  Out-of-training-distribution (OOD) scenarios are a common challenge of
learning agents at deployment, typically leading to arbitrary deductions and
poorly-informed decisions. In principle, detection of and adaptation to OOD
scenes can mitigate their adverse effects. In this paper, we highlight the
limitations of current approaches to novel driving scenes and propose an
epistemic uncertainty-aware planning method, called \emph{robust imitative
planning} (RIP). Our method can detect and recover from some distribution
shifts, reducing the overconfident and catastrophic extrapolations in OOD
scenes. If the model's uncertainty is too great to suggest a safe course of
action, the model can instead query the expert driver for feedback, enabling
sample-efficient online adaptation, a variant of our method we term
\emph{adaptive robust imitative planning} (AdaRIP). Our methods outperform
current state-of-the-art approaches in the nuScenes \emph{prediction}
challenge, but since no benchmark evaluating OOD detection and adaption
currently exists to assess \emph{control}, we introduce an autonomous car
novel-scene benchmark, \texttt{CARNOVEL}, to evaluate the robustness of driving
agents to a suite of tasks with distribution shifts.
