Driver distraction represents a road safety issue worldwide. Therefore, a distraction detection system can help improve road safety by warning drivers of non-autonomous vehicles or taking back control in the case of autonomous vehicles. Previous studies primarily relied upon classification-based supervised methods to predict distraction, but these methods require time-consuming annotation and are prone to errors. However, in this work, we propose a novel approach that explores the use of unsupervised anomaly detection techniques including traditional and deep learning methods and an anomaly score as an indicator of the distraction level. Our approach relies only on normal driving data and does not require dangerous driving situations to be collected. Therefore, this approach facilitates the acquisition of large-scale databases under real-world driving conditions. To evaluate its reliability, we use a fully annotated database containing eye tracker's data from drivers in both distracting and non-distracting settings in manual and autonomous driving modes, in a car simulator, allowing for a comparison between supervised and unsupervised models. Our unsupervised approach achieves F1-scores of 0.83 and 0.88 on the autonomous and manual driving datasets, respectively, showing close performance compared to the supervised methods. The positive correlation between anomaly scores and distraction level further demonstrates the effectiveness of our unsupervised approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Driver Visual Distraction Detection Using Unsupervised Learning Techniques


    Beteiligte:


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    725538 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    METHOD FOR FINE-GRAINED DETECTION OF DRIVER DISTRACTION BASED ON UNSUPERVISED LEARNING

    CHEN JIE / LI BING / CHENG ZIHAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff


    DRIVER DISTRACTION DETECTION

    HERMAN DAVID MICHAEL | Europäisches Patentamt | 2021

    Freier Zugriff

    Driver distraction detection

    HERMAN DAVID MICHAEL | Europäisches Patentamt | 2022

    Freier Zugriff

    Transfer Learning-based Driver Distraction Detection

    Goel, Lipika / Chennamaneni, Sukruthi / Golla, Anjali et al. | IEEE | 2023