Knowledge representation and reasoning has a long history of examining how knowledge can be formalized, interpreted, and semantically analyzed by machines. In the area of automated vehicles, recent advances suggest the ability to formalize and leverage relevant knowledge as a key enabler in handling the inherently open and complex context of the traffic world. This paper demonstrates ontologies to be a powerful tool for a) modeling and formalization of and b) reasoning about factors associated with criticality in the environment of automated vehicles. For this, we leverage the well-known 6-Layer Model to create a formal representation of the environmental context. Within this representation, an ontology models domain knowledge as logical axioms, enabling deduction on the presence of critical factors within traffic scenes and scenarios. For executing automated analyses, a joint description logic and rule reasoner is used in combination with an a-priori predicate augmentation. We elaborate on the modular approach, present a publicly available implementation, and evaluate the method by means of a large-scale drone data set of urban traffic scenarios.
翻译:在自动化车辆领域,最近的进展表明,能够将相关知识正规化和加以利用,作为处理交通世界内在的开放和复杂环境的关键促进因素。本文件表明,本要素是进行以下工作的一个有力工具:一种(模拟和正规化)和(b)与自动车辆环境的临界性有关的因素的推理。为此,我们利用众所周知的6-Layer模型,建立环境环境环境的正式代表。在这个模型中,一个肿瘤模型域知识作为逻辑轴体,使交通场景和情景中关键因素的存在得以扣减。进行自动化分析时,将联合描述逻辑和规则解释器与优先的上游增强结合起来使用。我们详细阐述模块化方法,介绍可公开使用的实施情况,并以大规模无人驾驶飞机数据组合的城市交通情景评估方法。