  The end-to-end learning pipeline is gradually creating a paradigm shift in
the ongoing development of highly autonomous vehicles, largely due to advances
in deep learning, the availability of large-scale training datasets, and
improvements in integrated sensor devices. However, a lack of interpretability
in real-time decisions with contemporary learning methods impedes user trust
and attenuates the widespread deployment and commercialization of such
vehicles. Moreover, the issue is exacerbated when these cars are involved in or
cause traffic accidents. Such drawback raises serious safety concerns from
societal and legal perspectives. Consequently, explainability in end-to-end
autonomous driving is essential to build trust in vehicular automation.
However, the safety and explainability aspects of end-to-end driving have
generally been investigated disjointly by researchers in today's state of the
art. This survey aims to bridge the gaps between these topics and seeks to
answer the following research question: When and how can explanations improve
safety of end-to-end autonomous driving? In this regard, we first revisit
established safety and state-of-the-art explainability techniques in end-to-end
driving. Furthermore, we present three critical case studies and show the
pivotal role of explanations in enhancing self-driving safety. Finally, we
describe insights from empirical studies and reveal potential value,
limitations, and caveats of practical explainable AI methods with respect to
their safety assurance in end-to-end autonomous driving.
