{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:13:00Z","timestamp":1773155580295,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T00:00:00Z","timestamp":1693699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["PID2021-125850OB-I00"],"award-info":[{"award-number":["PID2021-125850OB-I00"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["S2018-EMT-4362"],"award-info":[{"award-number":["S2018-EMT-4362"]}]},{"name":"ERDF A way of making Europe with the project DISCERN","award":["PID2021-125850OB-I00"],"award-info":[{"award-number":["PID2021-125850OB-I00"]}]},{"name":"ERDF A way of making Europe with the project DISCERN","award":["S2018-EMT-4362"],"award-info":[{"award-number":["S2018-EMT-4362"]}]},{"name":"Community of Madrid through the SEGVAUTO 4.0-CM Programme","award":["PID2021-125850OB-I00"],"award-info":[{"award-number":["PID2021-125850OB-I00"]}]},{"name":"Community of Madrid through the SEGVAUTO 4.0-CM Programme","award":["S2018-EMT-4362"],"award-info":[{"award-number":["S2018-EMT-4362"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.<\/jats:p>","DOI":"10.3390\/s23177633","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T02:59:55Z","timestamp":1693796395000},"page":"7633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1088-4052","authenticated-orcid":false,"given":"Juan Luis","family":"Hortelano","sequence":"first","affiliation":[{"name":"Centro de Autom\u00e1tica y Rob\u00f3tica, CSIC\u2014Universidad Polit\u00e9cnica de Madrid, Ctra. Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3963-7952","authenticated-orcid":false,"given":"Jorge","family":"Villagr\u00e1","sequence":"additional","affiliation":[{"name":"Centro de Autom\u00e1tica y Rob\u00f3tica, CSIC\u2014Universidad Polit\u00e9cnica de Madrid, Ctra. Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3132-5348","authenticated-orcid":false,"given":"Jorge","family":"Godoy","sequence":"additional","affiliation":[{"name":"Centro de Autom\u00e1tica y Rob\u00f3tica, CSIC\u2014Universidad Polit\u00e9cnica de Madrid, Ctra. Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1197-0937","authenticated-orcid":false,"given":"V\u00edctor","family":"Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Centro de Autom\u00e1tica y Rob\u00f3tica, CSIC\u2014Universidad Polit\u00e9cnica de Madrid, Ctra. Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pauls, J.H., Strauss, T., Hasberg, C., Lauer, M., and Stiller, C. (2018, January 4\u20137). Can we trust our maps? An evaluation of road changes and a dataset for map validation. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569249"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MITS.2020.3014152","article-title":"Mapping for autonomous driving: Opportunities and challenges","volume":"13","author":"Wong","year":"2020","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s00138-011-0404-2","article-title":"Recent progress in road and lane detection: A survey","volume":"25","author":"Lerner","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1016\/j.engappai.2013.01.006","article-title":"Terrain traversability analysis methods for unmanned ground vehicles: A survey","volume":"26","author":"Papadakis","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s11390-020-0476-4","article-title":"Lane detection: A survey with new results","volume":"35","author":"Liang","year":"2020","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gao, B., Pan, Y., Li, C., Geng, S., and Zhao, H. (2021). Are we hungry for 3D LiDAR data for semantic segmentation? a survey of datasets and methods. arXiv.","DOI":"10.1109\/TITS.2021.3076844"},{"key":"ref_8","unstructured":"(2023, April 09). OpenStreetMap Contributors. Available online: https:\/\/www.openstreetmap.org."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Laddha, A., Kocamaz, M.K., Navarro-Serment, L.E., and Hebert, M. (2016, January 19\u201322). Map-supervised road detection. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535374"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107958","DOI":"10.1109\/ACCESS.2020.3000777","article-title":"Map-enhanced ego-lane detection in the missing feature scenarios","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Poggenhans, F., Pauls, J.H., Janosovits, J., Orf, S., Naumann, M., Kuhnt, F., and Mayr, M. (2018, January 4\u20137). Lanelet2: A High-Definition Map Framework for the Future of Automated Driving. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569929"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Maierhofer, S., Klischat, M., and Althoff, M. (2021, January 19\u201322). Commonroad scenario designer: An open-source toolbox for map conversion and scenario creation for autonomous vehicles. Proceedings of the 2021 IEEE Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564885"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2020, January 13\u201319). Scalability in perception for autonomous driving: Waymo open dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_14","unstructured":"Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., Khandelwal, S., Pan, B., Kumar, R., Hartnett, A., and Pontes, J.K. (2023). Argoverse 2: Next generation datasets for self-driving perception and forecasting. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fritsch, J., Kuehnl, T., and Geiger, A. (2013, January 6\u20139). A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms. Proceedings of the International Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728473"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuScenes: A multimodal dataset for autonomous driving. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Conference, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020, January 20\u201325). BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liao, Y., Xie, J., and Geiger, A. (2021). KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D. arXiv.","DOI":"10.1109\/TPAMI.2022.3179507"},{"key":"ref_20","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. (November, January 27). SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. Proceedings of the IEEE\/CVF Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_21","unstructured":"Vasiljevic, I., Kolkin, N., Zhang, S., Luo, R., Wang, H., Dai, F.Z., Daniele, A.F., Mostajabi, M., Basart, S., and Walter, M.R. (2019). DIODE: A Dense Indoor and Outdoor DEpth Dataset. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Plachetka, C., Sertolli, B., Fricke, J., Klingner, M., and Fingscheidt, T. (2022, January 8\u201312). 3DHD CityScenes: High-Definition Maps in High-Density Point Clouds. Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.","DOI":"10.1109\/ITSC55140.2022.9921866"},{"key":"ref_23","unstructured":"Wang, H., Liu, Z., Li, Y., Li, T., Chen, L., Sima, C., Wang, Y., Jiang, S., Wen, F., and Xu, H. (2023). Road Genome: A Topology Reasoning Benchmark for Scene Understanding in Autonomous Driving. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, Q., Wang, Y., Wang, Y., and Zhao, H. (2021). HDMapNet: An Online HD Map Construction and Evaluation Framework. arXiv.","DOI":"10.1109\/ICRA46639.2022.9812383"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xue, H., Fu, H., Ren, R., Zhang, J., Liu, B., Fan, Y., and Dai, B. (October, January 27). LiDAR-based Drivable Region Detection for Autonomous Driving. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636289"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fan, R., Wang, H., Cai, P., and Liu, M. (2020, January 23\u201328). Sne-roadseg: Incorporating surface normal information into semantic segmentation for accurate freespace detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.36227\/techrxiv.12864287"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/JAS.2019.1911459","article-title":"Progressive lidar adaptation for road detection","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chang, Y., Xue, F., Sheng, F., Liang, W., and Ming, A. (2022). Fast Road Segmentation via Uncertainty-aware Symmetric Network. arXiv.","DOI":"10.1109\/ICRA46639.2022.9812452"},{"key":"ref_29","unstructured":"Liu, Z., Yu, S., Wang, X., and Zheng, N. (2017). Detecting drivable area for self-driving cars: An unsupervised approach. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sun, J.Y., Kim, S.W., Lee, S.W., Kim, Y.W., and Ko, S.J. (2019, January 27\u201328). Reverse and boundary attention network for road segmentation. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00116"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3070","DOI":"10.1109\/TITS.2018.2871945","article-title":"Histograms of the normalized inverse depth and line scanning for urban road detection","volume":"20","author":"Gu","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TITS.2018.2789462","article-title":"Road-segmentation-based curb detection method for self-driving via a 3D-LiDAR sensor","volume":"19","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, L., Yang, J., and Kong, H. (June, January 29). Lidar-histogram for fast road and obstacle detection. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989159"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lyu, Y., Bai, L., and Huang, X. (2019, January 26\u201329). Road segmentation using cnn and distributed lstm. Proceedings of the 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan.","DOI":"10.1109\/ISCAS.2019.8702174"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10846-021-01381-7","article-title":"A Framework for Drivable Area Detection Via Point Cloud Double Projection on Rough Roads","volume":"102","author":"Xu","year":"2021","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5386","DOI":"10.1109\/TCSVT.2022.3146305","article-title":"Pseudo-LiDAR-Based Road Detection","volume":"32","author":"Sun","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gu, S., Yang, J., and Kong, H. (2021\u20135, January 30). A cascaded lidar-camera fusion network for road detection. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561935"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, L., Wu, T., Xiao, Z., Xiao, L., Zhao, D., and Han, J. (2016, January 10\u201312). Multi-cue road boundary detection using stereo vision. Proceedings of the 2016 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Beijing, China.","DOI":"10.1109\/ICVES.2016.7548169"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Oh, M., Jung, E., Lim, H., Song, W., Hu, S., Lee, E.M., Park, J., Kim, J., Lee, J., and Myung, H. (2022). TRAVEL: Traversable Ground and Above-Ground Object Segmentation Using Graph Representation of 3D LiDAR Scans. arXiv.","DOI":"10.1109\/LRA.2022.3182096"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Certad, N., Morales-Alvarez, W., and Olaverri-Monreal, C. (2022, January 14\u201316). Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information. Proceedings of the 2022 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Bogota, Colombia.","DOI":"10.1109\/ICVES56941.2022.9986939"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"29623","DOI":"10.1109\/ACCESS.2019.2902170","article-title":"A 3D LiDAR data-based dedicated road boundary detection algorithm for autonomous vehicles","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1007\/s11633-022-1339-y","article-title":"Yolop: You only look once for panoptic driving perception","volume":"19","author":"Wu","year":"2022","journal-title":"Mach. Intell. Res."},{"key":"ref_43","unstructured":"Vu, D., Ngo, B., and Phan, H. (2022). HybridNets: End-to-End Perception Network. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., and Stachniss, C. (2019, January 3\u20138). Rangenet++: Fast and accurate lidar semantic segmentation. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rummelhard, L., Paigwar, A., N\u00e8gre, A., and Laugier, C. (2017, January 11\u201314). Ground estimation and point cloud segmentation using spatiotemporal conditional random field. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995861"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Horv\u00e1th, E., Pozna, C., and Unger, M. (2021). Real-time LiDAR-based urban road and sidewalk detection for autonomous vehicles. Sensors, 22.","DOI":"10.3390\/s22010194"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MITS.2014.2352371","article-title":"Map-aided evidential grids for driving scene understanding","volume":"7","author":"Kurdej","year":"2015","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Burger, P., Naujoks, B., and Wuensche, H.J. (2019, January 27\u201330). Unstructured road slam using map predictive road tracking. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917129"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Palafox, P.R., Betz, J., Nobis, F., Riedl, K., and Lienkamp, M. (2019). Semanticdepth: Fusing semantic segmentation and monocular depth estimation for enabling autonomous driving in roads without lane lines. Sensors, 19.","DOI":"10.3390\/s19143224"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","unstructured":"Lee, J.H., Han, M.K., Ko, D.W., and Suh, I.H. (2019). From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chao, W.L., Garg, D., Hariharan, B., Campbell, M., and Weinberger, K.Q. (2019, January 15\u201320). Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00864"},{"key":"ref_53","first-page":"1","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man, Cybern."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2021, January 20\u201325). Scaled-yolov4: Scaling cross stage partial network. Proceedings of the IEEE\/cvf Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_58","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning (PMLR), Long Beach, CA, USA."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1177\/0278364906065387","article-title":"The graph SLAM algorithm with applications to large-scale mapping of urban structures","volume":"25","author":"Thrun","year":"2006","journal-title":"Int. J. Robot. Res."},{"key":"ref_60","unstructured":"(2023, July 21). OSM Maps User Stats. Available online: https:\/\/osmstats.neis-one.org\/?item=members."},{"key":"ref_61","unstructured":"(2023, July 21). Overture Maps. Available online: https:\/\/overturemaps.org\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7633\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:45:43Z","timestamp":1760129143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7633"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,3]]},"references-count":61,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177633"],"URL":"https:\/\/doi.org\/10.3390\/s23177633","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,3]]}}}