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Autonomous Vehicle Driving Algorithms, Deep Learning-based Sensing Technologies, and Big Geospatial Data Analytics in Smart Sustainable Intelligent Transportation Systems

Autonomous Vehicle Driving Algorithms, Deep Learning-based Sensing Technologies, and Big... We draw on a substantial body of theoretical and empirical research on autonomous vehicle driving algorithms, deep learning-based sensing technologies, and big geospatial data analytics in smart sustainable intelligent transportation systems, and to explore this, we inspected, used, and replicated survey data from AUVSI, BikePGH, Capgemini, CarGurus, CivicScience, GenPop, Ipsos, KPMG, Management Events, McKinsey, Perkins Coie, Pew Research Center, and Statista, performing analyses and making estimates regarding how motion control and object recognition improve road traffic safety and reduce fatalities by use of sensing and navigation systems and mobile data traffic. Computer vision, sensor data processing, and adaptive and dynamic planning optimize road user safety and mobility across autonomous vehicle control systems through real-time object detection and recognition. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. Keywords: autonomous vehicle; deep learning; driving algorithm; intelligent transportation system; big data; geospatial analytics http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Readings in Law and Social Justice Addleton Academic Publishers

Autonomous Vehicle Driving Algorithms, Deep Learning-based Sensing Technologies, and Big Geospatial Data Analytics in Smart Sustainable Intelligent Transportation Systems

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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 autonomous vehicle driving algorithms, deep learning-based sensing technologies, and big geospatial data analytics in smart sustainable intelligent transportation systems, and to explore this, we inspected, used, and replicated survey data from AUVSI, BikePGH, Capgemini, CarGurus, CivicScience, GenPop, Ipsos, KPMG, Management Events, McKinsey, Perkins Coie, Pew Research Center, and Statista, performing analyses and making estimates regarding how motion control and object recognition improve road traffic safety and reduce fatalities by use of sensing and navigation systems and mobile data traffic. Computer vision, sensor data processing, and adaptive and dynamic planning optimize road user safety and mobility across autonomous vehicle control systems through real-time object detection and recognition. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. Keywords: autonomous vehicle; deep learning; driving algorithm; intelligent transportation system; big data; geospatial analytics

Journal

Contemporary Readings in Law and Social Justice – Addleton Academic Publishers

Published: Jan 1, 2021

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