The Social Justice of Intelligent Transportation Systems: Deep Learning-based Autonomous Driving Technologies, Cooperative Navigation Algorithms, and Vehicle and Pedestrian Detection Tools
The Social Justice of Intelligent Transportation Systems: Deep Learning-based Autonomous Driving Technologies, Cooperative Navigation Algorithms, and Vehicle and Pedestrian Detection Tools

Summary/Abstract: Based on an in-depth survey of the literature, the purpose of the paper is to explore Internet of Things-sensing tools, data processing and smart mobility technologies, and self-driving car control algorithms. In this research, previous findings were cumulated showing that self-driving cars develop on route detection and environment mapping algorithms, deep neural network technology, and image processing tools, and I contribute to the literature by indicating that connected car data, algorithm-driven sensing devices, and spatial data visualization tools reduce preventable road injuries. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “social justice” + “intelligent transportation systems” + “deep learning-based autonomous driving technologies,” “cooperative navigation algorithms,” and “vehicle and pedestrian detection tools.” As research published in 2022 was inspected, only 187 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, I selected 34 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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