Accidents caused by drivers' drowsiness have a high fatality rate because of the marked decline in the drivers' vehicle control abilities. Preventing accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers. This paper proposes a brain-machine interface that combines electroencephalographic power spectrum estimation, principal component analysis, and fuzzy neural networks to estimate/predict drivers' drowsiness level in a virtual-reality-based driving simulator. The driving performance is defined as deviation between the center of the vehicle and the center of the cruising lane. Our results demonstrated that the proposed method is feasible to accurately estimate quantitatively driving performance in a realistic driving simulator.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    EEG-based fuzzy neural network estimator for driving performance


    Contributors:


    Publication date :

    2004


    Size :

    7 Seiten, 13 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Estimating driving performance based on EEG spectrum and fuzzy neural network

    Wu, Ruei-Cheng / Lin, Chin-Teng / Liang, Sheng-Fu et al. | Tema Archive | 2004


    An iterative control for driving model based on fuzzy neural network

    Tang, Jin-jun / Wang, Guo-zhong / Liu, Fang et al. | Tema Archive | 2010




    Simulation of actual driving operations by using fuzzy and neural network models

    Itakura,N. / Honda,N. / Yikai,K. et al. | Automotive engineering | 1996