A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving

被引：3
作者：
Zipfl, Maximilian [1 ,2 ]
Koch, Nina [2 ]
Zoellner, J. Marius [1 ,2 ]
机构：
[1] FZI Res Ctr Informat Technol, Karlsruhe, Germany
[2] KIT Karlsruhe Inst Technol, Karlsruhe, Germany
来源：
2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV | 2023年
关键词：
SYSTEMS;
D O I：
10.1109/IV55152.2023.10186681
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a promising method for validating such a complex system. Ontologies can be utilized to produce test scenarios that are both meaningful and relevant. One crucial aspect of this process is selecting the appropriate method for describing the entities involved. The level of detail and specific entity classes required will vary depending on the system being tested. It is important to choose an ontology that properly reflects these needs. This paper summarizes key representative ontologies for scenario-based testing and related use cases in the field of autonomous driving. The considered ontologies are classified according to their level of detail for both static facts and dynamic aspects. Furthermore, the ontologies are evaluated based on the presence of important entity classes and the relations between them.
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页数：7
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