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.

翻译：从条件自动化向手动驾驶控制的安全过渡与车辆的横向和纵向动力学密切相关。这种过渡可能源于系统触发的强制接管（MTOR）或驾驶员触发的自主接管（DTOR）。在两种情况下，接管过程都涉及不同的认知需求，并可能对驾驶行为产生不同影响。本研究分析了在虚拟沉浸式现实环境中从104名参与者记录的304次接管尝试中的驾驶稳定性和感知心理负荷。采用探索性方法，本动态仿真研究采用混合因子设计。利用基于深度神经网络的生存分析与SHAP可解释性，研究考察了协变量对感知-反应时间（PRT）的影响，区分了安全与不安全的控制过渡，并揭示了这些过渡的时间动态特性。分析了实验组关键参数的分布，并通过多元线性回归估计了影响感知心理负荷的因素。研究结果表明，当驾驶员具备先前控制过渡经验并熟悉自动驾驶车辆（AVs）时，不安全接管的风险（表现为更长的PRT）显著降低。然而，驾驶员对AVs的先前熟悉度和经验仅降低了与DTOR相关的感知心理负荷，对MTOR的认知需求影响不显著。此外，自动驾过程中的多任务处理显著增加了与DTOR相关的认知需求，并在MTOR情境中导致更长的PRT。