{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T09:10:29Z","timestamp":1760346629467,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,7,30]],"date-time":"2017-07-30T00:00:00Z","timestamp":1501372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundations of China","award":["61503362","61305111","61304100","91420104","51405471","61503363"],"award-info":[{"award-number":["61503362","61305111","61304100","91420104","51405471","61503363"]}]},{"name":"National Science Foundation of Anhui Province","award":["1508085MF133","KJ2016A233"],"award-info":[{"award-number":["1508085MF133","KJ2016A233"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Robust and quick road curb detection under various situations is critical in developing intelligent vehicles. However, the road curb detection is easily affected by the obstacles in the road area when Lidar based method is applied. A practical road curb detection method using point cloud from a three-dimensional Lidar for autonomous vehicle is reported in this paper. First, a multi-feature, loose-threshold, varied-scope ground segmentation method is presented to increase the robustness of ground segmentation with which obstacles above the ground can be detected. Second, the road curb is detected by applying the global road trend and an extraction-update mechanism. Experiments show the robustness and efficiency of the road curb detection under various environments. The road curb detection method is 10 times the speed of traditional method and the accuracy is much higher than existing methods.<\/jats:p>","DOI":"10.3390\/info8030093","type":"journal-article","created":{"date-parts":[[2017,8,1]],"date-time":"2017-08-01T03:30:06Z","timestamp":1501558206000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle"],"prefix":"10.3390","volume":"8","author":[{"given":"Rulin","family":"Huang","sequence":"first","affiliation":[{"name":"School of Engineering, Anhui Agriculture University, Hefei 230036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8137-671X","authenticated-orcid":false,"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Engineering, Anhui Agriculture University, Hefei 230036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0472-157X","authenticated-orcid":false,"given":"Biao","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihua","family":"Wu","sequence":"additional","affiliation":[{"name":"Ma\u2019anshan Power Supply Company, Ma\u2019anshan 243000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","article-title":"Pedestrian Detection: An Evaluation of the State of the Art","volume":"34","author":"Dollar","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5258","DOI":"10.1109\/WCICA.2004.1343725","article-title":"Vision based lane detection in autonomous vehicle","volume":"6","author":"Shu","year":"2004","journal-title":"Intell. Control Autom."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/JPROC.2002.801444","article-title":"Artificial vision in road vehicles","volume":"90","author":"Bertozzi","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.ins.2014.10.040","article-title":"A rapid learning algorithm for vehicle classification","volume":"295","author":"Wen","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TITS.2010.2040177","article-title":"A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking","volume":"11","author":"Sivaraman","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Arrospide, J., Salgado, L., and Marinas, J. (2012). HOG-Like Gradient-Based Descriptor for Visual Vehicle Detection. IEEE Intell. Veh. Symp.","DOI":"10.1109\/IVS.2012.6232119"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TPAMI.2006.104","article-title":"On-road vehicle detection: A review","volume":"28","author":"Sun","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, J., Liang, H., Wang, Z., Mei, T., and Song, Y. (2013, January 12\u201314). Robust lane marking detection under different road conditions. Proceedings of the 2013 IEEE International Conference on Robotics and Biomimetics, Shenzhen, China.","DOI":"10.1109\/ROBIO.2013.6739721"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MITS.2014.2336271","article-title":"Three decades of driver assistance systems: Review and future perspectives","volume":"6","author":"Bengler","year":"2014","journal-title":"IEEE Intell. Trans. Syst. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Buehler, M., Iagnemma, K., and Singh, S. (2007). The 2005 DARPA Grand Challenge, Springer.","DOI":"10.1007\/978-3-540-73429-1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Buehler, M., Iagnemma, K., and Singh, S. (2009). The DARPA Urban Challenge, Springer.","DOI":"10.1007\/978-3-642-03991-1"},{"key":"ref_12","unstructured":"(2017, July 22). Baidu Leads China\u2019s Self-Driving Charge in Silicon Valley. Available online: http:\/\/www.reuters.com\/article\/us-autos-baidu-idUSKBN19L1KK."},{"key":"ref_13","unstructured":"(2017, July 22). Ford Promises Fleets of Driverless Cars within Five Years. Available online: https:\/\/www.nytimes.com\/2016\/08\/17\/business\/ford-promises-fleets-of-driverless-cars-within-five-years.html."},{"key":"ref_14","unstructured":"(2017, July 22). What Self-Driving Cars See. Available online: https:\/\/www.nytimes.com\/2017\/05\/25\/automobiles\/wheels\/lidar-self-driving-cars.html."},{"key":"ref_15","unstructured":"(2017, July 22). Uber Engineer Barred from Work on Key Self-Driving Technology, Judge Says. Available online: https:\/\/www.nytimes.com\/2017\/05\/15\/technology\/uber-self-driving-lawsuit-waymo.html."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Brehar, R., Vancea, C., and Nedevschi, S. (2014, January 4\u20136). Pedestrian Detection in Infrared Images Using Aggregated Channel Features. Proceedings of the 2014 IEEE International Conference on Intelligent Computer Communication and Processing, Cluj Napoca, Romania.","DOI":"10.1109\/ICCP.2014.6936964"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1002\/rob.20256","article-title":"Driving with Tentacles: Integral Structures for Sensing and Motion","volume":"25","author":"Himmelsbach","year":"2008","journal-title":"J. Field Robot"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1002\/rob.20255","article-title":"Autonomous Driving in Urban Environments: Boss and the Urban Challenge","volume":"25","author":"Urmson","year":"2008","journal-title":"J. Field Robot"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1002\/rob.20252","article-title":"Team AnnieWAY\u2019s Autonomous System for the 2007 DARPA Urban Challenge","volume":"25","author":"Kammel","year":"2008","journal-title":"J. Field Robot"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1002\/rob.20258","article-title":"Junior: The Stanford Entry in the Urban Challenge","volume":"25","author":"Montemerlo","year":"2008","journal-title":"J. Field Robot"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/rob.20147","article-title":"Stanley: The Robot that Won the DARPA Grand Challenge","volume":"23","author":"Thrun","year":"2006","journal-title":"J. Field Robot"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Moosmann, F., Pink, O., and Stiller, C. (2009, January 3\u20135). Segmentation of 3D Lidar Data in Non-Flat Urban Environments Using a Local Convexity Criterion. Proceedings of the 2009 IEEE Intelligent Vehicles Symposium, Xi\u2019an, China.","DOI":"10.1109\/IVS.2009.5164280"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cheng, J., Xiang, Z., Cao, T., and Liu, J. (June, January 31). Robust vehicle detection using 3D Lidar under complex urban environment. Proceedings of the 2014 IEEE International Conference on Robotics and Automation, Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6906929"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21931","DOI":"10.3390\/s150921931","article-title":"A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment","volume":"15","author":"Liu","year":"2015","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, T., Dai, B., Liu, D., Song, J., and Liu, Z. (July, January 28). Velodyne-based curb detection up to 50 meters away. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium, Seoul, South Korea.","DOI":"10.1109\/IVS.2015.7225693"},{"key":"ref_26","unstructured":"Zhao, G., and Yuan, J. (October, January 30). Curb detection and tracking using 3D-LIDAR scanner. Proceedings of the 19th IEEE International Conference on Image Processing, Orlando, FL, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hata, A.Y., Osorio, F.S., and Wolf, D.F. (2014, January 8\u201311). Robust Curb Detection and Vehicle Localization in Urban Environments. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856405"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, J., Wang, X., Li, C., and Wang, L. (2015, January 21\u201323). A real-time curb detection and tracking method for UGVs by using a 3D-LIDAR sensor. Proceedings of the IEEE Conference on Control Applications, Sydney, Australia.","DOI":"10.1109\/CCA.2015.7320746"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/3\/93\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:34Z","timestamp":1760208274000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/3\/93"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,30]]},"references-count":28,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["info8030093"],"URL":"https:\/\/doi.org\/10.3390\/info8030093","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2017,7,30]]}}}