{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:41:42Z","timestamp":1760229702079,"version":"build-2065373602"},"reference-count":16,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["U1864203","2020AAA001","2020BAB099","2021BED004"],"award-info":[{"award-number":["U1864203","2020AAA001","2020BAB099","2021BED004"]}]},{"name":"Science and Technology Major Special Project of Hubei","award":["U1864203","2020AAA001","2020BAB099","2021BED004"],"award-info":[{"award-number":["U1864203","2020AAA001","2020BAB099","2021BED004"]}]},{"name":"Key Research &amp; Development Program of Hubei","award":["U1864203","2020AAA001","2020BAB099","2021BED004"],"award-info":[{"award-number":["U1864203","2020AAA001","2020BAB099","2021BED004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Automatic control of intelligent vehicles depends heavily on accurate lane-level location information. The traditional digital map cannot meet the automatic driving requirements, and it is very difficult for lane-level road maps (LRMs) to achieve a good balance between the representation accuracy and data storage volume. To tackle this problem, we obtain the recursive definition of a lane line by analyzing road lanes using the fractal geometry theory. Subsequently, we construct a recursive feedback system and model it. On this basis, an autonomous storage optimization LRM generation strategy based on cascaded NURBS is devised. According to the input data density, this framework generates an LRM with the minimum data storage volume within the error constraint. It is mainly composed of two modules: (1) a random fractal and (2) an adaptive iteration memory optimizer, based on cascaded NURBS error feedback. First, the whole lane is divided into several meta lane line modules. Second, data sampling\/interpolation is carried out iteratively according to the NURBS fitting error and symmetry of road geometry. In this way, the storage space used to store lane lines and other information is automatically optimized. Last, a simulation experiment is carried out by using a dataset obtained from a park. The simulation results show that the proposed method can achieve high accuracy and high storage efficiency. Under the constraint of a 0.1 m curve fitting error, the average memory demand of the lane line elements is less than 2.4 bytes\/m.<\/jats:p>","DOI":"10.3390\/sym14071307","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"1307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Autonomous Storage Optimization LRM Generation Strategy Based on Cascaded NURBS"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2533-0781","authenticated-orcid":false,"given":"Song","family":"Ding","sequence":"first","affiliation":[{"name":"School of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kui","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanding","family":"Yang","sequence":"additional","affiliation":[{"name":"Dongfeng USharing Technology Co., Ltd., Dongfeng Motor Corporation, Wuhan 430056, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youbing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Cao","sequence":"additional","affiliation":[{"name":"Dongfeng USharing Technology Co., Ltd., Dongfeng Motor Corporation, Wuhan 430056, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanghuang","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongzhi","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, D., Chung, T., and Yi, K. (July, January 28). Lane map building and localization for automated driving using 2D laser rangefinder. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea.","DOI":"10.1109\/IVS.2015.7225763"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4517","DOI":"10.1109\/TVT.2016.2535210","article-title":"Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems","volume":"66","author":"Gwon","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_3","unstructured":"Yu, Z., Zhu, H., Lin, L., Liang, H., Yu, B., and Huang, W. (2020). Laser Data Based Automatic Generation of Lane-Level Road Map for Intelligent Vehicles. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Stannartz, N., Theers, M., Llarena, A., Sons, M., Kuhn, M., and Bertram, T. (2020, January 20\u201323). Comparison of Curve Representations for Memory-Efficient and High-Precision Map Generation. 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The NURBS Book, Springer.","DOI":"10.1007\/978-3-642-97385-7"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.matcom.2014.12.003","article-title":"A new iterative algorithm for creating a mean 3D axis of a road from a set of GNSS traces","volume":"118","author":"Reinoso","year":"2015","journal-title":"Math. Comput. Simul."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/7\/1307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:39:04Z","timestamp":1760139544000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/7\/1307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":16,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["sym14071307"],"URL":"https:\/\/doi.org\/10.3390\/sym14071307","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2022,6,24]]}}}