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Digitalization of Traffic Scenes in Support of Intelligent Transportation Applications.

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Title: Digitalization of Traffic Scenes in Support of Intelligent Transportation Applications.
Authors: Lu, Linjun1 (AUTHOR) ll0074@mix.wvu.edu, Dai, Fei2 (AUTHOR) fei.dai@mail.wvu.edu
Source: Journal of Computing in Civil Engineering. Sep2023, Vol. 37 Issue 5, p1-14. 14p.
Subjects: Digital twin, Digital technology, Road users, Video surveillance, Automotive transportation
Abstract: Digitalization of real-world traffic scenes is a fundamental task in development of digital twins of road transportation. However, the existing digitalization approaches are either expensive in equipment costs or inapplicable to collect granular level data of traffic scenes. This study proposed a vision-based method for real-time digitalization of traffic scenes through modeling and merging the road infrastructure (static components) and road users (dynamic components) progressively. Specifically, the former is reconstructed by leveraging unmanned aerial vehicles (UAVs) and structure from motion; and the latter is digitized via using roadside surveillance videos and a new reconstruction process through applying deep learning and view geometry. Last, the digital model of the traffic scene is built by merging the digital models of static and dynamic components. A field experiment was performed to evaluate the performance of the proposed method. The results showed that the traffic scene can be successfully digitalized by the proposed method with promising accuracy, thus signifying the method's potential for the development of the digital twins of road transportation in support of intelligent transportation applications. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computing in Civil Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Digitalization of Traffic Scenes in Support of Intelligent Transportation Applications.
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  Data: <searchLink fieldCode="AR" term="%22Lu%2C+Linjun%22">Lu, Linjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ll0074@mix.wvu.edu</i><br /><searchLink fieldCode="AR" term="%22Dai%2C+Fei%22">Dai, Fei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> fei.dai@mail.wvu.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computing+in+Civil+Engineering%22">Journal of Computing in Civil Engineering</searchLink>. Sep2023, Vol. 37 Issue 5, p1-14. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22Road+users%22">Road users</searchLink><br /><searchLink fieldCode="DE" term="%22Video+surveillance%22">Video surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Automotive+transportation%22">Automotive transportation</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Digitalization of real-world traffic scenes is a fundamental task in development of digital twins of road transportation. However, the existing digitalization approaches are either expensive in equipment costs or inapplicable to collect granular level data of traffic scenes. This study proposed a vision-based method for real-time digitalization of traffic scenes through modeling and merging the road infrastructure (static components) and road users (dynamic components) progressively. Specifically, the former is reconstructed by leveraging unmanned aerial vehicles (UAVs) and structure from motion; and the latter is digitized via using roadside surveillance videos and a new reconstruction process through applying deep learning and view geometry. Last, the digital model of the traffic scene is built by merging the digital models of static and dynamic components. A field experiment was performed to evaluate the performance of the proposed method. The results showed that the traffic scene can be successfully digitalized by the proposed method with promising accuracy, thus signifying the method's potential for the development of the digital twins of road transportation in support of intelligent transportation applications. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computing in Civil Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1061/JCCEE5.CPENG-5204
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 1
    Subjects:
      – SubjectFull: Digital twin
        Type: general
      – SubjectFull: Digital technology
        Type: general
      – SubjectFull: Road users
        Type: general
      – SubjectFull: Video surveillance
        Type: general
      – SubjectFull: Automotive transportation
        Type: general
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      – TitleFull: Digitalization of Traffic Scenes in Support of Intelligent Transportation Applications.
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            NameFull: Lu, Linjun
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            NameFull: Dai, Fei
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            – D: 01
              M: 09
              Text: Sep2023
              Type: published
              Y: 2023
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              Value: 37
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