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Radar Sensing for Intelligent Vehicles in Urban Environments

Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the...

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Detalles Bibliográficos
Autores principales: Reina, Giulio, Johnson, David, Underwood, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507681/
https://www.ncbi.nlm.nih.gov/pubmed/26102493
http://dx.doi.org/10.3390/s150614661
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author Reina, Giulio
Johnson, David
Underwood, James
author_facet Reina, Giulio
Johnson, David
Underwood, James
author_sort Reina, Giulio
collection PubMed
description Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations.
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spelling pubmed-45076812015-07-22 Radar Sensing for Intelligent Vehicles in Urban Environments Reina, Giulio Johnson, David Underwood, James Sensors (Basel) Article Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations. MDPI 2015-06-19 /pmc/articles/PMC4507681/ /pubmed/26102493 http://dx.doi.org/10.3390/s150614661 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Reina, Giulio
Johnson, David
Underwood, James
Radar Sensing for Intelligent Vehicles in Urban Environments
title Radar Sensing for Intelligent Vehicles in Urban Environments
title_full Radar Sensing for Intelligent Vehicles in Urban Environments
title_fullStr Radar Sensing for Intelligent Vehicles in Urban Environments
title_full_unstemmed Radar Sensing for Intelligent Vehicles in Urban Environments
title_short Radar Sensing for Intelligent Vehicles in Urban Environments
title_sort radar sensing for intelligent vehicles in urban environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507681/
https://www.ncbi.nlm.nih.gov/pubmed/26102493
http://dx.doi.org/10.3390/s150614661
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