# Take-Over Requests after Making in Autonomous Vehicles
## 10 Conclusions  
We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses vary so as to present a guide and manner in which to align intelligibility services for take-over request alerts that are appropriate to wake a driver from sleep and the take-over scenario with the greatest needs. Drivers awakened by a take-over request alert delivered >10 s prior to an event were more focused on the driving context regardless of the presentation manner (i.e., situation awareness aid). The >10 s take-over request alert allowed drivers more time to adapt to situations with the highest demand. On the other hand, even take-over request alerts delivered within 5 s, if designed with an appropriate presentation modality, significantly reduced the time drivers needed to safely adapt to situations while maintaining equivalent levels of autonomous vehicle acceptance and perceived task workload to take-over request alerts delivered in >10 s. In particular, unlike take-over request alerts delivered in >10 s, a visual accompaniment was critical for a safe take-over request delivered in <5 s. Data on physiological arousal also reveal that visual or visual + auditory take-over request alerts are more efficient in higher temporal demand situations (i.e., when delivered at fewer than 5 s rather than at greater than 10 s), which helps drivers switch quickly to more attentive manual driving. This study contributes to understanding the influence of take-over requests at the moment drivers wake from sleep, thereby delivering guidelines regarding take-over requests in autonomous vehicles, focusing on when (feedforward timing) and how (modality type) to inform drivers who wake from sleep. This research will support safer take-over requests in conditional automation (level 3). Far beyond level 3, in higher automation (level 4-5) where intelligibility services and non-driving related services are prevalent, this will also contribute to the improvement of the manner and guidelines for when and how to best inform drivers who wake from sleep in autonomous vehicles. This knowledge will be useful for car developers who wish to design autonomous vehicles that best comply with the safest guidelines for take-over requests aimed at sleeping drivers. This will also assist researchers in conducting further in-depth research, as shown in future works below. The current study sets out some avenues for future work. First, the foci of this study are situations where take-over requests occur and drivers wake from sleep. We will conduct more take-over request studies by extending to other non-driving related tasks, such as eating/drinking, using media, and having a conversation with passengers, thereby comparing how drivers in various situations react to take-over requests. This enhances the manner in which an autonomous vehicle can provide take-over requests that consider drivers' in-situ situations (e.g., non-driving related tasks and the driver's physical state and distraction based on sensor data) and enables development of an intelligent system to adaptively support driver-centered take-over requests in autonomous vehicles. Secondly, as a means of implementing an intelligent system, development of a machine learning model to automatically classify 'user interruptibility' is required. Hence, we will develop a machine learning model for evaluation metrics that were collected in this study (e.g., driving performances, physiological responses, and other feature extraction from video recordings). Lastly, this study is expected to be beneficial in its field for its exploration of drivers' reactions to take-over requests in enhanced reality (e.g., actual vehicle) and for elements of its experimental design, such as sleep by time difference. Conceptualization, W.K., E.J. and S.K. ; methodology, W.K. and E.J. ; formal analysis, W.K. and E.J. ; writing--original draft preparation, W.K., E.J., G.K. and D.Y. ; writing--review and editing, W.K., E.J., G.K., D.Y. and S.K. ; supervision, S.K.