Modelling the Dynamics of Situation Awareness Recovery: The Impact of Graded Warning Strategies in Automated Driving Takeovers

ABSTRACT

Recovering situation awareness (SA) from non‐driving related tasks during Level 3 takeovers is critical for safe and timely control transitions. However, out‐of‐the‐loop drivers often require an initial perceptual reorientation phase before effective SA recovery begins, a temporal delay that is frequently overlooked. Existing quantification models typically assume an instantaneous maximum recovery rate and therefore fail to capture the early temporal delay represented by the initialisation latency parameter, which may overestimate driver readiness during the critical early phase. This driving simulator study compared single‐ and two‐stage warning strategies and evaluated a parsimonious and interpretable sigmoid‐based dynamic modelling approach. Results indicated that two‐stage strategies produced an attentional priming effect, optimising visual attention distribution, enhancing subjective SA and improving takeover performance. Compared with the exponential reference model, the sigmoid model provided a better account of SA recovery dynamics. It characterised the delayed onset of SA accumulation in single‐stage recovery, whereas two‐stage strategies reduced the estimated initialisation latency. These findings suggest that graded warnings can serve as a cognitive scaffold that supports driver preparation before the control transition. This study provides a quantitative foundation for adaptive takeover warning design and supports the modelling of driver readiness dynamics in conditional automated driving.