Improving the Validation of Automotive Self-Learning Systems through the Synergy of Scenario-Based Testing and Metamorphic Relations-Reference-Cited by-同舟云学术

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Improving the Validation of Automotive Self-Learning Systems through the Synergy of Scenario-Based Testing and Metamorphic Relations

Published:2023-12-04 Issue: Volume: Page:1-7
ISSN:
Container-title:Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies
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Author:

Stang Marco¹^ORCID,Sommer Martin¹^ORCID,Kraus David¹^ORCID,Sax Eric²^ORCID

Affiliation:

1. Karlsruhe Institute of Technology (KIT), Institut fuer Technik der Informationsverarbeitung (ITIV), Karlsruhe, DE

2. Karlsruhe Institute of Technology (KIT), Institut fuer Technik der Informationsverarbeitung (ITIV), Karlsruhe, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3632366.3632383

Reference11 articles.

1. Menzel et al. 2017. Szenarien für entwicklung, absicherung und test von automatisierten fahrzeugen. In 11. Workshop Fahrerassistenzsysteme. Hrsg. von Uni-DAS e. V. 125--135.

2. Machine learning testing: Survey, landscapes and horizons. In IEEE;Zhang J.M.;Trans. Softw. Eng.,2020

3. Fundamental Considerations around Scenario-Based Testing for Automated Driving

4. PEGASUS. 2018. https://www.pegasusprojekt.de/files/tmpl/PDF-Symposium/04_Scenario-Description.pdf. Accessed: 2022-09-02.

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