# Ontology based Scene Creation for the Development of Automated Vehicles

## TL;DR

This paper proposes using ontologies and natural language generation to create diverse traffic scenarios for testing automated vehicles, addressing the limitations of expert-driven scenario identification.

## Contribution

It introduces an ontology-based approach combined with natural language generation for comprehensive traffic scene creation in automated vehicle development.

## Key findings

- Ontology-based scene generation broadens scenario diversity.
- Natural language descriptions facilitate scenario analysis.
- Method supports economical and thorough testing processes.

## Abstract

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.