Ontology-based Scenario Generation for Automated Driving Systems Verification and Validation using Rules of the Road

The verification and validation (V&V) process for Automated Driving Systems (ADS) has undergone a significant transformation in defining the meaning of safety. Initially rooted in the quantity of miles driven, it has now shifted towards emphasizing the quality of test miles. These test miles mus...

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Veröffentlicht in:IEEE transactions on intelligent vehicles 2024, p.1-11
Hauptverfasser: da Costa, Antonio Anastasio Bruto, Irvine, Patrick, Zhang, Xizhe, Khastgir, Siddartha, Jennings, Paul
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Sprache:eng
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Zusammenfassung:The verification and validation (V&V) process for Automated Driving Systems (ADS) has undergone a significant transformation in defining the meaning of safety. Initially rooted in the quantity of miles driven, it has now shifted towards emphasizing the quality of test miles. These test miles must effectively capture the full spectrum of behaviours and operational design domains (ODD) of the ADS. To assess an ADS's compliance with specific rules or requirements, a connection must be established between the rules and the scenarios used for testing. In this paper, we propose a targeted scenario generation methodology aimed at testing ADS against formal rules. Our approach leverages ontologies to represent objects and their relationships in a scenario. The rules, are first formally specified, expressed as horn clauses. We then employ a rule transformation process, along with off-the-shelf reasoning tools, to generate corresponding scenarios. These generated scenarios may then utilized to test the ADS's adherence to the specified rules. To illustrate the effectiveness of our methodology, we present an application to example rules derived from both the UK Highway Code and the Vienna Conventions. By utilizing our approach, we enhance the precision and rigor of the verification and validation flow for ADS, ensuring improved safety measures during operation.
ISSN:2379-8858
2379-8904
DOI:10.1109/TIV.2024.3377534