Deep Learning-based Ethical Judgments in Connected Vehicle Technologies: Route Planning Algorithms, Spatial Data Visualization Tools, and Real-Time Predictive Analytics
Deep Learning-based Ethical Judgments in Connected Vehicle Technologies: Route Planning Algorithms, Spatial Data Visualization Tools, and Real-Time Predictive Analytics

Summary/Abstract: The aim of this systematic review is to synthesize and analyze virtual simulation and geospatial mapping tools, computer vision and path planning algorithms, and autonomous vehicle interaction control software. With increasing evidence of data simulation and optimal trajectory planning tools, predictive control algorithms, and connected car data, there is an essential demand for comprehending whether connected autonomous vehicles require deep convolutional neural networks, big data-driven urban analytics, and remote sensing data fusion techniques. In this research, prior findings were cumulated indicating that cooperative navigation and route planning algorithms, modeling and simulation tools, and data fusion technologies articulate sustainable urban governance networks. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2022, with search terms including “deep learning-based ethical judgments” + “connected vehicle technologies” + “route planning algorithms,” “spatial data visualization tools,” and “real-time predictive analytics.” As I analyzed research published in 2022, only 168 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 34, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Meth- odological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR.

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