Published August 10, 2023 | Version v1
Conference paper Open

SOTIF-Compliant Scenario Generation Using Semi-Concrete Scenarios and Parameter Sampling

  • 1. IMC University of Applied Sciences Krems: Krems, AT
  • 2. ROR icon Karlsruhe Institute of Technology
  • 3. Technical University Braunschweig
  • 4. FZI Forschungszentrum Informatik

Description

The SOTIF standard (ISO 21448) requires scenario based testing to verify and validate Advanced Driver Assistance Systems and Automated Driving Systems but does not suggest any practical way to do so effectively and efficiently. Existing scenario generation approaches either focus on exploring or exploiting the scenario space. This generally leads to test suites that cover many known cases but potentially miss edge cases or focused test suites that are effective but also contain less diverse scenarios. To generate SOTIF-compliant test suites that achieve higher coverage and find more faults, this paper proposes semi-concrete scenarios and combines them with parameter
sampling to adequately balance scenario space exploration and exploitation. Semi-concrete scenarios enable combinatorial scenario generation techniques that systematically explore the scenario space, while parameter sampling allows for the exploitation of continuous parameters. Our experimental results show that the proposed concept can generate more effective test suites than state-of-the-art coverage-based sampling. Moreover, our results show that including a feedback mechanism to drive
parameter sampling further increases test suites’ effectiveness.

Notes

The Flexcrash project has received funding from the Horizon Europe programme under grant agreement No. 101069674. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them.

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Additional details

Funding

European Commission
FLEXCRASH – Flexible and hybrid manufacturing of green aluminium to produce tailored adaptive crash-tolerant structures 101069674