In the stage of automobile industry transition from SAE level “0,1” low autonomous through “2,3,4” human-in-the-loop and ultimately “5” fully autonomous driving, advanced driving monitor system is critical to understand the status, performance, and behavior of drivers for next-generation intelligent vehicles. By making necessary warnings or adjustments, they could operate collaboratively to ensure a safe and efficient traffic environment. The performance and behavior can be viewed as a reflection of the driver’s cognitive workload, which corresponds as well to the environment of their driving scenarios. In this study, image features extracted from driving scenarios, as well as additional environmental features were utilized to classify driving workload levels for different driving scenario video clips. As a continuing study of exploring transfer learning capability, two transfer learning approaches for feature extraction, image segmentation mask transfer approach and image-fixation map overlaid approach were compared and shown comparable results with a 0.910 AUC score and 0.918 AUC score respectively. Environmental information with easy accessibility also shown the effectiveness of contributing to the classification task as additional feature sources.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Extracting Features from Driving Scenarios for Driving Workload Level Classification - A Case Study of Transfer Learning


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Hansen, John (Autor:in) / Liu, Yongkang (Autor:in)

    Kongress:

    SAE WCX Digital Summit ; 2021



    Erscheinungsdatum :

    2021-04-06




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





    GENERATING SIMULATED EDGE-CASE DRIVING SCENARIOS

    ATSMON DAN / SPIEGEL EHUD | Europäisches Patentamt | 2021

    Freier Zugriff

    GENERATING SIMULATED EDGE-CASE DRIVING SCENARIOS

    ATSMON DAN / SPIEGEL EHUD | Europäisches Patentamt | 2023

    Freier Zugriff


    Research on Driving Workload Characteristics of Drivers Under Various Dangerous Scenarios Based on EEG

    Feng, Shumin / Sheng, Bin | British Library Conference Proceedings | 2020