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Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul

Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems a...

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Autores principales: Cho, Minwoo, Kim, Kitae, Cho, Soohyun, Cho, Seung-Mo, Chung, Woojin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824216/
https://www.ncbi.nlm.nih.gov/pubmed/36617036
http://dx.doi.org/10.3390/s23010438
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author Cho, Minwoo
Kim, Kitae
Cho, Soohyun
Cho, Seung-Mo
Chung, Woojin
author_facet Cho, Minwoo
Kim, Kitae
Cho, Soohyun
Cho, Seung-Mo
Chung, Woojin
author_sort Cho, Minwoo
collection PubMed
description Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated.
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spelling pubmed-98242162023-01-08 Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul Cho, Minwoo Kim, Kitae Cho, Soohyun Cho, Seung-Mo Chung, Woojin Sensors (Basel) Article Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated. MDPI 2022-12-31 /pmc/articles/PMC9824216/ /pubmed/36617036 http://dx.doi.org/10.3390/s23010438 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 Article
Cho, Minwoo
Kim, Kitae
Cho, Soohyun
Cho, Seung-Mo
Chung, Woojin
Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul
title Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul
title_full Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul
title_fullStr Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul
title_full_unstemmed Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul
title_short Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul
title_sort frequent and automatic update of lane-level hd maps with a large amount of crowdsourced data acquired from buses and taxis in seoul
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824216/
https://www.ncbi.nlm.nih.gov/pubmed/36617036
http://dx.doi.org/10.3390/s23010438
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