{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:30:12Z","timestamp":1779917412188,"version":"3.53.1"},"reference-count":32,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["N0002428"],"award-info":[{"award-number":["N0002428"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Science and ICT","award":["IITP-2021-2020-0-01462"],"award-info":[{"award-number":["IITP-2021-2020-0-01462"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Although numerous road segmentation studies have utilized vision data, obtaining robust classification is still challenging due to vision sensor noise and target object deformation. Long-distance images are still problematic because of blur and low resolution, and these features make distinguishing roads from objects difficult. This study utilizes light detection and ranging (LiDAR), which generates information that camera images lack, such as distance, height, and intensity, as a reliable supplement to address this problem. In contrast to conventional approaches, additional domain transformation to a bird\u2019s eye view space is executed to obtain long-range data with resolutions comparable to those of short-range data. This study proposes a convolutional neural network architecture that processes data transformed to a bird\u2019s eye view plane. The network\u2019s pathways are split into two parts to resolve calibration errors in the transformed image and point cloud. The network, which has modules that operate sequentially at various scaled dilated convolution rates, is designed to quickly and accurately handle a wide range of data. Comprehensive empirical studies using the Karlsruhe Institute of Technology and Toyota Technological Institute\u2019s (KITTI\u2019s) road detection benchmarks demonstrate that this study\u2019s approach takes advantage of camera and LiDAR information, achieving robust road detection with short runtimes. Our result ranks 22nd in the KITTI\u2019s leaderboard and shows real-time performance.<\/jats:p>","DOI":"10.3390\/s21227623","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T09:16:11Z","timestamp":1637140571000},"page":"7623","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Free Space Detection Using Camera-LiDAR Fusion in a Bird\u2019s Eye View Plane"],"prefix":"10.3390","volume":"21","author":[{"given":"Byeongjun","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Smart Car Engineering, Chungbuk National University, Cheongju 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9418-3973","authenticated-orcid":false,"given":"Dongkyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Smart Car Engineering, Chungbuk National University, Cheongju 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Seol","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Control and Robot Engineering, Chungbuk National University, Cheongju 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seok-Cheol","family":"Kee","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems & Robotics, Chungbuk National University, Cheongju 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., and Hajishirzi, H. 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