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Socially Responsible Technologies in Autonomous Mobility Systems: Self-Driving Car Control Algorithms, Virtual Data Modeling Tools, and Cognitive Wireless Sensor Networks

Socially Responsible Technologies in Autonomous Mobility Systems: Self-Driving Car Control... We draw on a substantial body of theoretical and empirical research on smart infrastructure sensors, deep learning-based autonomous driving and data processing technologies, and spatio-temporal fusion algorithms. In this research, prior findings were cumulated indicating that monitoring and sensing technologies, predictive maintenance and data mining tools, and computer vision and object detection algorithms optimize vehicular traffic flows and road safety. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout June 2022, with search terms including “socially responsible technologies” + “autonomous mobility systems” + “self-driving car control algorithms,” “virtual data modeling tools,” and “cognitive wireless sensor networks.” As we analyzed research in 2022, only 181 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, we decided on 27, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. Keywords: autonomous mobility system; self-driving car control algorithm; virtual data modeling tool; cognitive wireless sensor network http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Readings in Law and Social Justice Addleton Academic Publishers

Socially Responsible Technologies in Autonomous Mobility Systems: Self-Driving Car Control Algorithms, Virtual Data Modeling Tools, and Cognitive Wireless Sensor Networks

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1948-9137
eISSN
2162-2752
Publisher site
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Abstract

We draw on a substantial body of theoretical and empirical research on smart infrastructure sensors, deep learning-based autonomous driving and data processing technologies, and spatio-temporal fusion algorithms. In this research, prior findings were cumulated indicating that monitoring and sensing technologies, predictive maintenance and data mining tools, and computer vision and object detection algorithms optimize vehicular traffic flows and road safety. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout June 2022, with search terms including “socially responsible technologies” + “autonomous mobility systems” + “self-driving car control algorithms,” “virtual data modeling tools,” and “cognitive wireless sensor networks.” As we analyzed research in 2022, only 181 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, we decided on 27, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. Keywords: autonomous mobility system; self-driving car control algorithm; virtual data modeling tool; cognitive wireless sensor network

Journal

Contemporary Readings in Law and Social Justice – Addleton Academic Publishers

Published: Jan 1, 2022

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