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In view of the specific problem of lane departure, a lane departure decision-making method is established without calibration relying on the Kalman filtering fuzzy logic algorithm, according to the characteristics of expressway lanes, based on the machine vision and hearing fusion analysis of lane departure, integrating the extraction of the linear lane line model and the region of interest (ROI) in this paper to judge the degree of vehicle departure from the lane by integrating the slope values of the 2 lane lines in the road image. The results show that the system has good lane recognition capabilities and accurate departure decision-making capabilities, and meet the lane departure warning requirements in the expressway environment.<\/jats:p>","DOI":"10.3233\/jifs-189970","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T12:08:04Z","timestamp":1620994084000},"page":"4855-4862","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Kalman filtering fuzzy logic algorithm for recognition of lane departure"],"prefix":"10.1177","volume":"41","author":[{"given":"Kai","family":"Ren","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou, China"}]}],"member":"179","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01508-6"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.9b01428"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.9b01428"},{"issue":"5","key":"e_1_3_1_5_2","first-page":"545","article-title":"Online determination of tool run-out and wear using machine vision and image processing techniques[J]","volume":"17","author":"Makki H.","year":"2019","unstructured":"MakkiH., HeinemannR., HindujaS., et al., Online determination of tool run-out and wear using machine vision and image processing techniques[J], Genes & Development17(5) (2019), 545\u2013580.","journal-title":"Genes & Development"},{"issue":"2","key":"e_1_3_1_6_2","first-page":"132","article-title":"Effect of environmental conditions on performance of image recognition-based lane departure warning system:[J]","volume":"18","author":"Mohammed H.","year":"2018","unstructured":"MohammedH., PrasoonH., et al., Effect of environmental conditions on performance of image recognition-based lane departure warning system:[J], Transportation Research Record18(2) (2018), 132\u2013140.","journal-title":"Transportation Research Record"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2019.0717"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsr.2018.05.006"},{"issue":"8","key":"e_1_3_1_9_2","first-page":"150","article-title":"Lane departure warning algorithm based on probability statistics of driving habits[J]","volume":"3","author":"Zhang J.","year":"2020","unstructured":"ZhangJ., SiJ., YinX., et al., Lane departure warning algorithm based on probability statistics of driving habits[J], Soft Computing3(8) (2020), 150\u2013158.","journal-title":"Soft Computing"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2019.03.002"},{"issue":"4","key":"e_1_3_1_11_2","first-page":"104","article-title":"Machine vision for soil roughness measurement and control of tillage machines during seedbed preparation[J]","volume":"196","author":"Riegler-Nurscher P.","year":"2020","unstructured":"Riegler-NurscherP., MoitziG., PranklJ., et al., Machine vision for soil roughness measurement and control of tillage machines during seedbed preparation[J], Soil and Tillage Research196(4) (2020), 104\u2013110.","journal-title":"Soil and Tillage Research"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/10942912.2020.1720715"},{"issue":"2","key":"e_1_3_1_13_2","first-page":"910","article-title":"Lane departure identification for advanced driver assistance[J]","volume":"16","author":"Gaikwad V.","year":"2015","unstructured":"GaikwadV. and LokhandeS., Lane departure identification for advanced driver assistance[J], IEEE Transactions on Intelligent Transportation Systems16(2) (2015), 910\u2013918.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2018.12.011"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19235332"},{"key":"e_1_3_1_16_2","first-page":"1","article-title":"Vision-based lane departure detection using a stacked sparse autoencoder[J]","volume":"11","author":"Wang Z.","year":"2018","unstructured":"WangZ., WangX., ZhaoL., et al., Vision-based lane departure detection using a stacked sparse autoencoder[J], Mathematical Problems in Engineering11 (2018), 1\u201315.","journal-title":"Mathematical Problems in Engineering"}],"container-title":["Journal of Intelligent &amp; 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