Using ontologies for dataset engineering in automotive AI applications

Abstract

Basis of a robust safety strategy for an automated driving function based on neural networks is a detailed description of its input domain, i.e. a description of the environment, in which the function is used. This is required to describe its functional system boundaries and to perform a comprehensive safety analysis. Moreover, it allows to tailor datasets specifically designed for safety related validation tests. Ontologies fulfill the task to gather expert knowledge and model information to enable computer aided processing, while using a notion understandable for humans. In this contribution, we propose a methodology for domain analysis to build up an ontology for perception of autonomous vehicles including characteristic features that become important when dealing with neural networks. Additionally, the method is demonstrated by the creation of a synthetic test dataset for an Euro NCAP-like use case.