This paper presents an innovative approach to classifying the driver's driving style by analyzing the jerk profile of the driver. Driving style is a dynamic behavior of a driver on the road. At times a driver can be calm but aggressive at others. The information about driver's dynamic driving style can be used to better control fuel economy. We propose to classify driver's style based on the measure of how fast a driver is accelerating and decelerating. We developed an algorithm that classifies driver's style utilizing the statistical information from the jerk profile and the road way type and traffic congestion level prediction. Our experiment results show that our approach generates more reasonable results than those generated by using other published methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver's style classification using jerk analysis


    Contributors:


    Publication date :

    2009-03-01


    Size :

    1623447 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Mathematical Model of Skilled Driver's Steering Pattern Based on Minimum Jerk Model

    Kushiro, Ikuo / Suzuki, Keisuke | British Library Online Contents | 2016


    A Hybrid Classification of Driver’s Style and Skill Using Fully-Connected Deep Neural Networks

    Hu, Hongyu / Liu, Jiarui / Hu, Ruofei | SAE Technical Papers | 2021


    A Hybrid Classification of Driver’s Style and Skill Using Fully-Connected Deep Neural Networks

    Liu, Jiarui / Hu, Ruofei / Hu, Hongyu | British Library Conference Proceedings | 2020


    A Hybrid Classification of Driver’s Style and Skill Using Fully-Connected Deep Neural Networks

    Liu, Jiarui / Hu, Ruofei / Hu, Hongyu | British Library Conference Proceedings | 2020


    Classification Driver's Behaviour Using Supervised Algorithm

    Sihakhom, Phounsiri / Sulistyo, Selo / Mustika, I Wayan | IEEE | 2020