  Understanding occupant-vehicle interactions by modeling control transitions
is important to ensure safe approaches to passenger vehicle automation. Models
which contain contextual, semantically meaningful representations of driver
states can be used to determine the appropriate timing and conditions for
transfer of control between driver and vehicle. However, such models rely on
real-world control take-over data from drivers engaged in distracting
activities, which is costly to collect. Here, we introduce a scheme for data
augmentation for such a dataset. Using the augmented dataset, we develop and
train take-over time (TOT) models that operate sequentially on mid and
high-level features produced by computer vision algorithms operating on
different driver-facing camera views, showing models trained on the augmented
dataset to outperform the initial dataset. The demonstrated model features
encode different aspects of the driver state, pertaining to the face, hands,
foot and upper body of the driver. We perform ablative experiments on feature
combinations as well as model architectures, showing that a TOT model supported
by augmented data can be used to produce continuous estimates of take-over
times without delay, suitable for complex real-world scenarios.
