  The future of automated driving (AD) is rooted in the development of robust,
fair and explainable artificial intelligence methods. Upon request, automated
vehicles must be able to explain their decisions to the driver and the car
passengers, to the pedestrians and other vulnerable road users and potentially
to external auditors in case of accidents. However, nowadays, most explainable
methods still rely on quantitative analysis of the AD scene representations
captured by multiple sensors. This paper proposes a novel representation of AD
scenes, called Qualitative eXplainable Graph (QXG), dedicated to qualitative
spatiotemporal reasoning of long-term scenes. The construction of this graph
exploits the recent Qualitative Constraint Acquisition paradigm. Our
experimental results on NuScenes, an open real-world multi-modal dataset, show
that the qualitative eXplainable graph of an AD scene composed of 40 frames can
be computed in real-time and light in space storage which makes it a
potentially interesting tool for improved and more trustworthy perception and
control processes in AD.
