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Intelligent Transportation Applications, Autonomous Vehicle Perception Sensor Data, and Decision-Making Self-Driving Car Control Algorithms in Smart Sustainable Urban Mobility Systems

Intelligent Transportation Applications, Autonomous Vehicle Perception Sensor Data, and... 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 and 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Readings in Law and Social Justice Addleton Academic Publishers

Intelligent Transportation Applications, Autonomous Vehicle Perception Sensor Data, and Decision-Making Self-Driving Car Control Algorithms in Smart Sustainable Urban Mobility Systems

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1948-9137
eISSN
2162-2752
Publisher site
See Article on Publisher Site

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 and 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

Journal

Contemporary Readings in Law and Social JusticeAddleton Academic Publishers

Published: Jan 1, 2021

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