ABSTRACT. This article presents an empirical study carried out to evaluate and analyze autonomous driving algorithms and behaviors, sensing and computing technologies, and connected vehicle data in smart transportation networks. Building our argument by drawing on data collected from AAA, AUVSI, CARiD, Deloitte, McKinsey, Perkins Coie, Schoettle & Sivak (2014), Statista, Thomas et al. (2015), and YouGov, we performed analyses and made estimates regarding how autonomous vehicle planning and driving algorithms can optimize road safety, being instrumental in sensor and data processing methods by cutting down crashes and casualties through a massive volume of information that can be shared between self-driving cars and roadside infrastructure. Data collected from 5,900 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: sensing and computing technology; autonomous driving; smart transportation network; connected vehicle data; algorithm; behavior

How to cite: Clayton, E., and Kral, P. (2021). “Autonomous Driving Algorithms and Behaviors, Sensing and Computing Technologies, and Connected Vehicle Data in Smart Transportation Networks,” Contemporary Readings in Law and Social Justice 13(2): 9–22. doi: 10.22381/CRLSJ13220211.

Received 18 June 2021 • Received in revised form 9 November 2021
Accepted 12 November 2021 • Available online 15 November 2021

Elizabeth Clayton
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Center for Networked Transport Systems
at AAER, Leeds, England
(corresponding author)
Pavol Kral
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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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