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.

翻译：我们延续了之前的工作，在此前开发了MR-CKR框架，用于跨多关系层次组织中具有知识覆盖的上下文进行推理。该推理通过结合代数度量的回答集程序实现，可灵活定义偏好。本文展示了如何将理论成果应用于真实自动驾驶场景数据。本工作目标是应用MR-CKR解决自动驾驶学习中的挑战性场景生成问题。实际中，自动驾驶车辆的学习场景数据多集中于常见情境，因此难以捕获特定情形（如路口行人部分遮挡）。MR-CKR模型通过利用数据的多维性（如时间与空间维度）实现数据组织。跨多上下文的推理能够通过不同场景本体的组合，实现场景的验证与配置。我们描述了一个基于MR-CKR与代数度量相结合的语义引导数据生成框架，该框架已在概念验证原型中实现，并展示了若干场景生成案例。