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 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 exemplarily evaluate the method by means of a large-scale drone data set of urban traffic scenarios.

中文翻译：

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使用本体对自动驾驶的关键性进行形式化和识别

知识表示和推理在检查知识如何被机器形式化、解释和语义分析方面有着悠久的历史。在自动驾驶汽车领域，最近的进展表明，将相关知识形式化和利用的能力是处理交通世界固有的开放和复杂环境的关键推动力。本文证明本体是一个强大的工具，用于 a) 建模和形式化，以及 b) 推理与自动车辆环境中的关键性相关的因素。为此，我们利用著名的 6 层模型来创建环境上下文的正式表示。在这种表示中，本体将领域知识建模为逻辑公理，从而能够推断交通场景中关键因素的存在。为了执行自动分析，联合描述逻辑和规则推理器与先验谓词增强结合使用。我们详细阐述了模块化方法，展示了一个公开可用的实现，并通过城市交通场景的大规模无人机数据集示例性地评估了该方法。