  Safety-critical Autonomous Systems require trustworthy and transparent
decision-making process to be deployable in the real world. The advancement of
Machine Learning introduces high performance but largely through black-box
algorithms. We focus the discussion of explainability specifically with
Autonomous Vehicles (AVs). As a safety-critical system, AVs provide the unique
opportunity to utilize cutting-edge Machine Learning techniques while requiring
transparency in decision making. Interpretability in every action the AV takes
becomes crucial in post-hoc analysis where blame assignment might be necessary.
In this paper, we provide positioning on how researchers could consider
incorporating explainability and interpretability into design and optimization
of separate Autonomous Vehicle modules including Perception, Planning, and
Control.
