  Autonomous vehicles often make complex decisions via machine learning-based
predictive models applied to collected sensor data. While this combination of
methods provides a foundation for real-time actions, self-driving behavior
primarily remains opaque to end users. In this sense, explainability of
real-time decisions is a crucial and natural requirement for building trust in
autonomous vehicles. Moreover, as autonomous vehicles still cause serious
traffic accidents for various reasons, timely conveyance of upcoming hazards to
road users can help improve scene understanding and prevent potential risks.
Hence, there is also a need to supply autonomous vehicles with user-friendly
interfaces for effective human-machine teaming. Motivated by this problem, we
study the role of explainable AI and human-machine interface jointly in
building trust in vehicle autonomy. We first present a broad context of the
explanatory human-machine systems with the "3W1H" (what, whom, when, how)
approach. Based on these findings, we present a situation awareness framework
for calibrating users' trust in self-driving behavior. Finally, we perform an
experiment on our framework, conduct a user study on it, and validate the
empirical findings with hypothesis testing.
