A Study on Driving Behavior Intelligence Recognition Based on Discrete Wavelet Transform and Support Vector Machine Algorithm
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 driver”s 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.
driving behavior model machine learning discrete wavelet transform support vector machine model optimization
Zhu Xianglei Zhao Shuai Zhang Lu Zhou Bolin Wen Quan Hao Jianye
China Automotive Technology & Research Center Tianjin University
国内会议
上海
英文
159-164
2017-10-24（万方平台首次上网日期，不代表论文的发表时间）