ABSTRACT. The aim of this paper is to synthesize and analyze existing evidence on connected vehicle technologies, autonomous driving perception algorithms, and smart sustainable urban mobility behaviors in networked transport systems. Using and replicating data from AAA, Abraham et al. (2017), Adobe Analytics, ANSYS, Atomik Research, AUDI AG, AUVSI, Capgemini, CarGurus, CBS Interactive, Ipsos, Nvidia, Perkins Coie, Pew Research Center, TechRepublic, and ZDNet, we performed analyses and made estimates regarding how routing and navigating decisions generated by automated collision avoidance systems across urban driving environments and networked digital infrastructures are optimized by connected vehicle technologies, predictive analytics, and big data-enabled visual perception and recognition. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: smart sustainable urban mobility behavior; connected vehicle technology; networked transport system; perception algorithm; autonomous driving

How to cite: Johnson, E., and Nica, E. (2021). “Connected Vehicle Technologies, Autonomous Driving Perception Algorithms, and Smart Sustainable Urban Mobility Behaviors in Networked Transport Systems,” Contemporary Readings in Law and Social Justice 13(2): 37–50. doi: 10.22381/CRLSJ13220213.

Received 11 June 2021 • Received in revised form 6 November 2021
Accepted 9 November 2021 • Available online 15 November 2021

Evelyn Johnson
This email address is being protected from spambots. You need JavaScript enabled to view it.
Autonomous Vehicle Decision-Making Algorithms
Research Unit at ISBDA, Los Angeles, CA, USA
(corresponding author)
Elvira Nica
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Center for Human Resources and Labor Studies
at AAER, New York City, NY, USA;
The Bucharest University of Economic Studies, Romania

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