ABSTRACT. Empirical evidence on autonomous vehicle routing and navigation, computer vision algorithms, and transportation analytics in network connectivity systems has been scarcely documented in the literature. Using and replicating data from ANSYS, Atomik Research, APA, AUDI AG, BCG, Capgemini, EY, Ipsos, and Kennedys, we performed analyses and made estimates regarding how self-driving cars can considerably decrease highway congestion and motor vehicle collision frequency and severity by identifying the road users and surrounding infrastructure promptly and precisely. Object detection is essential in the autonomous vehicle perception system, resulting in reduced traffic collisions and fatalities. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: autonomous vehicle; computer vision algorithm; transportation analytics; routing; navigation; network connectivity system

How to cite: Holmes, J., and Cug, J. (2021). “Autonomous Vehicle Routing and Navigation, Computer Vision Algorithms, and Transportation Analytics in Network Connectivity Systems,” Contemporary Readings in Law and Social Justice 13(2): 135–148. doi: 10.22381/CRLSJ132202110.

Received 13 June 2021 • Received in revised form 9 November 2021
Accepted 11 November 2021 • Available online 15 November 2021

John Holmes
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Center for Intelligent Vehicular Networks
at AAER, El Paso, TX, USA
(corresponding author)
Juraj Cug
This email address is being protected from spambots. You need JavaScript enabled to view it.
Department of Economics,
Faculty of Operation and Economics
of Transport and Communications,
University of Zilina, Zilina, Slovak Republic

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