  The safe transition from conditional automation to manual driving control is
significantly intertwined with the vehicle's lateral and longitudinal dynamics.
The transition may occur as a result of a system-initiated mandatory takeover
(MTOR) or as a driver-initiated discretionary takeover (DTOR). In either
condition, the takeover process entails differing cognitive demands and may
affect the driving behaviour differently. This study analyzes driving stability
and perceived mental workload in 304 takeover attempts recorded from 104
participants within virtual and immersive reality environments. Adopting an
exploratory approach, this dynamic simulator study employs a mixed factorial
design. Utilizing a deep neural network-based survival analysis with SHAP
interpretability, the study investigated the influence of covariates on
perception-reaction time (PRT), distinguishing between safe and unsafe control
transition and offering insights into the temporal dynamics of these shifts.
The distributions of key parameters in experimental groups were analyzed and
factors influencing the perceived mental workload were estimated using
multivariate linear regression. The findings indicate a notable decrease in the
risk of unsafe takeovers (described by a longer PRT) when drivers have prior
control-transition experience and familiarity with Automated Vehicles (AVs).
However, driver's prior familiarity and experience with AVs only decreased the
perceived mental workload associated with DTOR, with an insignificant impact on
the cognitive demand of MTOR. Furthermore, multitasking during automated
driving significantly elevated the cognitive demand linked to DTOR and led to
longer PRT in MTOR situations.
