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In order to identify the existence of target vehicle in front of Advanced Driver Assistance System (ADAS) for the extracted ROI, a neural network recognizer of the Radial Basis Function (RBF) is found by extracting a series of parameters on the vehicle\u2019s edge and region features. Using a large amount of collected images, the ROI that may contain vehicles is verified to be effective by extracting composite features of the shadow at the bottom of vehicle and taillights. Based on the positive and negative sample base of vehicles, the neural network recognizer is trained and learned, which can quickly realize network convergence. Furthermore, the vehicle can be effectively identified in the region of interest using the trained network. Test results show that the vehicle detection method based on multifeature extraction and recognition method based on RBF network have stable performance and high recognition accuracy.<\/jats:p>","DOI":"10.1155\/2020\/8842297","type":"journal-article","created":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T01:55:41Z","timestamp":1602035741000},"page":"1-11","source":"Crossref","is-referenced-by-count":10,"title":["Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2794-3987","authenticated-orcid":true,"given":"Xuewen","family":"Chen","sequence":"first","affiliation":[{"name":"College of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaqing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-014-0128-4"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s12239-014-0097-4"},{"issue":"2","key":"3","first-page":"910","article-title":"Lane departure identification for advanced driver assistance","volume":"16","author":"V. 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