Abstract
In order to assess AI/ML-based systems in terms of safety, is it not sufficient to assure the system in terms of possible failure but also consider functional weaknesses/insufficiencies of the used algorithms according to Safety Of The Intended Functionality (SOTIF). Therefore, we introduce the concept of the so-called Component Fault and Deficiency Tree (CFDT). With this extension of the Component Fault Tree (CFT) methodology cause-effect-relationships between individual failures as well as functional insufficiencies and system hazards of the specified system can be described. Hence, it is possible to conduct safety analysis to apply for AI/ML-based systems. Thereby, we are able to show that all risks have been sufficiently mitigated and document efficiently the various mitigation schemes on different system levels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Avizienis, A., Laprie, J.C., Randell, B., Landwehr, C.: Basic concepts and taxonomy of dependable and secure computing. Dependable Secure Comput. IEEE Trans. 1(1), 11–33 (2004). https://doi.org/10.1109/TDSC.2004.2
Höfig, K., et al.: Model-based reliability and safety: reducing the complexity of safety analyses using component fault trees. In: 2018 Annual Reliability and Maintainability Symposium (RAMS) (2018)
International Organization for Standardization (ISO): ISO/PAS 21448 - road vehicles-safety of the intended functionality (2019)
International Electrotechnical Commission (IEC): IEC 60812: Analysis Techniques for System Reliability - Procedure for Failure Mode and Effects Analysis (FMEA) (1991)
International Electrotechnical Commission (IEC): IEC 61508: Functional safety of electrical/electronic/programmable electronic safety related systems (1998)
International Organization for Standardization (ISO): ISO 26262: Road vehicles - Functional safety (2011)
Kaiser, B., et al.: Advances in component fault trees. In: Proceedings of the 28th European Safety and Reliability Conference (ESREL) (2018)
Kaiser, B., Liggesmeyer, P., Mäckel, O.: A new component concept for fault trees. In: Proceedings of the 8th Australian Workshop on Safety Critical Systems and Software, pp. 37–46 (2003)
Ruijters, E., Stoelinga, M.: Fault tree analysis: a survey of the state-of-the-art in modeling, analysis and tools. Comput. Sci. Rev. 15–16, 29–62 (2015). https://doi.org/10.1016/j.cosrev.2015.03.001
Thomas, S., Groth, K.M.: Toward a hybrid causal framework for autonomous vehicle safety analysis. Proc. Inst. Mech. Eng. J. Risk Reliab. (2021). https://doi.org/10.1177/1748006X211043310
Vesely, W.E., Goldberg, F.F., Roberts, N.H., Haasl, D.F.: Fault Tree Handbook. US Nuclear Regulatory Commission (1981)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zeller, M. (2022). Component Fault and Deficiency Tree (CFDT): Combining Functional Safety and SOTIF Analysis. In: Seguin, C., Zeller, M., Prosvirnova, T. (eds) Model-Based Safety and Assessment. IMBSA 2022. Lecture Notes in Computer Science, vol 13525. Springer, Cham. https://doi.org/10.1007/978-3-031-15842-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-15842-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15841-4
Online ISBN: 978-3-031-15842-1
eBook Packages: Computer ScienceComputer Science (R0)