This study carried out a simulator test to determine and predict the duration of reduced driver performance during the automated driving takeover process. Vehicle trajectory and driver behavior data were collected in critical and non-critical takeover scenarios. The earth mover’s distance was then adopted to identify the data with the optimal combination of indicators by comparing it to the reference data. The Gaussian mixture model was employed to classify the driving state as either stable or unstable, and the duration of reduced driver performance was derived for each participant based on these results. Subsequently, a generalized linear mixed model was developed to predict the duration of reduced driver performance and examine the impact of various factors on it. Results uncovered a recovery of the reduced driver performance state after drivers took over the automated vehicle. In the non-critical and critical takeover scenarios, the mean duration of reduced driver performance was 17.48 and 27.25 s, respectively. Additionally, the developed model demonstrated good overall prediction accuracy, with the duration of reduced driver performance showing a positive correlation with the lead vehicle’s speed, duration of automated driving, and takeover request lead time. Furthermore, timid drivers exhibited a longer recovery duration than aggressive drivers. These research findings offer valuable insights into understanding the recovery of reduced driver performance during the takeover process, serving as a theoretical foundation for designing safer automated driving systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predicting the duration of reduced driver performance during the automated driving takeover process


    Weitere Titelangaben:

    C. Wang et al.
    Journal of Intelligent Transportation Systems


    Beteiligte:
    Wang, Changshuai (Autor:in) / Xu, Chengcheng (Autor:in) / Peng, Chang (Autor:in) / Tong, Hao (Autor:in) / Ren, Weilin (Autor:in) / Jiao, Yanli (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    04.03.2025


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Predicting Driver Takeover Time in Conditionally Automated Driving

    Ayoub, Jackie / Du, Na / Yang, X. Jessie et al. | IEEE | 2022


    Predicting Driver Takeover Time in Conditionally Automated Driving

    Ayoub, Jackie / Du, Na / Yang, X. Jessie et al. | ArXiv | 2021

    Freier Zugriff


    Driving takeover apparatus based on driver operation amount and takeover operation

    MIKI YUKI | Europäisches Patentamt | 2024

    Freier Zugriff

    Method for predicting scene awareness level of L3-level driving takeover driver

    GUO FENGXIANG / AI YUXIN / CHEN ZHENG et al. | Europäisches Patentamt | 2025

    Freier Zugriff