  Understanding driving scenes and communicating automated vehicle decisions
are key requirements for trustworthy automated driving. In this article, we
introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic
and qualitative representation for scene understanding in urban mobility. The
QXG enables interpreting an automated vehicle's environment using sensor data
and machine learning models. It utilizes spatio-temporal graphs and qualitative
constraints to extract scene semantics from raw sensor inputs, such as LiDAR
and camera data, offering an interpretable scene model. A QXG can be
incrementally constructed in real-time, making it a versatile tool for
in-vehicle explanations across various sensor types. Our research showcases the
potential of QXG, particularly in the context of automated driving, where it
can rationalize decisions by linking the graph with observed actions. These
explanations can serve diverse purposes, from informing passengers and alerting
vulnerable road users to enabling post-hoc analysis of prior behaviors.
