Ensuring the safety of Automated Driving Systems (ADSs) requires structured and transparent validation processes. Scenario-based testing has emerged as a widely adopted approach, enabling the targeted assessment of system behavior under diverse and challenging conditions. To offer a structured approach for scenario-based safety assurance, the European SUNRISE project developed the Safety Assurance Framework (SAF), which comprises stages such as scenario creation, allocation, execution, evaluation, decision-making, and in-service monitoring and reporting. Central to the SAF are scenario metrics, which quantify aspects such as coverage, criticality, and complexity and support evidence for safety cases. This paper provides a comprehensive overview of scenario-based scenario metrics relevant to ADS safety assessments. We categorize six core metric types: completeness, coverage, criticality, diversity/dissimilarity, exposure, and complexity. We explain their roles across the difference SAF components. This paper also discusses interdependencies among metrics, implementation challenges, and gaps where further research is needed, particularly in metric validation, aggregation, and standardization. By clarifying the landscape of scenario metrics and their application within the SAF, this work aims to support both practitioners and researchers in advancing scalable, data-driven safety assurance for ADSs.
Gelder et al. (Sat,) studied this question.
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