{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:54:53Z","timestamp":1760144093453,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Technology Innovation Program","doi-asserted-by":"publisher","award":["20013794","P0017033"],"award-info":[{"award-number":["20013794","P0017033"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["20013794","P0017033"],"award-info":[{"award-number":["20013794","P0017033"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous driving recognition technology that can quickly and accurately recognize even small objects must be developed in high-speed situations. This study proposes an object point extraction method using rule-based LiDAR ring data and edge triggers to increase both speed and performance. The LiDAR\u2019s ring information is interpreted as a digital pulse to remove the ground, and object points are extracted by detecting discontinuous edges of the z value aligned with the ring ID and azimuth. A bounding box was simply created using DBSCAN and PCA to check recognition performance from the extracted object points. Verification of the results of removing the ground and extracting points through Ring Edge was conducted using SemanticKITTI and Waymo Open Dataset, and it was confirmed that both F1 scores were superior to RANSAC. In addition, extracting bounding boxes of objects also showed higher PDR index performance when verified in open datasets, virtual driving environments, and actual driving environments.<\/jats:p>","DOI":"10.3390\/s24062005","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T09:13:08Z","timestamp":1711012388000},"page":"2005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Edge-Triggered Three-Dimensional Object Detection Using a LiDAR Ring"],"prefix":"10.3390","volume":"24","author":[{"given":"Eunji","family":"Song","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5397-6152","authenticated-orcid":false,"given":"Seyoung","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-2564","authenticated-orcid":false,"given":"Sung-Ho","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"ref_1","unstructured":"(2021). 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Drive Control"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. (2018, January 18\u201323). Voxelnet: End-to-end learning for point cloud based 3d object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zheng, W., Tang, W., Jiang, L., and Fu, C.W. (2021, January 20\u201325). SE-SSD: Self-Ensembling Single-Stage Object Detector from Point Cloud. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01426"},{"key":"ref_7","first-page":"104","article-title":"Validation of semantic segmentation dataset for autonomous driving","volume":"19","author":"Gwak","year":"2022","journal-title":"J. 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