  With increasing automation in passenger vehicles, the study of safe and
smooth occupant-vehicle interaction and control transitions is key. In this
study, we focus on the development of contextual, semantically meaningful
representations of the driver state, which can then be used to determine the
appropriate timing and conditions for transfer of control between driver and
vehicle. To this end, we conduct a large-scale real-world controlled data study
where participants are instructed to take-over control from an autonomous agent
under different driving conditions while engaged in a variety of distracting
activities. These take-over events are captured using multiple driver-facing
cameras, which when labelled result in a dataset of control transitions and
their corresponding take-over times (TOTs). After augmenting this dataset, we
develop and train TOT models that operate sequentially on low and mid-level
features produced by computer vision algorithms operating on different
driver-facing camera views. The proposed TOT model produces continuous
estimates of take-over times without delay, and shows promising qualitative and
quantitative results in complex real-world scenarios.
