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Autonomous Vehicle Ethics in Networked Transport Systems: Spatial Cognition Algorithms, Mobility Data Processing Tools, and Deep Learning-based Sensing Technologies

Autonomous Vehicle Ethics in Networked Transport Systems: Spatial Cognition Algorithms, Mobility... The present study systematically reviews the existing research on geospatial mapping and simulation modeling tools, environment and vehicle sensors, and crash avoidance technologies. My findings clarify that interconnected sensor networks integrate visual recognition and image processing tools, smart mobility technologies, and trajectory tracking control algorithms, and I contribute to the literature by indicating that connected autonomous vehicles necessitate urban traffic modeling and data visualization tools, deep neural network technology, and predictive control algorithms. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “autonomous vehicle ethics” + “networked transport systems” + “spatial cognition algorithms,” “mobility data processing tools,” and “deep learningbased sensing technologies.” As research published in 2022 was inspected, only 185 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 36 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. Keywords: networked transport system; spatial cognition algorithm; mobility data processing tool; sensing technology http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Readings in Law and Social Justice Addleton Academic Publishers

Autonomous Vehicle Ethics in Networked Transport Systems: Spatial Cognition Algorithms, Mobility Data Processing Tools, and Deep Learning-based Sensing Technologies

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1948-9137
eISSN
2162-2752
Publisher site
See Article on Publisher Site

Abstract

The present study systematically reviews the existing research on geospatial mapping and simulation modeling tools, environment and vehicle sensors, and crash avoidance technologies. My findings clarify that interconnected sensor networks integrate visual recognition and image processing tools, smart mobility technologies, and trajectory tracking control algorithms, and I contribute to the literature by indicating that connected autonomous vehicles necessitate urban traffic modeling and data visualization tools, deep neural network technology, and predictive control algorithms. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “autonomous vehicle ethics” + “networked transport systems” + “spatial cognition algorithms,” “mobility data processing tools,” and “deep learningbased sensing technologies.” As research published in 2022 was inspected, only 185 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 36 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. Keywords: networked transport system; spatial cognition algorithm; mobility data processing tool; sensing technology

Journal

Contemporary Readings in Law and Social JusticeAddleton Academic Publishers

Published: Jan 1, 2022

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