The Development of a Lane Identification and Assessment Framework for Maintenance Using AI Technology
This study proposes a vision-based framework to support AVs in maintaining stable lane-keeping by assessing the condition of lane markings. Unlike existing infrastructure standards focused on human visibility, this study addresses the need for criteria suited to sensor-based AV environments. Using r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7410 |
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| author | Hohyuk Na Do Gyeong Kim Ji Min Kang Chungwon Lee |
| author_facet | Hohyuk Na Do Gyeong Kim Ji Min Kang Chungwon Lee |
| author_sort | Hohyuk Na |
| collection | DOAJ |
| description | This study proposes a vision-based framework to support AVs in maintaining stable lane-keeping by assessing the condition of lane markings. Unlike existing infrastructure standards focused on human visibility, this study addresses the need for criteria suited to sensor-based AV environments. Using real driving data from urban expressways in Seoul, a YOLOv5-based lane detection algorithm was developed and enhanced through multi-label annotation and data augmentation. The model achieved a mean average precision (mAP) of 97.4% and demonstrated strong generalization on external datasets such as KITTI and TuSimple. For lane condition assessment, a pixel occupancy–based method was applied, combined with Canny edge detection and morphological operations. A threshold of 80-pixel occupancy was used to classify lanes as intact or worn. The proposed framework reliably detected lane degradation under various road and lighting conditions. These results suggest that quantitative, image-based indicators can complement traditional standards and guide AV-oriented infrastructure policy. Limitations include a lack of adverse weather data and dataset-specific threshold sensitivity. |
| format | Article |
| id | doaj-art-6e5a2856bbf84f75aa65a98d78e73fbd |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6e5a2856bbf84f75aa65a98d78e73fbd2025-08-20T03:50:17ZengMDPI AGApplied Sciences2076-34172025-07-011513741010.3390/app15137410The Development of a Lane Identification and Assessment Framework for Maintenance Using AI TechnologyHohyuk Na0Do Gyeong Kim1Ji Min Kang2Chungwon Lee3Department of Transportation Engineering, University of Seoul, Seoul 02504, Republic of KoreaDepartment of Transportation Engineering, University of Seoul, Seoul 02504, Republic of KoreaDepartment of Transportation Engineering, University of Seoul, Seoul 02504, Republic of KoreaDepartment of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of KoreaThis study proposes a vision-based framework to support AVs in maintaining stable lane-keeping by assessing the condition of lane markings. Unlike existing infrastructure standards focused on human visibility, this study addresses the need for criteria suited to sensor-based AV environments. Using real driving data from urban expressways in Seoul, a YOLOv5-based lane detection algorithm was developed and enhanced through multi-label annotation and data augmentation. The model achieved a mean average precision (mAP) of 97.4% and demonstrated strong generalization on external datasets such as KITTI and TuSimple. For lane condition assessment, a pixel occupancy–based method was applied, combined with Canny edge detection and morphological operations. A threshold of 80-pixel occupancy was used to classify lanes as intact or worn. The proposed framework reliably detected lane degradation under various road and lighting conditions. These results suggest that quantitative, image-based indicators can complement traditional standards and guide AV-oriented infrastructure policy. Limitations include a lack of adverse weather data and dataset-specific threshold sensitivity.https://www.mdpi.com/2076-3417/15/13/7410maintenance standardslane identification and assessmentlane degradation evaluationpixel occupancyframework |
| spellingShingle | Hohyuk Na Do Gyeong Kim Ji Min Kang Chungwon Lee The Development of a Lane Identification and Assessment Framework for Maintenance Using AI Technology Applied Sciences maintenance standards lane identification and assessment lane degradation evaluation pixel occupancy framework |
| title | The Development of a Lane Identification and Assessment Framework for Maintenance Using AI Technology |
| title_full | The Development of a Lane Identification and Assessment Framework for Maintenance Using AI Technology |
| title_fullStr | The Development of a Lane Identification and Assessment Framework for Maintenance Using AI Technology |
| title_full_unstemmed | The Development of a Lane Identification and Assessment Framework for Maintenance Using AI Technology |
| title_short | The Development of a Lane Identification and Assessment Framework for Maintenance Using AI Technology |
| title_sort | development of a lane identification and assessment framework for maintenance using ai technology |
| topic | maintenance standards lane identification and assessment lane degradation evaluation pixel occupancy framework |
| url | https://www.mdpi.com/2076-3417/15/13/7410 |
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