This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver's needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Online Prediction of Driver Distraction Based on Brain Activity Patterns




    Erscheinungsdatum :

    2015




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



    Online Prediction of Driver Distraction Based on Brain Activity Patterns

    Wang, Shouyi / Zhang, Yiqi / Wu, Changxu et al. | IEEE | 2015


    Driver distraction

    Kinnear, Neale / Stevens, Alan | ELBA - Bundesanstalt für Straßenwesen (BASt) | 2017

    Freier Zugriff

    Driver distraction

    Skewes, D. | British Library Online Contents | 1997


    Driver distraction determination

    OLSSON CLAES / GONZALEZ PINTOR SEBASTIAN / BRANNLUND OLLE | Europäisches Patentamt | 2020

    Freier Zugriff

    Driver distraction detection

    HERMAN DAVID MICHAEL | Europäisches Patentamt | 2022

    Freier Zugriff