{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:25:03Z","timestamp":1764937503503,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SK Telecom Co., Ltd., the Industry Core Technology Development Project by MOTIE","award":["20005062","NRF-2021R1A2C2007908"],"award-info":[{"award-number":["20005062","NRF-2021R1A2C2007908"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["20005062","NRF-2021R1A2C2007908"],"award-info":[{"award-number":["20005062","NRF-2021R1A2C2007908"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated.<\/jats:p>","DOI":"10.3390\/s23010438","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:08:59Z","timestamp":1672628939000},"page":"438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2072-149X","authenticated-orcid":false,"given":"Minwoo","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Korea University, Seoul 02841, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8912-9382","authenticated-orcid":false,"given":"Kitae","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0324-6076","authenticated-orcid":false,"given":"Soohyun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea University, Seoul 02841, Republic of Korea"}]},{"given":"Seung-Mo","family":"Cho","sequence":"additional","affiliation":[{"name":"SK Telecom Co., Ltd., Seoul 04539, Republic of Korea"}]},{"given":"Woojin","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea University, Seoul 02841, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"ref_1","unstructured":"(2022, November 04). Aerometrex. Available online: https:\/\/aerometrex.com.au\/."},{"key":"ref_2","unstructured":"(2022, November 04). Nearmap. Available online: https:\/\/www.nearmap.com\/us\/en\/products\/3d-mapping-dsm-textured-mesh-point-cloud."},{"key":"ref_3","unstructured":"(2022, November 04). WRLD3D Augmented Reality Maps. Available online: https:\/\/www.wrld3d.com\/3d-maps\/augmented-reality-3d-maps."},{"key":"ref_4","unstructured":"(2022, November 04). Tomtom HD Map. Available online: https:\/\/www.tomtom.com\/products\/hd-map\/."},{"key":"ref_5","unstructured":"(2022, November 04). HERE HD Live Map. Available online: https:\/\/www.here.com\/platform\/HD-live-map."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ma, W.C., Tartavull, I., B\u00e2rsan, I.A., Wang, S., Bai, M., Mattyus, G., Homayounfar, N., Lakshmikanth, S.K., Pokrovsky, A., and Urtasun, R. (2019, January 3\u20138). Exploiting sparse semantic HD maps for self-driving vehicle localization. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968122"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ghallabi, F., Nashashibi, F., El-Haj-Shhade, G., and Mittet, M.A. (2018, January 4\u20137). Lidar-based lane marking detection for vehicle positioning in an hd map. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569951"},{"key":"ref_8","unstructured":"Yang, B., Liang, M., and Urtasun, R. (2018, January 29\u201331). Hdnet: Exploiting hd maps for 3d object detection. Proceedings of the Conference on Robot Learning, Zurich, Switzerland."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jian, Z., Zhang, S., Chen, S., Lv, X., and Zheng, N. (2019, January 9\u201312). High-definition map combined local motion planning and obstacle avoidance for autonomous driving. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814229"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Diaz-Diaz, A., Oca\u00f1a, M., Llamazares, \u00c1., G\u00f3mez-Hu\u00e9lamo, C., Revenga, P., and Bergasa, L.M. (2022, January 5\u20139). HD maps: Exploiting OpenDRIVE potential for Path Planning and Map Monitoring. Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen, Germany.","DOI":"10.1109\/IV51971.2022.9827297"},{"key":"ref_11","unstructured":"Petrie, G. (2010). An Introduction to the Technology: Mobile Mapping Systems. GeoInformatics, 13."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MITS.2014.2306552","article-title":"Making bertha drive\u2014An autonomous journey on a historic route","volume":"6","author":"Ziegler","year":"2014","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.isprsjprs.2013.01.016","article-title":"Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds","volume":"79","author":"Yang","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"57","DOI":"10.5194\/isprsannals-II-3-W5-57-2015","article-title":"Detection and Classification of Pole-like Objects from Mobile Mapping Data","volume":"2","author":"Fukano","year":"2015","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/JSTARS.2014.2347276","article-title":"Learning hierarchical features for automated extraction of road markings from 3 to D mobile LiDAR point clouds","volume":"8","author":"Yu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","unstructured":"He, B., Ai, R., Yan, Y., and Lang, X. (2016, January 1\u20134). Lane marking detection based on convolution neural network from point clouds. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rachmadi, R.F., Uchimura, K., Koutaki, G., and Ogata, K. (2017, January 26\u201327). Road edge detection on 3D point cloud data using Encoder-Decoder Convolutional Network. Proceedings of the 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Surabaya, Indonesia.","DOI":"10.1109\/KCIC.2017.8228570"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/TGRS.2016.2607521","article-title":"Recognizing street lighting poles from mobile LiDAR data","volume":"55","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","unstructured":"(2022, November 04). Crowd Sourcing for Automated Driving: BMW Group and Mobileye Agree to Generate New Kind of Sensor Data. Available online: https:\/\/www.press.bmwgroup.com\/global\/article\/detail\/T0268039EN\/crowd-sourcing-for-automated-driving:-bmw-group-and-mobileye-agree-to-generate-new-kind-of-sensor-data."},{"key":"ref_20","unstructured":"(2022, November 04). Collaboration with TomTom for Real-Time HD Map Updating. Available online: https:\/\/hella-aglaia.com\/2019\/09\/05\/tomtom-collaborates-with-hella-aglaia-for-real-time-hd-map-updating\/."},{"key":"ref_21","unstructured":"(2022, November 04). Mobileye REMTM-Road Experience Management. Available online: https:\/\/www.mobileye.com\/technology\/rem\/."},{"key":"ref_22","unstructured":"(2022, November 04). Seoul and SKT to Jointly Develop the Real Time Super Precision Road Map Indispensable for AV with 5G and AI. Available online: http:\/\/english.seoul.go.kr\/seoul-and-skt-to-jointly-develop-the-real-time-super-precision-road-map-indispensable\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/TITS.2020.3040728","article-title":"Efficient lane-level map building via vehicle-based crowdsourcing","volume":"23","author":"Shu","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.trc.2018.02.007","article-title":"Generating lane-based intersection maps from crowdsourcing big trace data","volume":"89","author":"Yang","year":"2018","journal-title":"Transp. Res. Part Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1109\/TITS.2016.2521819","article-title":"A low-cost solution for automatic lane-level map generation using conventional in-car sensors","volume":"17","author":"Guo","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Takeda, Y., Tomizuka, M., and Zhan, W. (October, January 27). Automatic construction of lane-level hd maps for urban scenes. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636205"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dabeer, O., Ding, W., Gowaiker, R., Grzechnik, S.K., Lakshman, M.J., Lee, S., Reitmayr, G., Sharma, A., Somasundaram, K., and Sukhavasi, R.T. (2017, January 24\u201328). An end-to-end system for crowdsourced 3D maps for autonomous vehicles: The mapping component. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202218"},{"key":"ref_28","unstructured":"Liang, D., Guo, Y., Zhang, S., Zhang, S.H., Hall, P., Zhang, M., and Hu, S. (2018). LineNet: A zoomable CNN for crowdsourced high definition maps modeling in urban environments. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s42154-021-00173-x","article-title":"Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level","volume":"5","author":"Wijaya","year":"2022","journal-title":"Automot. Innov."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, J., Guo, Y., Bian, Y., Huang, Y., and Li, B. (2022). Lane Information Extraction for High Definition Maps Using Crowdsourced Data. IEEE Trans. Intell. Transp. Syst.","DOI":"10.1109\/TITS.2022.3222504"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Schreiber, M., Hellmund, A.M., and Stiller, C. (July, January 28). Multi-drive feature association for automated map generation using low-cost sensor data. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Republic of Korea.","DOI":"10.1109\/IVS.2015.7225837"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2552","DOI":"10.1109\/TITS.2016.2521482","article-title":"CLRIC: Collecting lane-based road information via crowdsourcing","volume":"7","author":"Tang","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jo, K., Kim, C., and Sunwoo, M. (2018). Simultaneous localization and map change update for the high definition map-based autonomous driving car. Sensors, 18.","DOI":"10.3390\/s18093145"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kim, C., Cho, S., Sunwoo, M., and Jo, K. (2018). Crowd-sourced mapping of new feature layer for high-definition map. Sensors, 18.","DOI":"10.3390\/s18124172"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8028","DOI":"10.1109\/ACCESS.2021.3049482","article-title":"Updating point cloud layer of high definition (hd) map based on crowd-sourcing of multiple vehicles installed lidar","volume":"9","author":"Kim","year":"2021","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Welte, A., Xu, P., Bonnifait, P., and Zinoune, C. (2021, January 11\u201317). HD Map Errors Detection using Smoothing and Multiple Drives. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), Nagoya, Japan.","DOI":"10.1109\/IVWorkshops54471.2021.9669237"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s42154-022-00188-y","article-title":"GCD-L: A Novel Method for Geometric Change Detection in HD Maps Using Low-Cost Sensors","volume":"5","author":"Sun","year":"2022","journal-title":"Automot. Innov."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pannen, D., Liebner, M., and Burgard, W. (2019, January 20\u201324). Hd map change detection with a boosted particle filter. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794329"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pannen, D., Liebner, M., Hempel, W., and Burgard, W. (August, January 31). How to keep HD maps for automated driving up to date. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197419"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Massow, K., Kwella, B., Pfeifer, N., H\u00e4usler, F., Pontow, J., Radusch, I., Hipp, J., D\u00f6litzscher, F., and Haueis, M. (2016, January 1\u20134). Deriving HD maps for highly automated driving from vehicular probe data. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795794"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kim, C., Jo, K., Bradai, B., and Sunwoo, M. (2017, January 25\u201326). Multiple vehicles based new landmark feature mapping for highly autonomous driving map. Proceedings of the 2017 14th Workshop on Positioning, Navigation and Communications (WPNC), Bremen, Germany.","DOI":"10.1109\/WPNC.2017.8250071"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Stoven-Dubois, A., Miguel, K.K., Dziri, A., Leroy, B., and Chapuis, R. (2019, January 27\u201330). A collaborative framework for high-definition mapping. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917292"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jiao, J. (2018, January 23\u201327). Machine learning assisted high-definition map creation. Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan.","DOI":"10.1109\/COMPSAC.2018.00058"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1109\/LRA.2021.3060406","article-title":"HD map update for autonomous driving with crowdsourced data","volume":"6","author":"Kim","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_45","unstructured":"Grisetti, G., K\u00fcmmerle, R., Strasdat, H., and Konolige, K. (2011, January 9\u201313). g2o: A general framework for (hyper) graph optimization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1093\/comjnl\/22.3.262","article-title":"Constructing the convex hull of a set of points in the plane","volume":"22","author":"Green","year":"1979","journal-title":"Comput. J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pannen, D., Liebner, M., and Burgard, W. (2019, January 3\u20138). Lane marking learning based on crowdsourced data. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967818"},{"key":"ref_48","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Kdd, Portland, OR, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gonzalez, T., Diaz-Herrera, J., and Tucker, A. (2014). Computing Handbook: Computer Science and Software Engineering, CRC Press.","DOI":"10.1201\/b16812"},{"key":"ref_50","unstructured":"(2022, November 04). Kakao Map. Available online: https:\/\/map.kakao.com."},{"key":"ref_51","unstructured":"(2022, November 04). Naver Map. Available online: https:\/\/m.map.naver.com\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/438\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:58Z","timestamp":1760147338000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,31]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010438"],"URL":"https:\/\/doi.org\/10.3390\/s23010438","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,31]]}}}