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

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...

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Bibliographic Details
Published in:Contemporary readings in law and social justice 2022, Vol.14 (2), p.172-188
Main Authors: Valaskova, Katarina, Horak, Jakub, Lazaroiu, George
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Automation
Autonomous vehicles
Bibliometrics
Big Data
Control algorithms
Data mining
Data processing
Decision making
Deep learning
Driving
Electronic data processing
Ethics / Practical Philosophy
Green technology
Information technology
Infrastructure
Internet of Things
Layout
Literature reviews
Mapping
Mobility
Neural networks
Quality assessment
Remote sensing systems
Road safety
Roads & highways
Sensors
Signal processing
Simulation
Smart structures
Social science research
Spatial data
Systematic review
Traffic
Transport / Logistics
Transportation planning
Urbanism
Virtual computer systems
Visualization
Wireless sensor networks
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Description
Summary: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 through- out 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.
ISSN:1948-9137
2162-2752
DOI:10.22381/CRLSJ142202210