This paper aims to investigate the physio-emotional state of the driver in the vehicle cabin using a multimodal approach, comprising context, motion, visual, and audio data, collected beforehand. Driver behavior monitoring is implemented upon the data gained from different types of sensors, including accelerometer, magnetometer, gyroscope, GPS, front-facing camera, microphone, and information retrieved from third-party services. This data is intended to fully describe driver behavior and aid advanced driver assistant systems to fully classify and recognize dangerous driving behavior, and generate alerts on how to eliminate emergency situations. The emotional state of the driver is determined as six basic emotions, including sadness, fear, disgust, anger, surprise, and happiness. This work eventually presents driving style classification, dividing drivers into three groups: normal, ecological, urban, risky, and aggressive driving. This classification may potentially recognize aggressive vehicle drivers on public roads, and, therefore, undertake measures to reduce the risk of traffic accident occurrence.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Multimodal Approach to Psycho-Emotional State Detection of a Vehicle Driver


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Arai, Kohei (Herausgeber:in) / Lashkov, Igor (Autor:in) / Kashevnik, Alexey (Autor:in)

    Kongress:

    Proceedings of SAI Intelligent Systems Conference ; 2021 ; Amsterdam September 02, 2021 - September 03, 2021



    Erscheinungsdatum :

    2021-08-03


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Psycho-physical tests in driver selection

    Engineering Index Backfile | 1946


    Multimodal Detection of Driver Distraction

    M. Eskenazi / L. Morency / A. W. Black et al. | NTIS | 2017


    Electroencephalogram-Based Driver Emotional State Detection with Manifold Learning

    Zhang, Wenqi / Qin, Yanjun / Zhang, Shanghang et al. | IEEE | 2023


    Multimodal driver state modeling through unsupervised learning

    Tavakoli, Arash / Heydarian, Arsalan | Elsevier | 2022


    Emotional control system and method for vehicle driver

    JANG JI HOON / LEE CHANG KI | Europäisches Patentamt | 2021

    Freier Zugriff