  Autonomous driving has achieved significant milestones in research and
development over the last two decades. There is increasing interest in the
field as the deployment of autonomous vehicles (AVs) promises safer and more
ecologically friendly transportation systems. With the rapid progress in
computationally powerful artificial intelligence (AI) techniques, AVs can sense
their environment with high precision, make safe real-time decisions, and
operate reliably without human intervention. However, intelligent
decision-making in such vehicles is not generally understandable by humans in
the current state of the art, and such deficiency hinders this technology from
being socially acceptable. Hence, aside from making safe real-time decisions,
AVs must also explain their AI-guided decision-making process in order to be
regulatory compliant across many jurisdictions. Our study sheds comprehensive
light on the development of explainable artificial intelligence (XAI)
approaches for AVs. In particular, we make the following contributions. First,
we provide a thorough overview of the state-of-the-art and emerging approaches
for XAI-based autonomous driving. We then propose a conceptual framework that
considers the essential elements for explainable end-to-end autonomous driving.
Finally, we present XAI-based prospective directions and emerging paradigms for
future directions that hold promise for enhancing transparency,
trustworthiness, and societal acceptance of AVs.
