In this paper, a completely non-intrusive method of monitoring driver drowsiness is described. Because of their abilities to learn behavior and represent very complex relationships, artificial neural networks are the basis of the method presented. Four artificial neural networks are designed based on the hypothesis that the time derivative of force (jerk) exerted by the driver at the steering wheel and accelerator pedal can be used to discern levels of alertness. The artificial neural networks are trained to replicate non-drowsy input, and then tested with unseen data. Data sets that are similar to the training sets will pass through the network with little change, and sets that are different will be changed considerably by the network. Thus, the further the driver's jerk profile deviates from the non-drowsy jerk profile, the greater the error between the input and output of the network will be. The changes in network error with drive time are presented from testing the networks with simulated driving data and the performance of the artificial neural network designs are compared.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Non-Intrusive Driver Drowsiness Monitoring Via Artificial Neural Networks


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Culp, J. (Autor:in) / El-Gindy, M. (Autor:in) / Haque, M. A. (Autor:in)

    Kongress:

    SAE World Congress & Exhibition ; 2008



    Erscheinungsdatum :

    2008-04-14




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Non-intrusive driver drowsiness monitoring via artificial neural networks

    Culp,J. / El-Gindy,M. / Haque,M.A. et al. | Kraftfahrwesen | 2008


    Non-Intrusive Driver Drowsiness Monitoring Via Artificial Neural Networks

    Culp, J. / El-Gindy, M. / Haque, M.A. et al. | British Library Conference Proceedings | 2008


    2008-01-0187 Non-Intrusive Driver Drowsiness Monitoring Via Artificial Neural Networks

    Culp, J. / El-Gindy, M. / Haque, M.A. et al. | British Library Conference Proceedings | 2008


    Driver Drowsiness Monitoring using Convolutional Neural Networks

    Victoria, D. Rosy Salomi / Mary, D. Glory Ratna | IEEE | 2021


    Driver Drowsiness Detection Using Convolution Neural Networks

    Ravi Teja, P. / Anjana Gowri, G. / Preethi Lalithya, G. et al. | Springer Verlag | 2021