{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:24:44Z","timestamp":1760243084684,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,7,10]],"date-time":"2015-07-10T00:00:00Z","timestamp":1436486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ph.D. Programs Foundation of Ministry of Education of China","award":["20112125120004"],"award-info":[{"award-number":["20112125120004"]}]},{"name":"Humanities and Social Sciences Foundation of the Ministry of Education in China","award":["12YJCZH280"],"award-info":[{"award-number":["12YJCZH280"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.<\/jats:p>","DOI":"10.3390\/info6030339","type":"journal-article","created":{"date-parts":[[2015,7,13]],"date-time":"2015-07-13T03:49:22Z","timestamp":1436759362000},"page":"339-360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems"],"prefix":"10.3390","volume":"6","author":[{"given":"Longhui","family":"Gang","sequence":"first","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiudong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,10]]},"reference":[{"key":"ref_1","unstructured":"Dagan, E., Mano, O., Stein, G.P., and Shashua, A. (2004, January 14\u201317). Forward collision warning with a single camera. Proceedings of the 2004 IEEE Intelligent Vehicles Symposium, Parma, Italy."},{"key":"ref_2","unstructured":"Park, S.J., Kim, T.Y., Kang, S.M., and Koo, K.H. (2003, January 8\u201313). A novel signal processing technique for vehicle detection radar. Proceedings of the 2003 IEEE MTT-S International Microwave Symposium Digest, Philadelphia, PA, USA."},{"key":"ref_3","unstructured":"Wang, C.C., Thorpe, C., and Suppe, A. (2003, January 9\u201311). Ladar-based detection and tracking of moving objects from a ground vehicle at high speeds. Proceedings of the 2003 IEEE Intelligent Vehicles Symposium, Columbus, OH, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Baek, Y.M., and Kim, W.Y. (2014). Forward vehicle detection using cluster-based adaboost. Opt. Eng., 53.","DOI":"10.1117\/1.OE.53.10.102103"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5483","DOI":"10.1016\/j.proeng.2011.08.1017","article-title":"Algorithm research on moving vehicles detection","volume":"15","author":"Zhan","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Aytekin, B., and Altug, E. (2010, January 10\u201313). Increasing driving safety with a multiple vehicle detection and tracking system using ongoing vehicle shadow information. Proceedings of the 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), Istanbul, Turkey.","DOI":"10.1109\/ICSMC.2010.5641879"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TITS.2013.2266661","article-title":"Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis","volume":"14","author":"Sivaraman","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","unstructured":"Ming, Q., and Jo, K.-H. (2011, January 22\u201324). Vehicle detection using tail light segmentation. Proceeding of the 6th International Forum on Strategic Technology (IFOST), Harbin, China."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TITS.2010.2045375","article-title":"Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions","volume":"11","author":"Jones","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1109\/TITS.2012.2186128","article-title":"Detection and classification of vehicles from video using multiple time-spatial images","volume":"13","author":"Mithun","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1016\/j.proeng.2012.01.376","article-title":"Three-frame difference algorithm research based on mathematical morphology","volume":"29","author":"Zhang","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1016\/j.eswa.2010.07.083","article-title":"A background subtraction algorithm for detecting and tracking vehicles","volume":"38","author":"Mandellos","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_14","first-page":"1309","article-title":"Improved support vector machine regression in multi-step-ahead prediction for rock displacement surrounding a tunnel","volume":"21","author":"Yao","year":"2014","journal-title":"Sci. Iran."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yao, B., Yu, B., Hu, P., Gao, J., and Zhang, M. (2015). An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot. Ann. Oper. Res.","DOI":"10.1007\/s10479-015-1792-x"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1109\/TNN.2003.820556","article-title":"Support vector machine with adaptive parameters in financial time series forecasting","volume":"14","author":"Cao","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"397","DOI":"10.2478\/amcs-2014-0030","article-title":"A support vector machine with the tabu search algorithm for freeway incident detection","volume":"24","author":"Yao","year":"2014","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, X., Duan, B., Yuan, H., and Wang, N. (2007, January 13\u201315). Rear vehicle detection and tracking for lane change assist. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey.","DOI":"10.1109\/IVS.2007.4290123"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1049\/iet-ipr.2010.0078","article-title":"Modified two-dimensional otsu image segmentation algorithm and fast realisation","volume":"6","author":"Chen","year":"2012","journal-title":"IET Image Process."},{"key":"ref_20","first-page":"292","article-title":"Research on lane detection based on improved hough transform","volume":"18","author":"Yang","year":"2010","journal-title":"Comput. Meas. Control"},{"key":"ref_21","unstructured":"Le Cam, L.M., and Neyman, J. (1976). Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TIT.1962.1057692","article-title":"Visual pattern recognition by moment invariants","volume":"8","author":"Hu","year":"1962","journal-title":"IRE Trans. Inf. Theory"},{"key":"ref_23","first-page":"555","article-title":"Scale invariance of discrete moment","volume":"23","author":"Han","year":"2008","journal-title":"J. Data Acquis. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/0167-8655(94)90092-2","article-title":"Affine moment invariants: A new tool for character recognition","volume":"15","author":"Flusser","year":"1994","journal-title":"Pattern Recog. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mridula, J., Kumar, K., and Patra, D. (2011, January 24\u201325). Combining glcm features and markov random field model for colour textured image segmentation. Proceedings of the 2011 International Conference on Devices and Communications (ICDeCom), Mesra, India.","DOI":"10.1109\/ICDECOM.2011.5738494"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_27","unstructured":"Hsu, C.-W., Chang, C.-C., and Lin, C.-J. (2003). A Practical Guide to Support Vector Classication, National Taiwan University."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.engappai.2006.06.015","article-title":"Optimizing the distribution of shopping centers with parallel genetic algorithm","volume":"20","author":"Yu","year":"2007","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/6\/3\/339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:49:02Z","timestamp":1760215742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/6\/3\/339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7,10]]},"references-count":28,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2015,9]]}},"alternative-id":["info6030339"],"URL":"https:\/\/doi.org\/10.3390\/info6030339","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2015,7,10]]}}}