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https://doi.org/10.21256/zhaw-30165| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Diversity-guided search exploration for self-driving cars test generation through Frenet space encoding |
| Authors: | Blattner, Timo Birchler, Christian Kehrer, Timo Panichella, Sebastiano |
| et. al: | No |
| DOI: | 10.21256/zhaw-30165 |
| Proceedings: | 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT '24) |
| Conference details: | 17th International Workshop on Search-Based and Fuzz Testing (SBFT), Lisbon, Portugal, 14-20 April 2024 |
| Issue Date: | 2024 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| ISBN: | 979-8-4007-0562-5 |
| Language: | English |
| Subject (DDC): | 005: Computer programming, programs and data 006: Special computer methods |
| Abstract: | The rise of self-driving cars (SDCs) presents important safety challenges to address in dynamic environments. While field testing is essential, current methods lack diversity in assessing critical SDC scenarios. Prior research introduced simulation based testing for SDCs, with Frenetic, a test generation approach based on Frenet space encoding, achieving a relatively high percentage of valid tests (approximately 50%) characterized by naturally smooth curves. The “minimal out-of-bound distance” is often taken as a fitness function, which we argue to be a sub-optimal metric. Instead, we show that the likelihood of leading to an out-of-bound condition can be learned by the deep-learning vanilla transformer model. We combine this “inherently learned metric” with a genetic algorithm, which has been shown to produce a high diversity of tests. To validate our approach, we conducted a large-scale empirical evaluation on a dataset comprising over 1,174 simulated test cases created to challenge the SDCs behavior. Our investigation revealed that our approach demonstrates a substantial reduction in generating non-valid test cases, increased diversity, and high accuracy in identifying safety violations during SDC test execution. |
| Further description: | A preprint version of this article is available at arXiv: https://doi.org/10.48550/arXiv.2401.14682 |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30165 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Published as part of the ZHAW project: | COSMOS – DevOps for Complex Cyber-physical Systems of Systems |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2024_Blattner-etal_Diversity-guided-search-exploration-SDC-test-generation.pdf | 308.74 kB | Adobe PDF |  View/Open |
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Blattner, T., Birchler, C., Kehrer, T., & Panichella, S. (2024). Diversity-guided search exploration for self-driving cars test generation through Frenet space encoding. 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT ’24). https://doi.org/10.21256/zhaw-30165
Blattner, T. et al. (2024) ‘Diversity-guided search exploration for self-driving cars test generation through Frenet space encoding’, in 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT ’24). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30165.
T. Blattner, C. Birchler, T. Kehrer, and S. Panichella, “Diversity-guided search exploration for self-driving cars test generation through Frenet space encoding,” in 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT ’24), 2024. doi: 10.21256/zhaw-30165.
BLATTNER, Timo, Christian BIRCHLER, Timo KEHRER und Sebastiano PANICHELLA, 2024. Diversity-guided search exploration for self-driving cars test generation through Frenet space encoding. In: 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT ’24). Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 2024. ISBN 979-8-4007-0562-5
Blattner, Timo, Christian Birchler, Timo Kehrer, and Sebastiano Panichella. 2024. “Diversity-Guided Search Exploration for Self-Driving Cars Test Generation through Frenet Space Encoding.” Conference paper. In 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT ’24). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30165.
Blattner, Timo, et al. “Diversity-Guided Search Exploration for Self-Driving Cars Test Generation through Frenet Space Encoding.” 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT ’24), ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2024, https://doi.org/10.21256/zhaw-30165.
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