Ontology-based test generation for automated and autonomous driving functions

Yihao Li, Jianbo Tao, Franz Wotawa*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Context: Ontologies are known as a formal and explicit conceptualization of entities, their interfaces, behaviors, and relationships. They have been applied in various application domains such as autonomous driving where ontologies are used for decision making, traffic description, auto-pilot etc. It has always been a challenge to test the corresponding safety-critical software systems in autonomous driving that have been playing an increasingly important role in our daily routines. Objective: Failures in these systems potentially not only cause great financial loss but also the loss of lives. Therefore, it is vital to obtain and cover as many as critical driving scenarios during auto drive testing to ensure that the system can always reach a fail-safe state under different circumstances. Method: We outline a general framework for testing, verification, and validation for automated and autonomous driving functions. The introduced method makes use of ontologies for describing the environment of autonomous vehicles and convert them to input models for combinatorial testing. The combinatorial test suite comprises abstract test cases that are mapped to concrete test cases that can be executed using simulation environments. Results: We discuss in detail on how to automatically convert ontologies to the corresponding combinatorial testing input models. Specifically, we present two conversion algorithms and compare their applicability using ontologies with different sizes. We also carried out a case study to further demonstrate the practical value of applying ontology-based test generation in industrial settings. Conclusion: The proposed approach for testing autonomous driving takes ontologies describing the environment of autonomous vehicles, and automatically converts it to test cases that are used in a simulation environment to verify automated driving functions. The conversion relies on combinatorial testing. The first experimental results relying on an example from the automotive industry indicates that the approach can be used in practice.

Original languageEnglish
Article number106200
Number of pages16
JournalInformation and Software Technology
Volume117
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • Autonomous driving
  • Combinatorial testing
  • Input models
  • Ontologies
  • Test case generation

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications

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