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The verification and validation of automated driving systems at SAE levels 4and 5 is a multi-faceted challenge for which classical statisticalconsiderations become infeasible. For this, contemporary approaches suggest adecomposition into scenario classes combined with statistical analysis thereofregarding the emergence of criticality. Unfortunately, these associationalapproaches may yield spurious inferences, or worse, fail to recognize thecausalities leading to critical scenarios, which are, in turn, prerequisite forthe development and safeguarding of automated driving systems. As toincorporate causal knowledge within these processes, this work introduces aformalization of causal queries whose answers facilitate a causal understandingof safety-relevant influencing factors for automated driving. This formalizedcausal knowledge can be used to specify and implement abstract safetyprinciples that provably reduce the criticality associated with theseinfluencing factors. Based on Judea Pearl's causal theory, we define a causalrelation as a causal structure together with a context, both related to adomain ontology, where the focus lies on modeling the effect of suchinfluencing factors on criticality as measured by a suitable metric. As toassess modeling quality, we suggest various quantities and evaluate them on asmall example. As availability and quality of data are imperative for validlyestimating answers to the causal queries, we also discuss requirements onreal-world and synthetic data acquisition. We thereby contribute toestablishing causal considerations at the heart of the safety processes thatare urgently needed as to ensure the safe operation of automated drivingsystems.

Grasping Causality for the Explanation of Criticality for Automated Driving