  We present the Qualitative Explainable Graph (QXG): a unified symbolic and
qualitative representation for scene understanding in urban mobility. QXG
enables the interpretation of an automated vehicle's environment using sensor
data and machine learning models. It leverages spatio-temporal graphs and
qualitative constraints to extract scene semantics from raw sensor inputs, such
as LiDAR and camera data, offering an intelligible scene model. Crucially, QXG
can be incrementally constructed in real-time, making it a versatile tool for
in-vehicle explanations and real-time decision-making across various sensor
types. Our research showcases the transformative potential of QXG, particularly
in the context of automated driving, where it elucidates decision rationales by
linking the graph with vehicle actions. These explanations serve diverse
purposes, from informing passengers and alerting vulnerable road users (VRUs)
to enabling post-analysis of prior behaviours.
