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We present a continuation to our previous work, in which we developed theMR-CKR framework to reason with knowledge overriding across contexts organizedin multi-relational hierarchies. Reasoning is realized via ASP with algebraicmeasures, allowing for flexible definitions of preferences. In this paper, weshow 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 challengingscenes for autonomous vehicle learning. In practice, most of the scene data forAV learning models common situations, thus it might be difficult to capturecases where a particular situation occurs (e.g. partial occlusions of acrossing pedestrian). The MR-CKR model allows for data organization exploitingthe multi-dimensionality of such data (e.g., temporal and spatial). Reasoningover multiple contexts enables the verification and configuration of scenes,using the combination of different scene ontologies. We describe a frameworkfor semantically guided data generation, based on a combination of MR-CKR andAlgebraic Measures. The framework is implemented in a proof-of-conceptprototype exemplifying some cases of scene generation.

Contextual Reasoning for Scene Generation (Technical Report)