{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T20:01:19Z","timestamp":1760385679824,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,4]],"date-time":"2018-12-04T00:00:00Z","timestamp":1543881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671441","41531177","U1764262"],"award-info":[{"award-number":["41671441","41531177","U1764262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.<\/jats:p>","DOI":"10.3390\/s18124274","type":"journal-article","created":{"date-parts":[[2018,12,5]],"date-time":"2018-12-05T12:22:00Z","timestamp":1544012520000},"page":"4274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Robust Lane-Detection Method for Low-Speed Environments"],"prefix":"10.3390","volume":"18","author":[{"given":"Qingquan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"College of Civil Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6707-6542","authenticated-orcid":false,"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7180-7627","authenticated-orcid":false,"given":"Bijun","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Yuan","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5403-1895","authenticated-orcid":false,"given":"Jinsheng","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"ref_1","unstructured":"Chen, M., Pomerleau, D., and Jochem, T. (1995, January 5\u20139). AURORA: A vision-based roadway departure warning system. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Human Robot Interaction and Cooperative Robots (IROS), Pittsburgh, PA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/83.650851","article-title":"GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection","volume":"7","author":"Bertozzi","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","unstructured":"Bertozzi, M., and Broggi, A. (1996, January 19\u201320). Real-time lane and obstacle detection on the GOLD system. Proceedings of the Intelligent Vehicles Symposium, Tokyo, Japan."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Loce, R.P., Bala, R., and Trivedi, M. (2017). Lane Detection and Tracking Problems in Lane Departure Warning Systems. Computer Vision and Imaging in Intelligent Transportation Systems, John Wiley & Sons, Ltd.","DOI":"10.1002\/9781118971666"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s41064-016-0003-y","article-title":"Efficient Online Segmentation for Sparse 3D Laser Scans","volume":"85","author":"Bogoslavskyi","year":"2017","journal-title":"PFG"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s10846-013-9889-4","article-title":"Gaussian-Process-Based Real-Time Ground Segmentation for Autonomous Land Vehicles","volume":"76","author":"Chen","year":"2014","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_7","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_8","unstructured":"Samples, M., and James, M.R. (2018, December 03). Learning a Real-Time 3D Point Cloud Obstacle Discriminator via Bootstrapping. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.385.6290&rep=rep1&type=pdf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1109\/TITS.2017.2751746","article-title":"Vanishing Point Constrained Lane Detection with a Stereo Camera","volume":"19","author":"Su","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"074005","DOI":"10.1088\/1361-6501\/aac163","article-title":"Real-time stereo vision-based lane detection system","volume":"29","author":"Fan","year":"2018","journal-title":"Meas. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TITS.2016.2586187","article-title":"Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation","volume":"18","author":"Ozgunalp","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TVT.2013.2281199","article-title":"A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios","volume":"63","author":"Li","year":"2014","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2017.08.014","article-title":"A Review of Recent Advances in Lane Detection and Departure Warning System","volume":"73","author":"Narote","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.apergo.2016.08.010","article-title":"Influence of lane departure warnings onset and reliability on car drivers\u2019 behaviors","volume":"59","author":"Navarro","year":"2017","journal-title":"Appl. Ergon."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Veit, T., Tarel, J.P., Nicolle, P., and Charbonnier, P. (2008, January 12\u201315). Evaluation of Road Marking Feature Extraction. Proceedings of the International IEEE Conference on Intelligent Transportation Systems, Beijing, China.","DOI":"10.1109\/ITSC.2008.4732564"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TITS.2004.838220","article-title":"Springrobot: A prototype autonomous vehicle and its algorithms for lane detection","volume":"5","author":"Li","year":"2004","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Daigavane, P.M., and Bajaj, P.R. (2010, January 19\u201321). Road Lane Detection with Improved Canny Edges Using Ant Colony Optimization. Proceedings of the International Conference on Emerging Trends in Engineering and Technology, Goa, India.","DOI":"10.1109\/ICETET.2010.128"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, S., Jiang, Y., Xi, J., and Gong, J. (2010, January 21\u201324). A novel lane detection based on geometrical model and Gabor filter. Proceedings of the Intelligent Vehicles Symposium, San Diego, CA, USA.","DOI":"10.1109\/IVS.2010.5548087"},{"key":"ref_19","unstructured":"Andrade, D.C., Bueno, F., Franco, F.R., Silva, R.A., Neme, J.H.Z., Margraf, E., Omoto, W.T., Farinelli, F.A., Tusset, A.M., and Okida, S. (2018). A Novel Strategy for Road Lane Detection and Tracking Based on a Vehicle\u2019s Forward Monocular Camera. IEEE Trans. Intell. Transp. Syst., 1\u201311."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kuk, J.G., An, J.H., Ki, H., and Cho, N.I. (2010, January 19\u201322). Fast lane detection & tracking based on Hough transform with reduced memory requirement. Proceedings of the International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.","DOI":"10.1109\/ITSC.2010.5625121"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Saudi, A., Teo, J., Hijazi, M.H.A., and Sulaiman, J. (2008, January 26\u201328). Fast lane detection with Randomized Hough Transform. Proceedings of the International Symposium on Information Technology, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ITSIM.2008.4631879"},{"key":"ref_22","unstructured":"Yu, B., and Jain, A.K. (1997, January 26\u201329). Lane boundary detection using a multiresolution Hough transform. Proceedings of the International Conference on Image Processing, Santa Barbara, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TPAMI.2008.300","article-title":"LSD: A Fast Line Segment Detector with a False Detection Control","volume":"32","author":"Jakubowicz","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1016\/j.patrec.2011.06.001","article-title":"EDLines: A real-time line segment detector with a false detection control","volume":"32","author":"Akinlar","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Lee, S., Zhang, G., Lim, J., Chung, W.K., and Suh, I.H. (June, January 31). Outdoor place recognition in urban environments using straight lines. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907675"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"K\u00fc\u00e7\u00fckmanisa, A., Tar\u0131m, G., and Urhan, O. (2017). Real-time illumination and shadow invariant lane detection on mobile platform. J. Real-Time Image Process., 1\u201314.","DOI":"10.1007\/s11554-017-0687-2"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1016\/j.eswa.2014.10.024","article-title":"Real-time illumination invariant lane detection for lane departure warning system","volume":"42","author":"Son","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_28","unstructured":"Wang, Z., Ren, W., and Qiu, Q. (arXiv, 2018). LaneNet: Real-Time Lane Detection Networks for Autonomous Driving, arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hoang, T.M., Na, R.B., Cho, S.W., Kim, K.W., and Kang, R.P. (2017). Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor. Sensors, 17.","DOI":"10.3390\/s17112475"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wedel, A., Franke, U., Badino, H., and Cremers, D. (2008, January 4\u20136). B-spline modeling of road surfaces for freespace estimation. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621254"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.imavis.2003.10.003","article-title":"Lane detection and tracking using B-Snake","volume":"22","author":"Wang","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/S0167-8655(00)00021-0","article-title":"Lane detection using spline model","volume":"21","author":"Wang","year":"2000","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Watanabe, A., Naito, T., and Ninomiya, Y. (2009, January 3\u20135). Lane detection with roadside structure using on-board monocular camera. Proceedings of the Intelligent Vehicles Symposium, Xi\u2019an, China.","DOI":"10.1109\/IVS.2009.5164276"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Revilloud, M., Gruyer, D., and Rahal, M. (2016, January 16\u201321). A new multi-agent approach for lane detection and tracking. Proceedings of the IEEE International Conference on Robotics and Automation, Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487482"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.comcom.2015.08.010","article-title":"A real-time lane marking localization, tracking and communication system","volume":"73","author":"Mammeri","year":"2016","journal-title":"Comput. Commun."},{"key":"ref_36","first-page":"567","article-title":"Lane detection algorithm based on extended Kalman filter","volume":"26","author":"Hong","year":"2015","journal-title":"J. Optoelectron. Laser"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lee, C., and Moon, J. (2018). Robust Lane Detection and Tracking for Real-Time Applications. IEEE Trans. Intell. Transp. Syst., 1\u20136.","DOI":"10.1109\/TITS.2018.2791572"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11554-012-0315-0","article-title":"Real-time lane tracking using Rao-Blackwellized particle filter","volume":"11","author":"Nieto","year":"2016","journal-title":"J. Real-Time Image Proc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Aly, M. (2008, January 4\u20136). Real time detection of lane markers in urban streets. Proceedings of the Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621152"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2015.12.010","article-title":"Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting","volume":"59","author":"Niu","year":"2016","journal-title":"Pattern Recognit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4274\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:31:12Z","timestamp":1760196672000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,4]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["s18124274"],"URL":"https:\/\/doi.org\/10.3390\/s18124274","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,12,4]]}}}