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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Concurrency and computation Jg. 34; H. 8
Hauptverfasser: Yao, Haichang, Chen, Chen, Liu, Shangdong, Li, Kui, Ji, Yimu, Huang, Guangyan, Wang, Ruchuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 10.04.2022
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
high‐precision map
lane marking detection
LIDAR
Marking
Multisensor fusion
Road conditions
ISSN:1532-0626, 1532-0634
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5797