An Adaptive Time Budget Adjustment Strategy Based on a Take-Over Performance Model for Passive Fatigue

As human-machine collaborative driving systems, highly automated driving vehicles require human drivers to take over when take-over requests are triggered. Extensive studies have shown that drivers' take-over performance is affected by their fatigue state, traffic conditions, and the take-over time budget (TB). However, there is still a paucity of a systematic understanding of how these factors affect take-over performance, which prevents the implementation of adaptive take-over systems. This study establishes a highly accurate take-over performance prediction model to systematically explore the effects of these factors on take-over performance and to propose an adaptive TB adjustment strategy for highly automated driving vehicles. First, we propose metrics to evaluate drivers' fatigue states and the relative positions of surrounding traffic. Second, a generalized additive model is established to predict take-over performance and accurately evaluate the influence of the aforementioned factors on take-over performance. Based on the model, we propose an adaptive adjustment strategy of the TB for take-over systems and demonstrate its effectiveness by a verification experiment. This study contributes to understanding the influence of drivers' passive fatigue states, the relative positions of surrounding traffic, and the TB on drivers' take-over performance as well as to the development of adaptive take-over systems for highly automated vehicles.