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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/18/7056 |
| _version_ | 1827656684699385856 |
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| author | Jinseop Jeong Jun Yong Yoon Hwanhong Lee Hatem Darweesh Woosuk Sung |
| author_facet | Jinseop Jeong Jun Yong Yoon Hwanhong Lee Hatem Darweesh Woosuk Sung |
| author_sort | Jinseop Jeong |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T22:32:49Z |
| format | Article |
| id | doaj.art-5ba1f90366e64308b1fcccfe7d784bc0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:32:49Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ba1f90366e64308b1fcccfe7d784bc02023-11-23T18:53:44ZengMDPI AGSensors1424-82202022-09-012218705610.3390/s22187056Tutorial on High-Definition Map Generation for Automated Driving in Urban EnvironmentsJinseop Jeong0Jun Yong Yoon1Hwanhong Lee2Hatem Darweesh3Woosuk Sung4School of Mechanical System and Automotive Engineering, Chosun University, Gwangju 61452, KoreaSchool of Mechanical System and Automotive Engineering, Chosun University, Gwangju 61452, KoreaSchool of Mechanical System and Automotive Engineering, Chosun University, Gwangju 61452, KoreaGraduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, JapanSchool of Mechanical System and Automotive Engineering, Chosun University, Gwangju 61452, KoreaHigh-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.https://www.mdpi.com/1424-8220/22/18/7056autonomous drivinghigh-definition maplocalizationpath planning |
| spellingShingle | Jinseop Jeong Jun Yong Yoon Hwanhong Lee Hatem Darweesh Woosuk Sung Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments Sensors autonomous driving high-definition map localization path planning |
| 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 | autonomous driving high-definition map localization path planning |
| url | https://www.mdpi.com/1424-8220/22/18/7056 |
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