Diversity-guided Search Exploration for Self-driving Cars Test Generation through Frenet Space Encoding

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Bibliographic Details
Main Authors:	Blattner, Timo, Birchler, Christian, Kehrer, Timo, Panichella, Sebastiano
Format:	Preprint
Published:	2024
Subjects:	Software Engineering
Online Access:	Acceder al recurso
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_version_	1866911764952645632
author	Blattner, Timo Birchler, Christian Kehrer, Timo Panichella, Sebastiano
author_facet	Blattner, Timo Birchler, Christian Kehrer, Timo Panichella, Sebastiano
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2401_14682
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Diversity-guided Search Exploration for Self-driving Cars Test Generation through Frenet Space Encoding Blattner, Timo Birchler, Christian Kehrer, Timo Panichella, Sebastiano Software Engineering 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.
title	Diversity-guided Search Exploration for Self-driving Cars Test Generation through Frenet Space Encoding
topic	Software Engineering
url	https://arxiv.org/abs/2401.14682