Lane marking detection algorithm based on high‐precision map and multisensor fusion
Summary In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur...
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| Vydané v: | Concurrency and computation Ročník 34; číslo 8 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Hoboken, USA
John Wiley & Sons, Inc
10.04.2022
Wiley Subscription Services, Inc |
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| ISSN: | 1532-0626, 1532-0634 |
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| Abstract | Summary
In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high‐precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter‐level high‐precision positioning combined with high‐precision map data to complete the detection of lane markings. In the process of generating high‐precision maps or in the uncovered areas of high‐precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. |
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| AbstractList | In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high‐precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter‐level high‐precision positioning combined with high‐precision map data to complete the detection of lane markings. In the process of generating high‐precision maps or in the uncovered areas of high‐precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. Summary In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high‐precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter‐level high‐precision positioning combined with high‐precision map data to complete the detection of lane markings. In the process of generating high‐precision maps or in the uncovered areas of high‐precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. |
| Author | Chen, Chen Liu, Shangdong Yao, Haichang Wang, Ruchuan Huang, Guangyan Li, Kui Ji, Yimu |
| Author_xml | – sequence: 1 givenname: Haichang orcidid: 0000-0002-5751-960X surname: Yao fullname: Yao, Haichang organization: Nanjing Institute of Industry Technology – sequence: 2 givenname: Chen surname: Chen fullname: Chen, Chen organization: Nanjing University of Posts and Telecommunications – sequence: 3 givenname: Shangdong surname: Liu fullname: Liu, Shangdong organization: Nanjing University of Posts and Telecommunications – sequence: 4 givenname: Kui surname: Li fullname: Li, Kui organization: Nanjing University of Posts and Telecommunications – sequence: 5 givenname: Yimu orcidid: 0000-0001-7019-3942 surname: Ji fullname: Ji, Yimu email: jiym@njupt.edu.cn organization: Nanjing University of Posts and Telecommunications – sequence: 6 givenname: Guangyan orcidid: 0000-0002-1821-8644 surname: Huang fullname: Huang, Guangyan organization: Deakin University – sequence: 7 givenname: Ruchuan surname: Wang fullname: Wang, Ruchuan organization: Nanjing University of Posts and Telecommunications |
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| Cites_doi | 10.1142/S0219622007002496 10.1002/cpe.5464 10.5194/isprsarchives-XLI-B3-251-2016 10.1007/978-3-319-91521-0_12 10.3390/math7020135 10.1007/s11432-018-9729-5 10.1109/CVPR.2017.265 10.1016/j.engappai.2018.11.007 10.1109/COMST.2019.2944748 10.1142/S0219622004000957 10.1109/IVS.2015.7225685 10.1142/S0219622018500128 10.1002/cpe.5478 10.1016/j.jpdc.2017.12.014 10.1016/j.jpdc.2016.10.011 10.1109/TII.2018.2822680 10.1109/CyberneticsCom.2013.6865800 10.1109/TNNLS.2016.2522428 10.3390/math7020184 10.1049/cje.2018.11.003 10.1007/s12293-017-0237-2 10.1109/CVPR.2018.00479 10.1109/ICETET.2010.128 10.1109/ASCC.2015.7244831 10.1109/TPAMI.1986.4767773 |
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| Notes | Funding information This paper is the extended version based on the ICDS 2019 conference paper. The source of this paper is in “Jing He, Philip S. Yu, Yong Shi, Xingsen Li, Zhijun Xie, Guangyan Huang, Jie Cao, Pu Xiao, Sixth International Conference, ICDS 2019, Ningbo, China, CCIS 1179, May 15‐20, 2019, Springer.” National Key R&D Program of China, 2017YFB1401302; 2017YFB1401301; Key R&D Program of Jiangsu, BE2017166; Modern Educational Technology Research Program of Jiangsu Province in 2019, 2019‐R‐67748; Open Foundation of Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department, Outstanding Youth of Jiangsu Natural Science Foundation, BK20170100; Postgraduate Research & Practice Innovation Program of Jiangsu Province, KYCX19_0906; KYCX19_0921; National Natural Science Foundation of P. R. China, 61702280; 61902194; Natural Science Foundation of the Jiangsu Higher Education Institutions of China, 19KJD520006; 19KJB520046 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on... In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road... |
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| SubjectTerms | Algorithms high‐precision map lane marking detection LIDAR Marking Multisensor fusion Road conditions |
| Title | Lane marking detection algorithm based on high‐precision map and multisensor fusion |
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