  The car-to-driver handover is a critically important component of safe
autonomous vehicle operation when the vehicle is unable to safely proceed on
its own. Current implementations of this handover in automobiles take the form
of a generic alarm indicating an imminent transfer of control back to the human
driver. However, certain levels of vehicle autonomy may allow the driver to
engage in other, non-driving related tasks prior to a handover, leading to
substantial difficulty in quickly regaining situational awareness. This delay
in re-orientation could potentially lead to life-threatening failures unless
mitigating steps are taken. Explainable AI has been shown to improve fluency
and teamwork in human-robot collaboration scenarios. Therefore, we hypothesize
that by utilizing autonomous explanation, these car-to-driver handovers can be
performed more safely and reliably. The rationale is, by providing the driver
with additional situational knowledge, they will more rapidly focus on the
relevant parts of the driving environment. Towards this end, we propose an
algorithmic failure-mode identification and explanation approach to enable
informed handovers from vehicle to driver. Furthermore, we propose a set of
human-subjects driving-simulator studies to determine the appropriate form of
explanation during handovers, as well as validate our framework.
