【摘 要】
：
With the rapid development of Artificial Intelligence,big data analysis,smart recognition of driving behaviors becomes the new focus of Intelligence and Connected Vehicles researches.The state-of-the-
【机 构】
：
China Automotive Technology & Research Center Tia
【出 处】
：
第19届亚太汽车工程年会暨2017中国汽车工程学会年会
论文部分内容阅读
With the rapid development of Artificial Intelligence,big data analysis,smart recognition of driving behaviors becomes the new focus of Intelligence and Connected Vehicles researches.The state-of-the-art research in this direction is to recognize driving scenes to support driving decisions,based on drivers driving data.This study used CATARC collected Controller Area Network(CAN)bus and CARTAC driving scene standard labeled data,implemented discrete wavelet transform(DWT)and support vector machine(SVM)algorithm,constructed a machine learning model with the ability of detecting 16 different driving behaviors.With details of fea-ture selection,filter selection,SVC parameters selection and many others techniques to optimize the model,achieved cross validation accuracy rate around 88%.This method can be applied to vehicles security warnings and intelligence control,therefore to improve ve-hicle safety performance.
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