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  • 1.
    Henrik, Thoreson
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Robin, Wesslund
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Naive Bayes-klassificering av förarbeteende2017Independent thesis Basic level (degree of Bachelor), 10,5 credits / 16 HE creditsStudent thesis
    Abstract [en]

    To be able to classify a driving style implies that you classify a driving behaviour, which is the foundation of safety and environmental driving classification.

    In this thesis we have let two drivers drive a car in an attempt to classify, with a desired accuracy of around 90%, which one of us drove the car. This was done by exclusively using speed and rpm data values provided from the OBD:II port of the car via the CAN-bus. We approched this problem like you would a text classification one, thus using two common models of Naive Bayes — Multinominal and Gaussian Naive Bayes together with N-gram and discretization.

    We found that using Multinominal Naive Bayes consisting of 4-gram resulted in an avarage accuracy of 91.48% in predicting the driver, non-discretized speed and discretized rpm values.

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