A Paper titled “Comprehensive Assessment of Driver Takeover Readiness via a Multimodal Fusion Multi-Task Learning Model” has been published in Advanced Engineering Informatics authored by Hanwen Deng, Shuxiang Lin, Chaojie Fan, Xianhui Wu, Kui Wang, Yong Peng and George Yannis. This Paper constructs a multimodal takeover readiness dataset that incorporates visual, physiological, gaze, and demographic features. The dataset was collected from 32 participants using a within-subject design, yielding a total of 20,160 s of synchronized multimodal time-series data. Furthermore, post-trial self-reported questionnaire scores were used as the ground-truth labels for takeover readiness. The experimental results demonstrate that the proposed model effectively fuses multimodal features and captures task-specific representations, achieving an average Accuracy of 79.0%, Balanced Accuracy of 78.5%, and Macro F1 score of 78.2% across five 5-class classification tasks, outperforming the selected baselines. These findings provide insights for the development of more precise and adaptive intervention strategies in future automated driving systems.