  We present a continuation to our previous work, in which we developed the
MR-CKR framework to reason with knowledge overriding across contexts organized
in multi-relational hierarchies. Reasoning is realized via ASP with algebraic
measures, allowing for flexible definitions of preferences. In this paper, we
show how to apply our theoretical work to real autonomous-vehicle scene data.
Goal of this work is to apply MR-CKR to the problem of generating challenging
scenes for autonomous vehicle learning. In practice, most of the scene data for
AV learning models common situations, thus it might be difficult to capture
cases where a particular situation occurs (e.g. partial occlusions of a
crossing pedestrian). The MR-CKR model allows for data organization exploiting
the multi-dimensionality of such data (e.g., temporal and spatial). Reasoning
over multiple contexts enables the verification and configuration of scenes,
using the combination of different scene ontologies. We describe a framework
for semantically guided data generation, based on a combination of MR-CKR and
Algebraic Measures. The framework is implemented in a proof-of-concept
prototype exemplifying some cases of scene generation.
