# Take-Over Requests after Making in Autonomous Vehicles
## 1 Introduction  
Autonomous vehicle (AV) technologies, such as Adaptive Cruise Control (ACC) and Advanced Driver Assistance Systems (ADAS) that correspond to Society of Automotive Engineering (SAE) Level 1 or 2, have become common, if minor, components of vehicles available today [1]. These partial vehicle automations have spurred the development of more advanced AV technologies. Consequently, drivers expect better performance and increased safety [2]. Even with improved capabilities, AVs must operate properly to earn driver trust and acceptance [3, 4]. However, trust is a human factor that is easily lost. If a driver does not recognize the route the AV is following or is unsatisfied with automated driving, for instance, they might take over vehicle operation, and because today's vehicle systems can easily switch between automatic and manual modes [5, 6, 7], often with a slight (perhaps mistaken) tap of the brake pedal or steering wheel, driver acceptance of AV technology can be lost. In addition to performance improvements, AVs must provide context-based and intelligent information about an upcoming event (e.g., a speed bump or an accident) to earn driver trust. These feedforward alerts allow the driver to respond properly to the situation and trust that AV is assuming control for a good reason [8]. This information must include both a description of the vehicle's actions (i.e., 'How') and the reason for those actions (i.e., 'Why') based on its artificial intelligence (AI). When these 'How' and 'Why' messages are accompanied with varying modality types (auditory, visual, and auditory + visual), especially with multi-modality, driver trust and satisfaction with driving performanceincreases [9]. As it relates to AV intelligibility--how the vehicle represents to drivers what it knows, how it knows, and what it is doing based on its AI system--trust also depends on how politely the vehicle provides the information and how well the interface mimics human behavior [10; 11; 12; 13; 14; 15]. Besides contextual information provided by the vehicle, trust toward AVs is affected by drivers' in-situ states, which vary with task load. As SAE Levels of Driving Automation rise from Level 0 (no automation/the driver is fully responsible for driving) to Level 5 (fully autonomous vehicle), drivers can increasingly perform various Non-Driving Related Tasks (NDRTs), each with its own task load. While performing a high-load task, that is, a task that requires continual attention such as texting or sleeping, drivers may not be available to accept intelligibility services, or information pushed from the vehicle to the driver based on contextual operation. If the AV's warning of an upcoming long curve prevents the driver from spilling a beverage, for example, the driver will be more satisfied with the AI system. However, continuous alerts when the driver cannot attend to them will lead to an interruptive user experience. Thus, intelligibility is defined by how well the AV provides context-based and intelligent information about upcoming events according to the NDRTs that drivers are performing. Some studies have explored how NDRTs affect user experience in AVs, though research on sleeping while driving is scarce. Yet, drivers most preferred to sleep while driving an AV (Table 1) and decreased engagement in driving (whilst adopting a higher level of automation) could increase drowsiness and the likelihood of falling asleep [16; 17]. Of course, one way that AVs promote driver safety is through Take-over Requests (TOR), wherein the vehicle prompts the driver to assume control. However, TORs that occur while the driver is asleep could be missed and thus lead to accidents. Therefore, to better ensure intelligibility as well as driver safety, AVs should be aware of drivers' in-situ states, or the NDRT that the driver is performing, and intervene by providing appropriate information.