ABSTRACT. The purpose of this study was to empirically examine autonomous vehicle interaction control software, big geospatial data analytics, and networked driverless technologies in smart sustainable urban transport systems. Building our argument by drawing on data collected from ANSYS, APA, Atomik Research, AUDI AG, AUVSI, Brookings, Capgemini, CivicScience, Dentons, Ipsos, and Perkins Coie, we performed analyses and made estimates regarding how automated navigational software, sensor-based traffic flow data, edge computing techniques, computer vision operations through collaborative perception, and collision avoidance technologies configure smart transportation mobility across vehicular networks, shaping the acceptance and adoption of self-driving cars. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: autonomous vehicle; urban transport system; big data; geospatial analytics; interaction control software; networked driverless technology

How to cite: Blackburn, E., and Pera, A. (2021). “Autonomous Vehicle Interaction Control Software, Big Geospatial Data Analytics, and Networked Driverless Technologies in Smart Sustainable Urban Transport Systems,” Contemporary Readings in Law and Social Justice 13(2): 121–134. doi: 10.22381/CRLSJ13220219.

Received 26 June 2021 • Received in revised form 4 November 2021
Accepted 10 November 2021 • Available online 15 November 2021

Elizabeth Blackburn
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Center for Networked Driverless Technologies
at ISBDA, Columbus, OH, USA
(corresponding author)
Aurel Pera
This email address is being protected from spambots. You need JavaScript enabled to view it.
University of Craiova, Romania

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