A paper titled Analyzing SHAP values of XGBoost algorithms to understand driving features affecting take-over time from vehicle alert to driver action authored by Marios Sekadakis, Thodoris Garefalakis, Peter Moertl and George Yannis has been published in Displays. This Study investigates the factors influencing Take-Over Time (TOT) during transitions from automated to manual driving, emphasizing the novelty of applying XGBoost modeling combined with SHAP analysis to uncover non-linear and implicit dependencies between features. Driving simulation data were utilized as key variables for the analysis. The findings indicate that higher automation levels resulted in longer TOT, with SHAP values consistently positive for AD Level 3, demonstrating the added value of explainable machine learning in clarifying these patterns. Furthermore, Dynamic driving parameters, such as deceleration and speed variability, were also significant. Moreover strong negative deceleration values were generally associated with shorter TOT, reflecting quicker responses under urgent braking. Speed showed a moderate positive effect on TOT at 80–110 km/h, with drivers taking additional time to assess the environment, but higher speeds (above 110 km/h) resulted in quicker responses. By combining methodological innovation with contextual insights, this study contributes to a deeper understanding of takeover behavior and provides actionable evidence for optimizing adaptive HMI design and takeover strategies in AD systems.