Analyzing driving stability and cognitive demands in takeover of conditional automation with immersive virtual reality

The transition from conditional automation to manual driving may occur due to a system-initiated mandatory takeover (MTOR) or a driver-initiated discretionary takeover (DTOR). Considering both conditions, we analyze driving stability and perceived mental workload in 304 takeover attempts recorded from 104 participants within immersive virtual reality. We employ a mixed factorial design for this dynamic simulator study. Utilizing a deep neural network-based survival analysis with SHAP interpretability, we investigated the influence of covariates on perception–reaction time (PRT), distinguishing between safe and unsafe control transitions. The factors influencing perceived mental workload were estimated using multivariate linear regression. The findings indicate a notable decrease in the risk of unsafe takeovers when drivers have prior control transition experience and familiarity with automated vehicles (AV). However, drivers’ prior familiarity and experience with AVs only decreased the perceived mental workload associated with DTOR, with an insignificant impact on the cognitive demand for MTOR.