This paper proposes a continuous identification scheme monitoring a driver's behavior and a detection scheme for an impostor. The proposed scheme uses Long Short-Term Memory (LSTM) to create a classifier of the driver's behavior according to acceleration data and classifies a driver. Additionally, it also detects an impostor according to statistical information of output from the classifier. Experimental results show that an acceleration sensor on a real vehicle is enough to classify 15 drivers according to real-time driver's behavior. Additionally, the proposed scheme can detect an impostor through the verification of real experimental data.


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    Titel :

    Proposal for Identification Scheme of Driver and Impostor based on Acceleration Data


    Beteiligte:
    Mori, Yuki (Autor:in) / Ono, Ryota (Autor:in) / Mitani, Takuya (Autor:in) / Naito, Katsuhiro (Autor:in) / Yamazato, Takaya (Autor:in)


    Erscheinungsdatum :

    01.11.2019


    Format / Umfang :

    380039 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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