Drivers typically depict different behavior with respect to various driving events. The modeling of their behavior enables an accurate estimation of fuel consumption during the truck design process and is also helpful for ADAS in order to give relevant advices. In this paper, we propose a learning-based approach to the automatic recognition of driving events, e.g., roundabouts or stops, which impact the driver behavior. We first synthesize and categorize meaningful driving events and then study a set of features potentially sensitive to the driver behavior. These features were experimented on real truck driver data using two machine-learning techniques, i.e., decision tree and linear logic regression, to evaluate their relevance and ability to recognize driving events.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Learning-based driving events classification


    Contributors:


    Publication date :

    2013-10-01


    Size :

    402112 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification of Driving Skills based on Machine Learning

    Pathirannehelage, C.N. / International Federation of Automobile Engineers' and Technicians' Associations | British Library Conference Proceedings | 2010


    MACHINE LEARNING MODEL FOR PREDICTING DRIVING EVENTS

    GUTIERREZ THORALF / POTTHAST JAMES / BARANAU ALEX | European Patent Office | 2024

    Free access

    Classification of driving skills based on machine learning

    Naiwala,P.C. / Kazunaria,N. / Ichiro,K. et al. | Automotive engineering | 2010


    MACHINE LEARNING MODEL FOR PREDICTING DRIVING EVENTS

    GUTIERREZ THORALF / POTTHAST JAMES / BARANAU ALEX | European Patent Office | 2023

    Free access