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Machine Learning-Based Fault Injection for Hazard Analysis and Risk Assessment

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Computer Safety, Reliability, and Security (SAFECOMP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12852))

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Abstract

Current automotive standards such as ISO 26262 require Hazard Analysis and Risk Assessment (HARA) on possible hazards and consequences of safety-critical components. This work attempts to ease this labour-intensive process by using machine learning-based fault injection to discover representative hazardous situations. Using a Simulation-Aided Hazard Analysis and Risk Assessment (SAHARA) methodology, a visualisation and suggested hazard classification is then presented for the safety engineer. We demonstrate this SAHARA methodology using machine learning-based fault injection on a safety-critical use case of an adaptive cruise control system, to show that our approach can discover, visualise, and classify hazardous situations in a (semi-)automated manner in around twenty minutes.

This work was partly funded by Flanders Make vzw, the strategic research centre for the Flemish manufacturing industry; and by the aSET project (grant no. HBC.2017.0389) of the Flanders Innovation and Entrepreneurship agency (VLAIO).

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Notes

  1. 1.

    Other approaches such as [13, 14] refer to a SAHARA approach. This paper uses SAHARA solely to refer to the methodology of [21].

  2. 2.

    The model is an adapted version of https://www.mathworks.com/help/mpc/ug/adaptive-cruise-control-using-model-predictive-controller.html.

  3. 3.

    Friction values sourced from Fig. 24 of Singh and Taheri [28].

  4. 4.

    https://github.com/nickovic/rtamt.

  5. 5.

    https://www.youtube.com/playlist?list=PLNyNvnuIvPKvsmUT1I-hwEYDMyZ7YGZkA.

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Authors and Affiliations

  1. University of Antwerp, Antwerp, Belgium

    Bentley James Oakes, Mehrdad Moradi, Simon Van Mierlo, Hans Vangheluwe & Joachim Denil

  2. Flanders Make vzw, Lommel, Belgium

    Bentley James Oakes, Mehrdad Moradi, Simon Van Mierlo, Hans Vangheluwe & Joachim Denil

Authors
  1. Bentley James Oakes
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  2. Mehrdad Moradi
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  3. Simon Van Mierlo
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  4. Hans Vangheluwe
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  5. Joachim Denil
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Correspondence to Mehrdad Moradi .

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Editors and Affiliations

  1. University of York, York, UK

    Ibrahim Habli

  2. Human Factors Everywhere Ltd., Woking, UK

    Mark Sujan

  3. Thales Deutschland GmbH, Ditzingen, Germany

    Friedemann Bitsch

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Oakes, B.J., Moradi, M., Van Mierlo, S., Vangheluwe, H., Denil, J. (2021). Machine Learning-Based Fault Injection for Hazard Analysis and Risk Assessment. In: Habli, I., Sujan, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2021. Lecture Notes in Computer Science(), vol 12852. Springer, Cham. https://doi.org/10.1007/978-3-030-83903-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-83903-1_12

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