  To make safe transitions from autonomous to manual control, a vehicle must
have a representation of the awareness of driver state; two metrics which
quantify this state are the Observable Readiness Index and Takeover Time. In
this work, we show that machine learning models which predict these two metrics
are robust to multiple camera views, expanding from the limited view angles in
prior research. Importantly, these models take as input feature vectors
corresponding to hand location and activity as well as gaze location, and we
explore the tradeoffs of different views in generating these feature vectors.
Further, we introduce two metrics to evaluate the quality of control
transitions following the takeover event (the maximal lateral deviation and
velocity deviation) and compute correlations of these post-takeover metrics to
the pre-takeover predictive metrics.
