Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving

• URL: http://arxiv.org/abs/2205.01532v1
• Date: Tue, 3 May 2022 14:32:11 GMT
• Title: Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving
• Authors: Lukas Westhofen, Christian Neurohr, Martin Butz, Maike Scholtes, Michael Schuldes
• Abstract summary: Recent advances suggest the ability to leverage relevant knowledge in handling the inherently open and complex context of the traffic world. This paper demonstrates to be a powerful tool for modeling and formalization of factors associated with criticality in the environment of automated vehicles. We elaborate on the modular approach, present a publicly available implementation, and evaluate the method by means of a large-scale drone data set of urban traffic scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge representation and reasoning has a long history of examining how knowledge can be formalized, interpreted, and semantically analyzed by machines. In the area of automated vehicles, recent advances suggest the ability to formalize and leverage relevant knowledge as a key enabler in handling the inherently open and complex context of the traffic world. This paper demonstrates ontologies to be a powerful tool for a) modeling and formalization of and b) reasoning about factors associated with criticality in the environment of automated vehicles. For this, we leverage the well-known 6-Layer Model to create a formal representation of the environmental context. Within this representation, an ontology models domain knowledge as logical axioms, enabling deduction on the presence of critical factors within traffic scenes and scenarios. For executing automated analyses, a joint description logic and rule reasoner is used in combination with an a-priori predicate augmentation. We elaborate on the modular approach, present a publicly available implementation, and evaluate the method by means of a large-scale drone data set of urban traffic scenarios.

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