low latency, ultra reliability, and high efficiency requirements of task execution of intelligent driving applications. Meanwhile, the deployment and combination of some key technologies supporting this system are not well-established. To address these issues, this article proposes a Digital Twin-based Cloud-native Vehicular Networks (DT-CVN) architecture to enhance the efficiency of virtual-reality integration in real-world vehicle traffic scenarios. In DT-CVN, the digital twins, which can bridge the physical space and cyberspace gaps in real-time, are implemented and deployed in a distributed manner by leveraging the distributed features of microservices based on cloud-native technology. DT-CVN employs the cybertwin as a smart communication agent in cloud-native vehicular networks, enabling efficient communication between cyberspace and physical spaces. Moreover, a case study is presented to demonstrate the effectiveness of DT-CVN. Simulation result shows the potential to address the challenges of integrating resources in vehicular networks with our proposed DT-CVN.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/MNET.2023.3337271</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-7489-2839</orcidid><orcidid>https://orcid.org/0009-0003-3846-4919</orcidid><orcidid>https://orcid.org/0000-0001-7090-8653</orcidid><orcidid>https://orcid.org/0009-0002-4710-6569</orcidid><orcidid>https://orcid.org/0000-0002-8736-1161</orcidid><orcidid>https://orcid.org/0000-0002-7329-4738</orcidid></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 0890-8044
ispartof IEEE network, 2024-01, Vol.38 (1), p.69-76
issn 0890-8044
1558-156X
language eng
recordid cdi_crossref_primary_10_1109_MNET_2023_3337271
source IEEE Electronic Library (IEL)
subjects Advanced driver assistance systems
Cloud computing
Communication
Cyberspace
Digital twins
Driving
Infrastructure
Network latency
Real-time systems
Servers
Task analysis
Vehicle dynamics
Vehicles
Vehicular ad hoc networks
Virtual reality
title Digital Twin-Based Cloud-Native Vehicular Networks Architecture for Intelligent Driving
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-17T12%3A38%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Digital%20Twin-Based%20Cloud-Native%20Vehicular%20Networks%20Architecture%20for%20Intelligent%20Driving&rft.jtitle=IEEE%20network&rft.au=Tan,%20Xiaobin&rft.date=2024-01&rft.volume=38&rft.issue=1&rft.spage=69&rft.epage=76&rft.pages=69-76&rft.issn=0890-8044&rft.eissn=1558-156X&rft.coden=IENEET&rft_id=info:doi/10.1109/MNET.2023.3337271&rft_dat=%3Cproquest_RIE%3E3041499638%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3041499638&rft_id=info:pmid/&rft_ieee_id=10329347&rfr_iscdi=true