| Project/Area Number |
20K23334
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
1001:Information science, computer engineering, and related fields
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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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| Project Status |
Discontinued (Fiscal Year 2022)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | Automated driving / Search-based testing / Genetic algorithms / noisy systems / scenario generation / Automated Driving / Search-Based Testing / Genetic Algorithms / Noisy Systems / Scenario Generation / verification / cyber-physical systems / modeling / testing |
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| Outline of Research at the Start |
The use of "black-box" components (e.g. AI modules) adds new, unprecedented difficulties to the verification of cyber-physical systems (CPSs). Nonetheless, their use in modern applications such as autonomous vehicles demands rigorous system testing and verification. Existing verification techniques do not scale well and rather focus on verifying individual components in isolation. This project describes aims to develop an efficient approach to lifting existing verification methods to the integration level and thereby coming one step closer to CPS verification at the full system level.
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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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