{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:01:16Z","timestamp":1787313676966,"version":"build-2736575974"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T00:00:00Z","timestamp":1554163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nature Science Foundation of Shandong Province","award":["ZR2016EEQ06"],"award-info":[{"award-number":["ZR2016EEQ06"]}]},{"name":"Tsinghua University Initiative Scientific Research Program","award":["20161080033"],"award-info":[{"award-number":["20161080033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The curvature of the lane output by the vision sensor caused by shadows, changes in lighting and line breaking jumps over in a period of time, which leads to serious problems for unmanned driving control. It is particularly important to predict or compensate the real lane in real-time during sensor jumps. This paper presents a lane compensation method based on multi-sensor fusion of global positioning system (GPS), inertial measurement unit (IMU) and vision sensors. In order to compensate the lane, the cubic polynomial function of the longitudinal distance is selected as the lane model. In this method, a Kalman filter is used to estimate vehicle velocity and yaw angle by GPS and IMU measurements, and a vehicle kinematics model is established to describe vehicle motion. It uses the geometric relationship between vehicle and relative lane motion at the current moment to solve the coefficient of the lane polynomial at the next moment. The simulation and vehicle test results show that the prediction information can compensate for the failure of the vision sensor, and has good real-time, robustness and accuracy.<\/jats:p>","DOI":"10.3390\/s19071584","type":"journal-article","created":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T03:39:28Z","timestamp":1554262768000},"page":"1584","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Research on Lane a Compensation Method Based on Multi-Sensor Fusion"],"prefix":"10.3390","volume":"19","author":[{"given":"Yushan","family":"Li","sequence":"first","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5242-8119","authenticated-orcid":false,"given":"Xuewu","family":"Ji","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanxiang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,2]]},"reference":[{"key":"ref_1","unstructured":"Chinese Society for Artificial Intelligence (2017). China Artificial Intelligence Series White Paper Intelligent Driving, Chinese Society for Artificial Intelligence."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/TITS.2015.2464253","article-title":"Efficient Lane Detection Based on Spatiotemporal Images","volume":"17","author":"Jung","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/TITS.2013.2246835","article-title":"Integrated lane and vehicle detection, localization, and tracking: A synergistic approach","volume":"14","author":"Sivaraman","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Low, C.Y., Zamzuri, H., and Mazlan, S.A. (2014, January 3\u20135). Simple robust road lane detection algorithm. Proceedings of the 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIAS.2014.6869550"},{"key":"ref_5","unstructured":"Tan, T., Yin, S., and Quyang, P. (2015, January 9\u201312). Efficient lane detection system based on monocular camera. Proceedings of the 2015 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA."},{"key":"ref_6","unstructured":"Chen, Q., and Wang, H. (2006, January 13\u201315). A real-time lane detection algorithm based on a hyperbola-pair model. Proceedings of the Intelligent Vehicles Symposium, Tokyo, Japan."},{"key":"ref_7","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_8","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_9","unstructured":"Chiu, K.Y., and Lin, S.F. (2005, January 6\u20138). Lane detection using color-based segmentation. Proceedings of the Intelligent Vehicles Symposium, Las Vegas, NV, USA."},{"key":"ref_10","unstructured":"Ma, C., Mao, L., and Zhang, Y.F. (2010, January 28\u201330). Lane detection using heuristic search methods based on color clustering. Proceedings of the 2010 International Conference on Communications, Circuits and Systems (ICCCAS), Chengdu, China."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, G., Li, S., and Liu, W. (2013, January 7\u20138). Lane detection algorithm based on local feature extraction. Proceedings of the Chinese Automation Congress (CAC), Changsha, China.","DOI":"10.1109\/CAC.2013.6775702"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1016\/j.patcog.2014.02.004","article-title":"Lane-mark extraction for automobiles under complex conditions","volume":"47","author":"Wu","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"188","DOI":"10.3807\/JOSK.2013.17.2.188","article-title":"Lane detection algorithm for night-time digital image based on distribution feature of boundary pixels","volume":"17","author":"You","year":"2013","journal-title":"J. Opt. Soc. Korea"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00138-015-0735-5","article-title":"Vision-based approach towards lane line detection and vehicle localization","volume":"27","author":"Du","year":"2016","journal-title":"Mach. Vis. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hoang, T.M., Baek, N.R., Cho, S.W., Kim, K.W., and Park, K.R. (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_16","first-page":"372","article-title":"Lane Detection Under Vehicles Disturbance","volume":"29","author":"Lei","year":"2007","journal-title":"Automot. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TITS.2007.908582","article-title":"Robust lane detection and tracking in challenging scenarios","volume":"9","author":"Kim","year":"2008","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Assidiq, A.A.M., Khalifa, O.O., Islam, R., and Khan, S. (2008, January 13\u201315). Real time lane detection for autonomous vehicles. Proceedings of the International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICCCE.2008.4580573"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Satzoda, R.K., Sathyanarayana, S., and Srikanthan, T. (2012, January 3\u20137). Robust extraction of lane markings using gradient angle histograms and directional signed edges. Proceedings of the Intelligent Vehicles Symposlum, Madrid, Spain.","DOI":"10.1109\/IVS.2012.6232296"},{"key":"ref_20","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."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3270","DOI":"10.3390\/s130303270","article-title":"Robust lane sensing and departure warning under shadows and occlusions","volume":"13","year":"2013","journal-title":"Sensors"},{"key":"ref_22","first-page":"1","article-title":"Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments","volume":"18","author":"Suhr","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"30199","DOI":"10.3390\/s151229795","article-title":"Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment","volume":"12","author":"Gu","year":"2015","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kang, C., Lee, S.H., and Chung, C.C. (2015, January 15\u201318). On-road vehicle localization with GPS under long term failure of a vision sensor. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), Gran Canaria, Spain.","DOI":"10.1109\/ITSC.2015.266"},{"key":"ref_25","first-page":"3","article-title":"Fusion of Vision, GPS and 3D Gyro Data in Solving Camera Registration Problem for Direct Visual Navigation","volume":"4","author":"Hu","year":"2006","journal-title":"Int. J. ITS Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tao, Z., Bonnifait, P., and Fr\u00e9mont, V. (2013, January 3\u20137). Mapping and localization using GPS, lane markings and proprioceptive sensors. Proceedings of the IEEE RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696383"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jung, J., and Bae, S.H. (2018). Real-Time Road Lane Detection in Urban Areas Using LiDAR Data. Electronics, 7.","DOI":"10.3390\/electronics7110276"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vivacqua, R., Vassallo, R., and Martins, F. (2017). A low cost sensors approach for accurate vehicle localization and autonomous driving application. Sensors, 17.","DOI":"10.3390\/s17102359"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Meng, X., Wang, H., and Liu, B. (2017). A robust vehicle localization approach based on gnss\/imu\/dmi\/lidar sensor fusion for autonomous vehicles. Sensors, 17.","DOI":"10.3390\/s17092140"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lee, G.I., Kang, C.M., and Lee, S.H. (2018, January 16\u201319). Multi object-based predictive virtual lane. Proceedings of the IEEE International Conference on Intelligent Transportation Systems, Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317688"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Song, M., Kim, C., Kim, M., and Yi, K. (2018). Robust lane tracking algorithm for forward target detection of automated driving vehicles. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.","DOI":"10.1177\/0954407018785484"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kang, C.M., Lee, S.H., and Chung, C.C. (2014, January 8\u201311). Lane estimation using a vehicle kinematic lateral motion model under clothoidal road constraints. Proceedings of the 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957829"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3378","DOI":"10.1109\/TVT.2014.2356204","article-title":"Robust multirate control scheme with predictive virtual lanes for lane-keeping system of autonomous highway driving","volume":"64","author":"Son","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1007\/s12555-017-0449-8","article-title":"Kinematics-based Fault-tolerant Techniques: Lane Prediction for an Autonomous Lane Keeping System","volume":"16","author":"Kang","year":"2018","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s12555-013-0276-5","article-title":"Predictive virtual lane method using relative motions between a vehicle and lanes","volume":"13","author":"Son","year":"2015","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TCST.2016.2562607","article-title":"Robust multirate on-road vehicle localization for autonomous highway driving vehicles","volume":"25","author":"Lee","year":"2017","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_37","unstructured":"Adrian, Z., and Marc, W. (2011, January 12). Control algorithm for hands-off lane centering on motorways. Proceedings of the Aachen Colloquium Automobile and Engine Technology, Aachen, Germany."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1177\/0954407014547240","article-title":"A new robust control method for active front steering considering the intention of the driver","volume":"229","author":"Ji","year":"2015","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wu, J., Cheng, S., Liu, B., and Liu, C. (2017). A Human-Machine-Cooperative-Driving Controller Based on AFS and DYC for Vehicle Dynamic Stability. Energies, 10.","DOI":"10.3390\/en10111737"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6906","DOI":"10.1109\/TVT.2018.2834381","article-title":"Interactive Control Paradigm based Robust Lateral Stability Controller Design for Autonomous Automobile Path Tracking with Uncertain Disturbance: A Dynamic Game Approach","volume":"67","author":"Ji","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/7\/1584\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:42:14Z","timestamp":1760186534000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/7\/1584"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,2]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["s19071584"],"URL":"https:\/\/doi.org\/10.3390\/s19071584","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,2]]}}}