ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore intelligent transportation applications, autonomous vehicle perception sensor data, and decision-making self-driving car control algorithms in smart sustainable urban mobility systems. Using and replicating data from AUVSI, Black & Veatch, Brookings, CarGurus, Deloitte, Ipsos, Kennedys, McKinsey, MRCagney, Perkins Coie, Reuters, and SAE, we performed analyses and made estimates regarding how connected and autonomous vehicles will reduce traffic crashes by routing and navigating decisions in terms of smart traffic data, sensing technologies, traffic management and analytics, and automotive radar techniques across sustainable smart transport and mobility systems. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: perception sensor data; decision-making algorithm; intelligent transportation application; autonomous vehicle; smart sustainable urban mobility system

How to cite: Woodward, B., and Kliestik, T. (2021). “Intelligent Transportation Applications, Autonomous Vehicle Perception Sensor Data, and Decision-Making Self-Driving Car Control Algorithms in Smart Sustainable Urban Mobility Systems,” Contemporary Readings in Law and Social Justice 13(2): 51–64. doi: 10.22381/CRLSJ13220214.

Received 25 June 2021 • Received in revised form 5 November 2021
Accepted 13 November 2021 • Available online 15 November 2021

Barbara Woodward
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Decision-Making Self-Driving Car Control Algorithms
Research Unit at AAER, Oakland, CA, USA
(corresponding author)
Tomas Kliestik
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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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