{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:31:27Z","timestamp":1772724687025,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T00:00:00Z","timestamp":1578441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for semantic understanding. Our approach focuses on dealing with the localization uncertainty, which is a key issue, while parsing information from the prior map. Readings from an exteroceptive sensor are as well integrated in the grid to provide real-time obstacle information. All the information is managed in an evidential framework based on Dempster\u2013Shafer theory. Real road results are reported with qualitative evaluation and quantitative analysis of the constructed grids to show the performance and the behavior of the method for real-time application.<\/jats:p>","DOI":"10.3390\/s20020352","type":"journal-article","created":{"date-parts":[[2020,1,9]],"date-time":"2020-01-09T03:07:11Z","timestamp":1578539231000},"page":"352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9969-3393","authenticated-orcid":false,"given":"Chunlei","family":"Yu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, 10084 Beijing, China"},{"name":"Sorbonne Universit\u00e9s, Universit\u00e9 de Technologie de Compi\u00e8gne, CNRS Heudiasyc UMR 7253, 60203 Compiegne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronique","family":"Cherfaoui","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9s, Universit\u00e9 de Technologie de Compi\u00e8gne, CNRS Heudiasyc UMR 7253, 60203 Compiegne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5842-1399","authenticated-orcid":false,"given":"Philippe","family":"Bonnifait","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9s, Universit\u00e9 de Technologie de Compi\u00e8gne, CNRS Heudiasyc UMR 7253, 60203 Compiegne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dian-ge","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, 10084 Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/2.30720","article-title":"Using occupancy grids for mobile robot perception and navigation","volume":"22","author":"Elfes","year":"1989","journal-title":"Computer"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, X.N., Gao, H., Zhou, M., Tan, C., and Xue, C. 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