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Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments
High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional se...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503031/ https://www.ncbi.nlm.nih.gov/pubmed/36146405 http://dx.doi.org/10.3390/s22187056 |
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author | Jeong, Jinseop Yoon, Jun Yong Lee, Hwanhong Darweesh, Hatem Sung, Woosuk |
author_facet | Jeong, Jinseop Yoon, Jun Yong Lee, Hwanhong Darweesh, Hatem Sung, Woosuk |
author_sort | Jeong, Jinseop |
collection | PubMed |
description | High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy. |
format | Online Article Text |
id | pubmed-9503031 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95030312022-09-24 Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments Jeong, Jinseop Yoon, Jun Yong Lee, Hwanhong Darweesh, Hatem Sung, Woosuk Sensors (Basel) Tutorial High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy. MDPI 2022-09-18 /pmc/articles/PMC9503031/ /pubmed/36146405 http://dx.doi.org/10.3390/s22187056 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Tutorial Jeong, Jinseop Yoon, Jun Yong Lee, Hwanhong Darweesh, Hatem Sung, Woosuk Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments |
title | Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments |
title_full | Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments |
title_fullStr | Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments |
title_full_unstemmed | Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments |
title_short | Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments |
title_sort | tutorial on high-definition map generation for automated driving in urban environments |
topic | Tutorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503031/ https://www.ncbi.nlm.nih.gov/pubmed/36146405 http://dx.doi.org/10.3390/s22187056 |
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