ABSTRACT. We develop a conceptual framework based on a systematic and comprehensive literature review on autonomous vehicle decision-making algorithms, interconnected sensor networks, and big geospatial data analytics in smart urban mobility systems. Building our argument by drawing on data collected from AAA, AHAS, AUVSI, BCG, Brookings, Capgemini, Gallup, GHSA, Kennedys, ORC, Perkins Coie, SAE, Statista, and World Economic Forum, we performed analyses and made estimates regarding how computer vision, path and motion planning algorithms, and machine learning-based predictors are pivotal in lane detection and congestion monitoring, reducing traffic collisions and related fatalities, and optimizing obstacle avoidance, navigation flow prediction, and trajectory planning for connected and autonomous vehicles across smart and sustainable driverless urban mobility. The data for this research were gathered via an online survey questionnaire. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: autonomous vehicle; smart urban mobility system; geospatial analytics; big data; interconnected sensor network; decision-making algorithm

How to cite: Goodman, C., and Frajtova Michalikova, K. (2021). “Autonomous Vehicle Decision-Making Algorithms, Interconnected Sensor Networks, and Big Geospatial Data Analytics in Smart Urban Mobility Systems,” Contemporary Readings in Law and Social Justice 13(2): 93–106. doi: 10.22381/CRLSJ13220217.

Received 25 June 2021 • Received in revised form 10 November 2021
Accepted 13 November 2021 • Available online 15 November 2021

Charles Goodman
This email address is being protected from spambots. You need JavaScript enabled to view it.
Transportation Analytics Research Unit
at AAER, Milwaukee, WI, USA
(corresponding author)
Katarina Frajtova Michalikova
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic

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