Driving is an essential activity in today’s busy and complex society, and it demands physical and mental abilities, collectively known as a driving workload. For safe and comfortable driving, it would be useful to detect when drivers are being overloaded. Analyzing driver’s workload using an electroencephalograph (EEG) is useful for this purpose. However, it is very inconvenient to obtain an EEG during actual driving, since the measuring device needs to be attached to the driver. In this paper, we develop a model to predict the driver’s EEG level utilizing basic information obtained while the vehicle is being driven. We divided the EEG values into two classes, “normal” and “overload”, and extracted useful features from the vehicle driving information, such as engine RPM, vehicle speed, lane changes, and turns. A classification model using a support vector machine was built to predict normal and overload states during actual driving. We evaluated the performance of the proposed method using field-of-test data collected when driving on actual roads, and suggest directions for future research based on an analysis of the experimental results.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predicting the EEG Level of a Driver Based on Driving Information


    Beteiligte:
    Kim, Hyun Suk (Autor:in) / Yoon, Daesub (Autor:in) / Shin, Hyun Soon (Autor:in) / Park, Cheong Hee (Autor:in)


    Erscheinungsdatum :

    2019-04-01


    Format / Umfang :

    3183223 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Predicting Driver Takeover Time in Conditionally Automated Driving

    Ayoub, Jackie / Du, Na / Yang, X. Jessie et al. | IEEE | 2022


    PREDICTING A DRIVER IDENTITY FOR UNASSIGNED DRIVING TIME

    DHARA RAGHU / DIMPLE / CHEN CHRIS | Europäisches Patentamt | 2023

    Freier Zugriff

    Predicting driver takeover performance in conditionally automated driving

    Du, Na / Zhou, Feng / Pulver, Elizabeth M. et al. | Elsevier | 2020


    Predicting Driver Intent from Models of Naturalistic Driving

    Bender, Asher / Ward, James R. / Worrall, Stewart et al. | IEEE | 2015


    Method for predicting driving motions in driver assistance system

    DESIES ADRIEN / QUACK LORENZ / MAIER MARKUS et al. | Europäisches Patentamt | 2022

    Freier Zugriff