Remote Sensing Data Fusion Techniques, Autonomous Vehicle Driving Perception Algorithms, and Mobility Simulation Tools in Smart Transportation Systems
Remote Sensing Data Fusion Techniques, Autonomous Vehicle Driving Perception Algorithms, and Mobility Simulation Tools in Smart Transportation Systems

Summary/Abstract: The objective of this paper is to systematically review remote sensing data fusion techniques, autonomous vehicle driving perception algorithms, and mobility simulation tools in smart transportation systems. The findings and analyses highlight that visual perception algorithms, sensing and computing technologies, and route planning and control tools configure networked digital infrastructures. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “smart transportation systems” + “remote sensing data fusion techniques,” “autonomous vehicle driving perception algorithms,” and “mobility simulation tools.” As research published between 2019 and 2022 was inspected, only 92 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 15 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

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