Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal Disks
Maps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centerlines are valuable references in the prediction and planning of trajectories, most cente...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2571 |
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| author | Chenhui Yin Marco Cecotti Daniel J. Auger Abbas Fotouhi Haobin Jiang |
| author_facet | Chenhui Yin Marco Cecotti Daniel J. Auger Abbas Fotouhi Haobin Jiang |
| author_sort | Chenhui Yin |
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| description | Maps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centerlines are valuable references in the prediction and planning of trajectories, most centerline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centerline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centerline by searching and linking the internal maximal circles along the lane. Compared with other centerline extraction methods producing similar numbers of center points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform. |
| format | Article |
| id | doaj-art-0f9638d2a40141f094126beb928ce718 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0f9638d2a40141f094126beb928ce7182025-08-20T02:25:07ZengMDPI AGSensors1424-82202025-04-01258257110.3390/s25082571Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal DisksChenhui Yin0Marco Cecotti1Daniel J. Auger2Abbas Fotouhi3Haobin Jiang4School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaAdvanced Vehicle Engineering Centre, Cranfield University, Cranfield MK43 0AL, UKAdvanced Vehicle Engineering Centre, Cranfield University, Cranfield MK43 0AL, UKAdvanced Vehicle Engineering Centre, Cranfield University, Cranfield MK43 0AL, UKAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaMaps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centerlines are valuable references in the prediction and planning of trajectories, most centerline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centerline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centerline by searching and linking the internal maximal circles along the lane. Compared with other centerline extraction methods producing similar numbers of center points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform.https://www.mdpi.com/1424-8220/25/8/2571maximal diskdistance transformvoronoi tessellationcenterline extraction |
| spellingShingle | Chenhui Yin Marco Cecotti Daniel J. Auger Abbas Fotouhi Haobin Jiang Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal Disks Sensors maximal disk distance transform voronoi tessellation centerline extraction |
| title | Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal Disks |
| title_full | Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal Disks |
| title_fullStr | Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal Disks |
| title_full_unstemmed | Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal Disks |
| title_short | Lane Centerline Extraction Based on Surveyed Boundaries: An Efficient Approach Using Maximal Disks |
| title_sort | lane centerline extraction based on surveyed boundaries an efficient approach using maximal disks |
| topic | maximal disk distance transform voronoi tessellation centerline extraction |
| url | https://www.mdpi.com/1424-8220/25/8/2571 |
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