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.

在某些情况下，自动驾驶（AD）系统仍然无法处理，从而导致无法实施Level 5 AD。因此，当系统在退出运营设计域（ODD）时发送接管请求（TOR）时，就需要进行控制转换，俗称车辆的接管。具有良好接管性能的自适应TOR要求根据驾驶员的视觉分散状态调整时间预算（TB），并遵守可靠的基于视觉分散的接管性能模型。根据许多驾驶模拟器实验，提出了面部定向相对于分散注意力区域的百分比（PFODA）和接管时机到达边界的时间（TTBT），以仅基于自然非驾驶相关任务（NDRT）下的面部定向来准确评估视觉分散的程度，并且分别评估接管绩效。为了阐明安全边界，本研究还提出了一种算法来设置合适的TTBT最小值。最后，建立了多元回归模型来描述PFODA，TB和TTBT之间的关系以及校正后的0.748的确定系数。基于该模型，本研究提出了一种适用于接管系统的自适应结核病调整方法。这项研究还提出了一种算法，以设置适当的TTBT最小值。最后，建立了多元回归模型来描述PFODA，TB和TTBT之间的关系以及校正后的0.748的确定系数。基于该模型，本研究提出了一种适用于接管系统的自适应结核病调整方法。这项研究还提出了一种算法，以设置适当的TTBT最小值。最后，建立了多元回归模型来描述PFODA，TB和TTBT之间的关系以及校正后的0.748的确定系数。基于该模型，本研究提出了一种适用于接管系统的自适应结核病调整方法。