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

How to cite: Rowland, Z., and Porter, K. (2021). “Autonomous Vehicle Driving Algorithms, Deep Learning-based Sensing Technologies, and Big Geospatial Data Analytics in Smart Sustainable Intelligent Transportation Systems,” Contemporary Readings in Law and Social Justice 13(2): 23–36. doi: 10.22381/CRLSJ13220212.

Received 22 June 2021 • Received in revised form 9 November 2021
Accepted 11 November 2021 • Available online 15 November 2021

Zuzana Rowland
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The Institute of Technology and Business in Ceske Budejovice,
The School of Expertness and Valuation, Czech Republic
Kathleen Porter
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Big Data-driven Transportation Planning and
Engineering Laboratory at CLI, Winnipeg, Canada
(corresponding author)

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