Autonomous Vehicle Perception Sensor Data, Motion Planning and Object Recognition Algorithms, and Virtual Simulation Modeling Tools in Smart Sustainable Intelligent Transportation Systems
Autonomous Vehicle Perception Sensor Data, Motion Planning and Object Recognition Algorithms, and Virtual Simulation Modeling Tools in Smart Sustainable Intelligent Transportation Systems

Summary/Abstract: The purpose of this study is to examine autonomous vehicle perception sensor data, motion planning and object recognition algorithms, and virtual simulation modeling tools in smart sustainable intelligent transportation systems. In this article, I cumulate previous research findings indicating that object localization algorithms, edge computing techniques, and autonomous vehicle perception sensors improve navigation accuracy. I contribute to the literature on autonomous driving perception algorithms, spatial recognition technologies, and virtual reality modeling tools by showing that urban sensing technologies develop on machine learning algorithms, predictive modeling techniques, and movement and behavior tracking tools. Throughout April 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “smart sustainable intelligent transportation systems” + “autonomous vehicle perception sensor data,” “motion planning and object recognition algorithms,” and “virtual simulation modeling tools.” As I inspected research published between 2019 and 2022, only 91 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 16, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR.

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