This article presents a driver fatigue recognition model based on a BP neural network that was developed to identify a driver's fatigue level both subjectively and precisely. Fatigue level was classified into the following four levels: alert, mild fatigue, deep fatigue, and drowsiness. Alpha rhythms, beta rhythms, delta rhythms, the mean of heart rate, and standard deviation of R-R intervals were selected as input parameters of the BP neural network and the driver's fatigue level was set as the output parameter. Part of the experimental data, collected from the JiangYan freeway, was used to train the BP neural network model, and the parameters of the model were calibrated. Then residual data was used to test the effectiveness of the model. Results showed that the model can recognize driver fatigue levels with high precision.


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    Title :

    A Driver Fatigue Recognition Model Based on BP Neural Network


    Contributors:
    Li, Shiwu (author) / Wang, Linhong (author) / Yang, Zhifa (author) / Tian, Jingjing (author) / Si, Dongmei (author)

    Conference:

    11th International Conference of Chinese Transportation Professionals (ICCTP) ; 2011 ; Nanjing, China


    Published in:

    ICCTP 2011 ; 2072-2079


    Publication date :

    2011-07-26




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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