2022 Fiscal Year Annual Research Report
Surrogate Model-based Integration Testing of CPS with Complex Black Box Components
| Project/Area Number |
20K23334
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
KLIKOVITS Stefan 国立情報学研究所, アーキテクチャ科学研究系, 特任研究員 (10875983)
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| Project Period (FY) |
2020-09-11 – 2023-03-31
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| Keywords | Automated driving / Search-based testing / Genetic algorithms / noisy systems / scenario generation |
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| Outline of Annual Research Achievements |
In the course of the project, I primarily investigated scenario generation for ADS. Specifically, I identified a major issue with noisy/non-deterministic simulators such as Autonomoose. To overcome, I developped kNN-Averaging, a search-based algorithm that improves current scenario heuristics.
Furthermore, I investigated the complexity of scenario generation and managed to proposed a hierarchical scenario definition framework, formalising the this complexity.
Next, together with colleagues, we participated in two editions of the SBST CPS challenge, where we developed and submitted Frenetic, one of the top-performing competitors.
We are in preparation of a empirical study of scenario/road diversity and its impact on ADS behaviour diversity.
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