Highlights We proposed a metric to evaluate drivers’ degree of visual distraction based on merely face orientation. We proposed a metric to evaluate the take-over performance accurately. An algorithm to set a suitable minimum value of the take-over performance metric was proposed. A highly accurate multiple regression model was built to predict take-over performance. We proposed an adaptive take-over time budget adjustment method for visual-distracted take-over.

    Abstract There are certain situations that automated driving (AD) systems are still unable to handle, preventing the implementation of Level 5 AD. Thus, a transition of control, colloquially known as take-over of the vehicle, is required when the system sends a take-over request (TOR) upon exiting the operational design domain (ODD). An adaptive TOR along with good take-over performance requires adjusting the time budget (TB) to drivers’ visual distraction state, adhering to a reliable visual-distraction-based take-over performance model. Based on a number of driving simulator experiments, the percentage of face orientation to distraction area (PFODA) and time to boundary at take-over timing (TTBT) were proposed to accurately evaluate the degree of visual distraction based on merely face orientation under naturalistic non-driving related tasks (NDRTs) and to evaluate take-over performance, respectively. In order to elucidate the safety boundary, this study also proposed an algorithm to set a suitable minimum value of the TTBT. Finally, a multiple regression model was built to describe the relationship among PFODA, TB and TTBT along with a corrected coefficient of determination of 0.748. Based on the model, this study proposed an adaptive TB adjustment method for the take-over system.


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    Title :

    Drivers’ visual-distracted take-over performance model and its application on adaptive adjustment of time budget


    Contributors:
    Li, Qingkun (author) / Hou, Lian (author) / Wang, Zhenyuan (author) / Wang, Wenjun (author) / Zeng, Chao (author) / Yuan, Quan (author) / Cheng, Bo (author)


    Publication date :

    2021-03-17




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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