Abstract
Development and verification of modern, dependable automotive systems require appropriate modelling approaches. Classic automotive safety is described by the normative regulations ISO 26262, its relative ISO/PAS 21448, and their respective methodologies. In recent publications, an emerging demand to combine environmental influences, machine learning, or reasoning under uncertainty with standard-compliant analysis techniques can be noticed. Therefore, adapting established methods like FTA and proper tool support is necessary. We argue that Bayesian Networks (BNs) can be used as a central component to address and merge these demands. In this paper, we present our Open-Source Python package BayesianSafety. First, we review how BNs relate to data-driven methods, model-to-model transformations, and causal reasoning. Together with FTA and ETA, these models form the core functionality of our software. After describing currently implemented features and possibilities of combining individual modelling approaches, we provide an informal view of the tool’s architecture and of the resulting software ecosystem. By comparing selected publicly available safety and reliability analysis libraries, we outline that many relevant methodologies yield specialized implementations. Finally, we show that there is a demand for a flexible, unifying analysis tool that allows researching system safety by using multi-model and multi-domain approaches.
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References
Ankan, A., Panda, A.: pgmpy: probabilistic graphical models using Python. In: Proceedings of the 14th Python in Science Conference (SCIPY 2015). Citeseer (2015)
Avizienis, A., Laprie, J.C., Randell, B.: Fundamental concepts of dependability. Technical report series. Department of Computing Science (2001)
Bearfield, G., Marsh, W.: Generalising event trees using Bayesian networks with a case study of train derailment. In: Winther, R., Gran, B.A., Dahll, G. (eds.) SAFECOMP 2005. LNCS, vol. 3688, pp. 52–66. Springer, Heidelberg (2005). https://doi.org/10.1007/11563228_5
Bobbio, A., Portinale, L., Minichino, M., Ciancamerla, E.: Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab. Eng. Syst. Saf. 71(3), 249–260 (2001). https://doi.org/10.1016/S0951-8320(00)00077-6
Cai, B., Liu, Y., Liu, Z., Chang, Y., Jiang, L.: Bayesian Networks for Reliability Engineering. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-6516-4
Ducamp, G., Gonzales, C., Wuillemin, P.H.: aGrUM/pyAgrum: a toolbox to build models and algorithms for Probabilistic Graphical Models in Python. In: 10th International Conference on Probabilistic Graphical Models. Proceedings of Machine Learning Research, Skørping, Denmark, vol. 138, pp. 609–612, September 2020. https://hal.archives-ouvertes.fr/hal-03135721
Epstein, S., Rauzy, A., Reinhart, F.: The open PSA initiative for next generation probabilistic safety assessment. Kerntechnik 74, 101–105 (2009). https://doi.org/10.3139/124.110020
Feth, P., et al.: Multi-aspect safety engineering for highly automated driving. In: Gallina, B., Skavhaug, A., Bitsch, F. (eds.) SAFECOMP 2018. LNCS, vol. 11093, pp. 59–72. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99130-6_5
Hagberg, A., Swart, P., Chult, D.S.: Exploring network structure, dynamics, and function using NetworkX. Technical report, Los Alamos National Lab. (LANL), Los Alamos, NM, United States (2008)
Khakzad, N., Khan, F., Amyotte, P.: Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf. Environ. Prot. 91(1), 46–53 (2013). https://doi.org/10.1016/j.psep.2012.01.005
Kirchhof, M., Haas, K., Kornas, T., Thiede, S., Hirz, M., Herrmann, C.: Root cause analysis in lithium-ion battery production with FMEA-based large-scale Bayesian network. arXiv:2006.03610 [stat], June 2020. https://doi.org/10.20944/preprints202012.0312.v1
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. Adaptive Computation and Machine Learning, MIT Press, Cambridge (2009)
Mosleh, A., Dias, A., Eghbali, G., Fazen, K.: An integrated framework for identification, classification, and assessment of aviation systems hazards. In: Spitzer, C., Schmocker, U., Dang, V.N. (eds.) Probabilistic Safety Assessment and Management, pp. 2384–2390. Springer, London (2004). https://doi.org/10.1007/978-0-85729-410-4_383
Nešić, D., Nyberg, M., Gallina, B.: A probabilistic model of belief in safety cases. Saf. Sci. 138, 105187 (2021). https://doi.org/10.1016/j.ssci.2021.105187
Pearl, J.: Causality: Models, Reasoning and Inference, 2nd edn. Cambridge University Press, Cambridge (2009)
Rudolph, A., Voget, S., Mottok, J.: A consistent safety case argumentation for artificial intelligence in safety related automotive systems. In: ERTS 2018: 9th European Congress on Embedded Real Time Software and Systems (ERTS 2018), Toulouse, France, January 2018
Schölkopf, B., et al.: Toward causal representation learning. Proc. IEEE 109, 612–634 (2021). http://arxiv.org/abs/2102.11107
Thomas, S., Groth, K.: Toward a hybrid causal framework for autonomous vehicle safety analysis. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. (2021). https://doi.org/10.1177/1748006X211043310
Vowels, M.J., Camgöz, N.C., Bowden, R.: D’ya like DAGs? A survey on structure learning and causal discovery. CoRR abs/2103.02582 (2021). https://arxiv.org/abs/2103.02582
Zurheide, F.T., Hermann, E., Lampesberger, H.: pyBNBowTie: Python library for bow-tie analysis based on Bayesian networks. Procedia Comput. Sci. 180, 344–351 (2021). https://doi.org/10.1016/j.procs.2021.01.172. Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)
Acknowledgment
The present paper is supported by Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie through the granting of the funding project \( HolmeS^{3} \) (FKZ: DIK0173/03). We thank L. Grabinger and D. Urlhart for valuable discussions.
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Maier, R., Mottok, J. (2022). BayesianSafety - An Open-Source Package for Causality-Guided, Multi-model Safety Analysis. In: Trapp, M., Saglietti, F., Spisländer, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2022. Lecture Notes in Computer Science, vol 13414. Springer, Cham. https://doi.org/10.1007/978-3-031-14835-4_2
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