Abstract
Engineering is based on the understanding of causes and effects. Thus, causality should also guide the safety assessment of complex systems such as autonomous driving cars. To ensure the safety of the intended functionality of these systems, normative regulations like ISO 21448 recommend scenario-based testing. An important task here is to identify critical scenarios, so-called edge and corner cases. Data-driven approaches to this task (e.g. based on machine learning) cannot adequately address a constantly changing operational design domain. Model-based approaches offer a remedy – they allow including different sources of knowledge (e.g. data, human experts) into safety considerations. With this paper, we outline a novel approach for ensuring automotive system safety. We propose to use structural causal models as a probabilistic modelling language to combine knowledge about an open-context environment from different sources. Based on these models, we investigate parameter configurations that are candidates for critical scenarios. In this paper, we first discuss some aspects of scenario-based testing. We then provide an informal introduction to causal models and relate their development lifecycle to the established V-model. Finally, we outline a generic workflow for using causal models to identify critical scenarios and highlight some challenges that arise in the process.
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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). Moreover, we thank Daniel Ebenhöch from e:fs TechHub GmbH, Tilo Linz from imbus AG, and their respective team members for valuable insights and discussions.
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Maier, R., Grabinger, L., Urlhart, D., Mottok, J. (2022). Towards Causal Model-Based Engineering in Automotive System Safety. 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_9
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