AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin

Friedrich Lindow, Christian Kaiser, Alexey Kashevnik, Alexander Stocker

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

    Abstract

    Driving a vehicle is an indispensable part of their everyday life for many people. However, sometimes this everyday life does not go as expected, as a lot of accidents happen on the public roads, and most of these accidents are due to inattentive driver behavior. Modern driver monitoring systems evaluate driver behavior by means of distinctive sensor technology and, if necessary, indicate undesirable driving behavior. However, many roadworthy vehicles do not have the possibility to implement such systems. Therefore, it seems to be interesting to investigate the implementation of such systems based on commodity hardware, e.g., smartphones, because nowadays almost every driver has a powerful smartphone equipped with many sensors at hand in the vehicle. Furthermore, recent advances in Machine Learning (ML) made it possible to analyze large amounts of data and to generate new outcomes. In this work we discuss how ML can be used for driver behavior recognition by improving an already existing threshold-based driver monitoring system with different ML-based techniques, Neural Networks and Random Forests, and evaluate their performance. We propose to use Microsoft Azure platform to analyze data generated by a Driver Monitoring System (DMS). Our results indicate ML as a useful technique for learning and adapting threshold-based reasoning about individual drivers' states.

    Original languageEnglish
    Title of host publicationProceedings of the 27th Conference of Open Innovations Association FRUCT, FRUCT 2020
    EditorsSergey Balandin, Luca Turchet, Tatiana Tyutina
    Pages116-125
    Number of pages10
    ISBN (Electronic)978-952-69244-3-4
    DOIs
    Publication statusPublished - 9 Sept 2020
    Event2020 IEEE FRUCT (Finnish-Russian University Cooperation and Telecommunications) Conference: 27th IEEE FRUCT Confernece - Hybrider Event, Italy
    Duration: 7 Sept 20209 Sept 2020
    https://www.fruct.org/

    Conference

    Conference2020 IEEE FRUCT (Finnish-Russian University Cooperation and Telecommunications) Conference
    Abbreviated titleIEEE FRUCT 2020
    Country/TerritoryItaly
    CityHybrider Event
    Period7/09/209/09/20
    Internet address

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • General Computer Science

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