ABSTRACT. This paper analyzes the outcomes of an exploratory review of the current research on intelligent vehicular networks, deep learning-based sensing technologies, and big data-driven algorithmic decision-making in smart transportation systems. The data used for this study was obtained and replicated from previous research conducted by AAA, Abraham et al. (2017), Accenture, AUVSI, CarGurus, Deloitte, eMarketer, Kennedys, Morning Consult, Perkins Coie, Pew Research Center, SAE, and Schoettle & Sivak (2014). We performed analyses and made estimates regarding how smart transportation technologies can leverage driving data to improve car safety and mobility in addition to road traffic and infrastructure, thus increasing autonomous vehicle adoption intentions by use of instantaneous motion planning and object detection and tracking algorithms to reduce traffic congestions and collisions. Data collected from 6,800 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: algorithmic decision-making; smart transportation; deep learning; intelligent vehicular network; big data; sensing technologies

How to cite: Aldridge, S., and Stehel, V. (2021). “Intelligent Vehicular Networks, Deep Learning-based Sensing Technologies, and Big Data-driven Algorithmic Decision-Making in Smart Transportation Systems,” Contemporary Readings in Law and Social Justice 13(2): 107–120. doi: 10.22381/CRLSJ13220218.

Received 20 June 2021 • Received in revised form 8 November 2021
Accepted 12 November 2021 • Available online 15 November 2021

Susan Aldridge
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Sustainable and Smart Urban Transport
Systems Laboratory at CLI, Portsmouth, England
(corresponding author)
Vojtech Stehel
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The School of Expertness and Valuation,
The Institute of Technology and Business
in Ceske Budejovice, Czech Republic

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