Genetic Algorithm-Based Test Parameter Optimization for ADAS System Testing

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Abstract

In this paper, we outline the use of a genetic algorithm for test parameter optimization in the context of autonomous and automated driving. Our approach iteratively optimizes test parameters to aim at obtaining critical scenarios that form the basis for virtual verification and validation of Advanced Driver Assistant Systems (ADAS). We consider a test scenario to be critical if the underlying parameter set causes a malfunction of the system equipped with the ADAS function (i.e., near crash or crash of the vehicle). For evaluating the effectiveness of our approach, we set up an automated simulation framework, where we simulated the Euro NCAP car-To-car rear scenario. To assess the criticality of each test scenario we rely on time-To-collision (TTC), which is a well-known and often used time-based safety indicator for recognizing rear-end conflicts. Our genetic algorithm approach showed a higher chance to generate a critical scenario, compared to a random selection of test parameters.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019
PublisherInstitute of Electrical and Electronics Engineers
Pages418-425
Number of pages8
ISBN (Electronic)9781728139272
DOIs
Publication statusPublished - 1 Jul 2019
Externally publishedYes
Event19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 - Sofia, Bulgaria
Duration: 22 Jul 201926 Jul 2019

Publication series

NameProceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019

Conference

Conference19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019
CountryBulgaria
CitySofia
Period22/07/1926/07/19

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Keywords

  • Automatic testing
  • Autonomous vehicles
  • Genetic algorithms
  • System verification

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

Cite this

Kluck, F., Zimmermann, M., Wotawa, F., & Nica, M. (2019). Genetic Algorithm-Based Test Parameter Optimization for ADAS System Testing. In Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 (pp. 418-425). [8854708] (Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/QRS.2019.00058