Computer Science > Software Engineering
[Submitted on 10 Sep 2024 (v1), last revised 15 Nov 2024 (this version, v3)]
Title:An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving
View PDF HTML (experimental)Abstract:Vehicles in public traffic that are equipped with Automated Driving Systems are subject to a number of expectations: Among other aspects, their behavior should be safe, conforming to the rules of the road and provide mobility to their users. This poses challenges for the developers of such systems: Developers are responsible for specifying this behavior, for example, in terms of requirements at system design time. As we will discuss in the article, this specification always involves the need for assumptions and trade-offs. As a result, insufficiencies in such a behavior specification can occur that can potentially lead to unsafe system behavior. In order to support the identification of specification insufficiencies, requirements and respective assumptions need to be made explicit. In this article, we propose the Semantic Norm Behavior Analysis as an ontology-based approach to specify the behavior for an Automated Driving System equipped vehicle. We use ontologies to formally represent specified behavior for a targeted operational environment, and to establish traceability between specified behavior and the addressed stakeholder needs. Furthermore, we illustrate the application of the Semantic Norm Behavior Analysis in a German legal context with two example scenarios and evaluate our results. Our evaluation shows that the explicit documentation of assumptions in the behavior specification supports both the identification of specification insufficiencies and their treatment. Therefore, this article provides requirements, terminology and an according methodology to facilitate ontology-based behavior specifications in automated driving.
Submission history
From: Nayel Salem [view email][v1] Tue, 10 Sep 2024 16:00:22 UTC (5,535 KB)
[v2] Fri, 11 Oct 2024 17:02:19 UTC (5,565 KB)
[v3] Fri, 15 Nov 2024 14:09:05 UTC (5,565 KB)
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