Artificial Moral Agents in Big Data-driven Transportation Systems: Autonomous Vehicle Perception Sensors, Virtual Simulation Algorithms, and Geospatial Mapping Tools
Artificial Moral Agents in Big Data-driven Transportation Systems: Autonomous Vehicle Perception Sensors, Virtual Simulation Algorithms, and Geospatial Mapping Tools

Summary/Abstract: Despite the relevance of autonomous vehicle driving algorithms, behavior tracking and virtual data modeling tools, and image recognition and urban sensing technologies, only limited research has been conducted on this topic. In this article, I cumulate previous research findings indicating that simulation and virtualization technologies, real-time image processing tools, and autonomous vehicle perception sensors curtail crash recurrence. I contribute to the literature on deep learning object detection and autonomous driving technologies, Internet of Things connected devices and sensing infrastructures, and visual recognition tools by showing that autonomous driving algorithms harness data-driven planning and deep learning technologies, digital mapping tools, and mobile wireless sensor networks. Throughout May 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “artificial moral agents” + “big data-driven transportation systems” + “autonomous vehicle perception sensors,” “virtual simulation algorithms,” and “geospatial mapping tools.” As I inspected research published in 2022, only 184 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by repli- cation, too imprecise material, or having similar titles, I decided upon 33, 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, Distiller SR, and MMAT.

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