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原文传递 A Novel Lane-Changing Recognition Method Using Frequency Analysis

题名：	A Novel Lane-Changing Recognition Method Using Frequency Analysis
正文语种：	eng
作者：	Xianjun Hou;Wenbo Li;Bin Zou;Luqi Tang;Kewei Wang;Wenjun Huang
作者单位：	Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology Hubei Research Center for New Energy and Intelligent Connected Vehicle Wuhan Univ. of Technology Wuhan 430070 China;Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology Hubei Research Center for New Energy and Intelligent Connected Vehicle Wuhan Univ. of Technology Wuhan 430070 China;Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology Hubei Research Center for New Energy and Intelligent Connected Vehicle Wuhan Univ. of Technology Wuhan 430070 China;Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology Hubei Research Center for New Energy and Intelligent Connected Vehicle Wuhan Univ. of Technology Wuhan 430070 China;Dongfeng USharing Technology Co. Ltd. No. 28 Chuanjiangchi 2nd Rd. Wuhan 430070 China;Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology Hubei Research Center for New Energy and Intelligent Connected Vehicle Wuhan Univ. of Technology Wuhan 430070 China
关键词：	Lane-changing recognition; Frequency analysis; Highest frequency; Frequency bands; Fusion
摘要：	Lane-changing recognition is an important task for advanced driver assistance systems, but is heavily challenged by poor driving habits, such as turning without using turn signals. To address this problem in this study, a lane-changing recognition method using frequency analysis was proposed. First, highest-frequency-based and frequency-bands-based methods were employed to evaluate the three behaviors of left lane changing, lane keeping, and right lane changing. To improve the recognition accuracy, the two methods were fused according to their classification advantages for different behaviors. The fused method was verified by lateral position data incorporating lane features that were manually extracted and annotated from the Next-Generation Simulation dataset. The frequency analysis framework achieved recognition accuracy of 91.8%, 97.4%, and 99.1% in 2, 1, and 0 s, respectively, before the vehicle crossed the lane line, which were significant improvements over the time-domain analysis methods. The proposed method was also validated by real-world road data with promising results.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	2
页码：	04022146.1-04022146.11