Driver behavior model and its application in driver fatigue identification

doi:10.16265/j.cnki.issn1003-3033.2018.06.008

Abstract

Abstract: It is held by the authors that in order to improve the identification precision of fatigue driving condition,individual difference among drivers should be considered.A RBF neural network based modeling framework is developed to characterize the lane keeping behavior of a driver under normal driving conditions.The effectiveness of the model is analyzed by studying the residuals,i.e.,the differences between the actual and model-predicted driver actions.The model is then used in fatigue driving to predict the hypothetical actions of the driver.The difference between the predicted normal behavior and the actual fatigued behavior gives individual insight into how the driving behavior is affected by fatigue.The residuals were extracted as the input features of a SVM model,which was used to classify normal and fatigued driving conditions for each driver.The validation results show that the average identification accuracy of fatigued driving condition by the proposed model is 85%.

Key words: personalized driving, radial basis function (RBF) neural network, driver behavior modeling, fatigued driving, support vector machine (SVM), field test

CLC Number:

X910

CHU Wenhui, WU Chaozhong, ZHANG Hui, YANG Man , LI Siyao. Driver behavior model and its application in driver fatigue identification[J]. China Safety Science Journal, 2018, 28(6): 43-48.

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