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In this paper, we proposed a method and designed a learning-based detection model of lane-change behaviour in highway environment, which only needs the vehicle to be equipped with velocity and direction sensors or each section of the highway to have a video camera. First, based on the Next Generation Simulation (NGSIM) Interstate 80 Freeway Dataset, we analyzed the relevant features of lane-changing behaviour and preprocessed the data and then used machine learning algorithms to select the suitable features for lane-change detection. According to the result of feature selection, we chose the lateral velocity of the vehicle as the lane-change feature and used machine learning algorithms to learn the lane-change behaviour of the vehicle to detect it. From the dataset, continuous data of 14 vehicles with frequent lane changes were selected for experimental analysis. The experimental results show that the designed KNN lane-change detection model has the best performance with detection accuracy between 89.57% and 100% on the selected dataset, which can well complete the vehicle lane-change detection task.<\/jats:p>","DOI":"10.1155\/2020\/8848363","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T01:35:36Z","timestamp":1601602536000},"page":"1-13","source":"Crossref","is-referenced-by-count":9,"title":["Learning-Based Lane-Change Behaviour Detection for Intelligent and Connected Vehicles"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9455-3241","authenticated-orcid":true,"given":"Luyao","family":"Du","sequence":"first","affiliation":[{"name":"School of Automation, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7224-1095","authenticated-orcid":true,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5279-9692","authenticated-orcid":true,"given":"Zhonghui","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjiang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Technology Research Center for Intelligent and Connected Vehicle Terminals, Shanghai 200030, China"},{"name":"Shanghai PATEO Electronic Equipment Manufacturing Co., Ltd., Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaizhi","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2567-5261","authenticated-orcid":true,"given":"Di","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"1","key":"1","first-page":"1","article-title":"State-of-the-art and technical trends of intelligent and connected vehicles","volume":"8","author":"K. 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