An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving
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Abstract (arXiv)
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.
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References · 14
- 2018Scenarios for Development, Test and Validation of Automated Vehiclescited by 48
- 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 43
- 2017Ontology based Scene Creation for the Development of Automated Vehiclescited by 40
- 20206-Layer Model for a Structured Description and Categorization of Urban Traffic and Environmentcited by 26
- 2023Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Riskcited by 11
- 2022No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehiclescited by 10
- 2017Towards a Skill- And Ability-Based Development Process for Self-Aware Automated Road Vehiclescited by 6
- 2024Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptionscited by 4
- 2022Phenomenon-Signal Model: Formalisation, Graph and Applicationcited by 3
- 2020Towards Efficient Hazard Identification in the Concept Phase of Driverless Vehicle Developmentcited by 2
- 2021Ph"anomen-Signal-Modell: Formalismus, Graph und Anwendungcited by 2
- 2018Representing the Unknown - Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generationcited by 1
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