Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems Article Swipe
Dmytro Humeniuk
,
Foutse Khomh
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2503.20642
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2503.20642
Testing autonomous robotic systems, such as self-driving cars and unmanned aerial vehicles, is challenging due to their interaction with highly unpredictable environments. A common practice is to first conduct simulation-based testing, which, despite reducing real-world risks, remains time-consuming and resource-intensive due to the vast space of possible test scenarios. A number of search-based approaches were proposed to generate test scenarios more efficiently. A key aspect of any search-based test generation approach is the choice of representation used during the search process. However, existing methods for improving test scenario representation remain limited. We propose RILaST (Representation Improvement in Latent Space for Search-Based Testing) approach, which enhances test representation by mapping it to the latent space of a variational autoencoder. We evaluate RILaST on two use cases, including autonomous drone and autonomous lane-keeping assist system. The obtained results show that RILaST allows finding between 3 to 4.6 times more failures than baseline approaches, achieving a high level of test diversity.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2503.20642
- https://arxiv.org/pdf/2503.20642
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409048151
All OpenAlex metadata
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https://openalex.org/W4409048151Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2503.20642Digital Object Identifier
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Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic SystemsWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-03-26Full publication date if available
- Authors
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Dmytro Humeniuk, Foutse KhomhList of authors in order
- Landing page
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https://arxiv.org/abs/2503.20642Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2503.20642Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2503.20642Direct OA link when available
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Representation (politics), Space (punctuation), Computer science, Artificial intelligence, Law, Political science, Operating system, PoliticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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