Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-30165Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Blattner, Timo | - |
| dc.contributor.author | Birchler, Christian | - |
| dc.contributor.author | Kehrer, Timo | - |
| dc.contributor.author | Panichella, Sebastiano | - |
| dc.date.accessioned | 2024-03-09T19:02:02Z | - |
| dc.date.available | 2024-03-09T19:02:02Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.isbn | 979-8-4007-0562-5 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/30165 | - |
| dc.description | A preprint version of this article is available at arXiv: https://doi.org/10.48550/arXiv.2401.14682 | de_CH |
| dc.description.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | ZHAW Zürcher Hochschule für Angewandte Wissenschaften | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject.ddc | 005: Computerprogrammierung, Programme und Daten | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | Diversity-guided search exploration for self-driving cars test generation through Frenet space encoding | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Informatik (InIT) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-30165 | - |
| zhaw.conference.details | 17th International Workshop on Search-Based and Fuzz Testing (SBFT), Lisbon, Portugal, 14-20 April 2024 | de_CH |
| zhaw.funding.eu | info:eu-repo/grantAgreement/EC/H2020/957254//DevOps for Complex Cyber-physical Systems/COSMOS | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.title.proceedings | 2024 ACM/IEEE International Workshop on Search-Based and Fuzz Testing (SBFT '24) | de_CH |
| zhaw.webfeed | Software Engineering | de_CH |
| zhaw.funding.zhaw | COSMOS – DevOps for Complex Cyber-physical Systems of Systems | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| 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 |
Show simple item record
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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