Modeling takeover decisions in driving automation: a multilevel drift-diffusion model (MDDM) framework integrating...

Takeover behaviors represent one of the most critical safety challenges when driving with automation, yet existing models for takeover prediction often overlook the underlying cognitive mechanisms. To address this gap, we propose a multilevel modeling framework that integrates hierarchical linear modeling with a drift-diffusion model (DDM) to characterize how human, system, and environmental factors jointly shape the cognitive process of takeover decisions. The framework separates rapid fluctuations in environmental influences from stable human and system influences and maps these components onto distinct cognitive parameters. To evaluate the framework, we collected a driving-simulator dataset (N = 64) that systematically manipulated vehicle speed, optical flow, lighting, driving experience, and automation reliability. The fitted model reproduced response-time distributions that highly matched empirical data, indicating high psychological interpretability and modeling validity. These results suggested a dissociable process representation: environmental factors were represented through drift-rate differences, driving experience through decision-boundary differences, and automation reliability through non-decision-time differences. These findings suggest that takeover behavior emerges from distinct cognitive pathways. Moreover, the proposed framework offers a generalizable framework to integrate human, environmental, and system factors into cognitive process models in the context of human-intelligent system interaction.