Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

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Objective:Our objective was to determine whether there is a need to go beyond measures of automation deactivation time to understand the transition to manual driving after take-over requests (TORs) using the example of office tasks as nondriving-related tasks (NDRTs).Background:Office tasks are likely NDRTs during automated commutes to/from work. Complex tasks can influence how manual control and visual attention is recovered after TORs.Method:N = 51 participants in a driving simulator performed either one of two office tasks or no task (between subjects). We recorded reaction times in a high-urgency and low-urgency scenario (within subjects) and analyzed task interruption strategies.Results:90% of the participants who performed an NDRT deactivated the automation after 7 to 8 s. However, 90% of the same drivers looked at the side mirror for the first time only after 11 to 14 s. Drivers with office tasks either interrupted the tasks sequentially or in parallel. Strategies were not adapted to the take-over situation or the task but appeared to be due to individual preferences.Conclusion:Drivers engaged in NDRTs may neglect lower priority subtasks after a TOR, such as mirror checking. Therefore, there is a need to go beyond measures of automation deactivation time to understand the transition to manual driving. Using analyses of attentional dynamics during take-over situations may enhance the safety of future car-driver handover assistance systems.Application:If low driver availability is detected, TORs should only be used as a fallback option if sufficient time and adaptive driver support can be provided.