## 2 State of the Art  
Different works in the transportation field have identified the importance of domain-knowledge structures for different purposes: to assess traffic scenes in real time applications [3], to provide automatic support for design and analysis of performance monitoring systems for Public Transport Systems [4], or to infer knowledge to aid test management [5]. Several ontologies in the transportation domain can be found in the literature, e.g., the Ontology of Traffic Networks (OTN) [6], which is summarized as a direct encoding of geographic data files (GDF) in OWL, and the Transport Disruption ontology [7], which provides a formal framework for modeling travel planning-related events. In recent years, the focus has been on knowledge-based approaches representing scenarios with the purpose of promoting the scenario-based evaluation of ADASs and AD. Ontologies have become a key component for formalizing this knowledge. An event-based scenario description for testing was presented [8] based on the three abstraction levels for scenario description: functional, logical, and concrete scenarios, as described in [9] and adopted by the Pegasus project ([[https://www.pegasusprojekt.de/en/](https://www.pegasusprojekt.de/en/)]([https://www.pegasusprojekt.de/en/](https://www.pegasusprojekt.de/en/)) accessed on 5 April 2021). The strategy was enhanced with a procedure that enabled qualitative description for the generation of more concrete scenarios. This scenario-based testing strategy entails the development of a structured knowledge and formal scenario representation language for driving simulation environments, such as, OpenSCENARIO ([[https://www.asam.net/project-detail/asam-openscenario-v1x/](https://www.asam.net/project-detail/asam-openscenario-v1x/)]([https://www.asam.net/project-detail/asam-openscenario-v1x/](https://www.asam.net/project-detail/asam-openscenario-v1x/)) accessed on 4 June 2021), CommonRoad ([[https://commonroad.in.tum.de/](https://commonroad.in.tum.de/)]([https://commonroad.in.tum.de/](https://commonroad.in.tum.de/)) accessed on 4 June 2021), or the Safety Pool's scenario description language ([[https://www.safetypool.ai/](https://www.safetypool.ai/)]([https://www.safetypool.ai/](https://www.safetypool.ai/)) accessed on 20 June 2021). Despite these advances, there is currently a lack of an open knowledge base in the automotive domain that covers the needs of the testing and labeling applications. Therefore, one of the aims of this paper is to present AGO. This was built with the purpose of formalizing the terminology used for representing automotive scenarios and providing the required knowledge layer to support semantic-labeling tools. A small number of studies have been published related to ontology engineering methodologies that present design and construction principles. However, to the best of our knowledge, there is not yet a standardized approach or any formal requirement other than the ontology languages defined by the W3C group [10; 11] to define an ontology. In the following, a selection of existing works is discussed. METHONTOLOGY [12], On-To-Knowledge Methodology (OTKM) [13], and DILIGENT [14] constitute the basis for many subsequent proposals. For instance, NeOn [2] emphasizes the reuse of existing resources for building a collaborative ontology rather than starting from scratch, such as in METHONTOLOGY [12]. In relation to the reusable resources related to the automotive domain, in 2018 a survey of existing ontologies for transportation was carried out [15] in which several approaches were studied and compared. Among these, the ontology for road traffic management [16] presents bidirectional axioms (\"doesAction\" and \"isActionDoneBy\") that relate classes to driving actions. Other examples also consider attributes by introducing additional axioms into the ontology. UPON [17] was published in 2005 as a proposal that takes advantage of the Unified Software Development Process and the Unified Modeling Language (UML).