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Digitalization of Traffic Scenes in Support of Intelligent Transportation Applications.

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:08873801
DOI:10.1061/JCCEE5.CPENG-5204