We propose a driver identification system that uses deep learning technology with controller area network (CAN) data obtained from a vehicle. The data are collected by sensors that are able to obtain the characteristics of drivers. A convolutional neural network (CNN) is used to learn and identify a driver. Various techniques such as CNN 1D, normalization, special section extracting, and post-processing are applied to improve the accuracy of the identification. The experimental results demonstrate that the proposed system achieves an average accuracy of 90% in an experiment with four drivers. In addition, we simulated real-time driver identification in an actual vehicle. In this experiment, we evaluated the time required to reach certain accuracy. For example, the time required to reach an accuracy of 80% was 4–5 min on average.


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    Title :

    Real-time Driver Identification using Vehicular Big Data and Deep Learning


    Contributors:
    Jeong, Daun (author) / Kim, MinSeok (author) / Kim, KyungTaek (author) / Kim, TaeWang (author) / Jin, JiHun (author) / Lee, ChungSu (author) / Lim, Sejoon (author)


    Publication date :

    2018-11-01


    Size :

    718775 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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