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Intelligent Vehicular Networks, Deep Learning-based Sensing Technologies, and Big Data-driven Algorithmic Decision-Making in Smart Transportation Systems

Intelligent Vehicular Networks, Deep Learning-based Sensing Technologies, and Big Data-driven... 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 and 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Readings in Law and Social Justice Addleton Academic Publishers

Intelligent Vehicular Networks, Deep Learning-based Sensing Technologies, and Big Data-driven Algorithmic Decision-Making in Smart 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

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 and 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

Journal

Contemporary Readings in Law and Social Justice – Addleton Academic Publishers

Published: Jan 1, 2021

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