A Lane-Level Road Marking Map Using a Monocular Camera
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Wonje | - |
dc.contributor.author | Hyun, Junhyuk | - |
dc.contributor.author | An, Jhonghyun | - |
dc.contributor.author | Cho, Minho | - |
dc.contributor.author | Kim, Euntai | - |
dc.date.accessioned | 2022-06-14T01:40:05Z | - |
dc.date.available | 2022-06-14T01:40:05Z | - |
dc.date.created | 2022-06-14 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 2329-9266 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84623 | - |
dc.description.abstract | The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE-CAA JOURNAL OF AUTOMATICA SINICA | - |
dc.title | A Lane-Level Road Marking Map Using a Monocular Camera | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000709092600019 | - |
dc.identifier.doi | 10.1109/JAS.2021.1004293 | - |
dc.identifier.bibliographicCitation | IEEE-CAA JOURNAL OF AUTOMATICA SINICA, v.9, no.1, pp.187 - 204 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85118293175 | - |
dc.citation.endPage | 204 | - |
dc.citation.startPage | 187 | - |
dc.citation.title | IEEE-CAA JOURNAL OF AUTOMATICA SINICA | - |
dc.citation.volume | 9 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | An, Jhonghyun | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Autonomous driving | - |
dc.subject.keywordAuthor | lane-level map | - |
dc.subject.keywordAuthor | road lane | - |
dc.subject.keywordAuthor | road marking map | - |
dc.subject.keywordAuthor | symbolic road marking | - |
dc.subject.keywordAuthor | weighted loss | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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