End-to-End Online Vectorized Map Construction With Confidence Estimates
This study proposes an end-to-end online vector map generation method, termed COMap, which integrates confidence estimation to enhance the robustness and reliability of environmental perception systems in autonomous driving. The method is based on the DETR framework and introduces a dynamic instance...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11028039/ |
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| author | Bo Huang Feng Du Tao Peng Xiao-Long Zheng Ji-Quan Wu Jun-Hui Zhang |
| author_facet | Bo Huang Feng Du Tao Peng Xiao-Long Zheng Ji-Quan Wu Jun-Hui Zhang |
| author_sort | Bo Huang |
| collection | DOAJ |
| description | This study proposes an end-to-end online vector map generation method, termed COMap, which integrates confidence estimation to enhance the robustness and reliability of environmental perception systems in autonomous driving. The method is based on the DETR framework and introduces a dynamic instance query mechanism to strengthen the geometric representation capabilities of map elements. Additionally, it employs a full-covariance Gaussian distribution for confidence modeling, effectively quantifying prediction uncertainty and thereby improving the reliability of the map data. Experimental results demonstrate that COMap achieves a higher mean Average Precision (mAP) value on the nuScenes dataset compared to existing methods and significantly reduces the false detection rate in complex scenarios, thereby enhancing the accuracy of road structure representation. This method not only improves the stability of online map generation but also provides more reliable data support for downstream tasks such as path planning and decision-making in autonomous driving. |
| format | Article |
| id | doaj-art-3171911b1bbc4f13965a9986a3a8affc |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3171911b1bbc4f13965a9986a3a8affc2025-08-20T03:21:32ZengIEEEIEEE Access2169-35362025-01-011310119610121010.1109/ACCESS.2025.357775111028039End-to-End Online Vectorized Map Construction With Confidence EstimatesBo Huang0https://orcid.org/0009-0001-2653-265XFeng Du1https://orcid.org/0000-0003-1833-1476Tao Peng2Xiao-Long Zheng3https://orcid.org/0009-0007-0668-5871Ji-Quan Wu4https://orcid.org/0009-0002-1602-4931Jun-Hui Zhang5School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, ChinaThis study proposes an end-to-end online vector map generation method, termed COMap, which integrates confidence estimation to enhance the robustness and reliability of environmental perception systems in autonomous driving. The method is based on the DETR framework and introduces a dynamic instance query mechanism to strengthen the geometric representation capabilities of map elements. Additionally, it employs a full-covariance Gaussian distribution for confidence modeling, effectively quantifying prediction uncertainty and thereby improving the reliability of the map data. Experimental results demonstrate that COMap achieves a higher mean Average Precision (mAP) value on the nuScenes dataset compared to existing methods and significantly reduces the false detection rate in complex scenarios, thereby enhancing the accuracy of road structure representation. This method not only improves the stability of online map generation but also provides more reliable data support for downstream tasks such as path planning and decision-making in autonomous driving.https://ieeexplore.ieee.org/document/11028039/Online vector map generationconfidence estimationend-to-end map buildingautonomous driving perception |
| spellingShingle | Bo Huang Feng Du Tao Peng Xiao-Long Zheng Ji-Quan Wu Jun-Hui Zhang End-to-End Online Vectorized Map Construction With Confidence Estimates IEEE Access Online vector map generation confidence estimation end-to-end map building autonomous driving perception |
| title | End-to-End Online Vectorized Map Construction With Confidence Estimates |
| title_full | End-to-End Online Vectorized Map Construction With Confidence Estimates |
| title_fullStr | End-to-End Online Vectorized Map Construction With Confidence Estimates |
| title_full_unstemmed | End-to-End Online Vectorized Map Construction With Confidence Estimates |
| title_short | End-to-End Online Vectorized Map Construction With Confidence Estimates |
| title_sort | end to end online vectorized map construction with confidence estimates |
| topic | Online vector map generation confidence estimation end-to-end map building autonomous driving perception |
| url | https://ieeexplore.ieee.org/document/11028039/ |
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