All drivers come with a driving signature during a driving. By aggregating adequate driving data of a driver via multiple driving sessions, which is already embedded with driving behaviors of a driver, driver identification task could be treated as a supervised machine learning classification problem. In this paper, we use a random forest classifier to implement the classification task. Therefore, we collected many time series signals from 60 driving sessions (4 sessions per driver and 15 drivers totally) via the Controller Area Network. To reduce the redundancy of information, we proposed a method for signal pre-selection. Besides, we proposed a strategy for parameters tuning, which includes signal refinement, interval feature extraction and selection, and the segmentation of a signal. We also explored the performance of different types of arrangement of features and samples. By following the proposed tuning strategy, the prediction performance of the random forest classifier achieved an accuracy of 89.14% for an identification task of four drivers, and 60.36% for fifteen drivers.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver Identification Using Multivariate In-vehicle Time Series Data


    Additional title:

    Sae Technical Papers


    Contributors:
    Luo, Dawei (author) / Guo, Gang (author) / Lu, Jianbo (author)

    Conference:

    WCX World Congress Experience ; 2018



    Publication date :

    2018-04-03




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Driver Identification Using Multivariate In-vehicle Time Series Data

    Luo, Dawei / Lu, Jianbo / Guo, Gang | British Library Conference Proceedings | 2018


    Driver Identification Using Vehicle Telematics Data

    Wang, Bo / Panigrahi, Smruti / Narsude, Mayur et al. | British Library Conference Proceedings | 2017


    Time-Shifted Transformers for Driver Identification Using Vehicle Data

    Govers, Wim / Yurtman, Aras / Aslandere, Turgay et al. | IEEE | 2024


    Driver Identification Using Vehicle Telematics Data

    Mohanty, Amit / Wang, Bo / Narsude, Mayur et al. | SAE Technical Papers | 2017


    VEHICLE DRIVER IDENTIFICATION

    MYERS SCOTT VINCENT / ELWART SHANE / TALAMONTI WALTER JOSEPH et al. | European Patent Office | 2020

    Free access