Robustness Requirement Coverage using a Situation Coverage Approach for Vision-based AI Systems

Abstract: AI-based robots and vehicles are expected to operate safely in complex and dynamic environments, even in the presence of component degradation. In such systems, perception relies on sensors such as cameras to capture environmental data, which is then processed by AI models to support decision-making. However, degradation in sensor performance directly impacts input data quality and can impair AI inference. Specifying safety requirements for all possible sensor degradation scenarios leads to unmanageable complexity and inevitable gaps. In this position paper, we present a novel framework that integrates camera noise factor identification with situation coverage analysis to systematically elicit robustness-related safety requirements for AI-based perception systems. We focus specifically on camera degradation in the automotive domain. Building on an existing framework for identifying degradation modes, we propose involving domain, sensor, and safety experts, and incorporating Operational Design Domain specifications to extend the degradation model by incorporating noise factors relevant to AI performance. Situation coverage analysis is then applied to identify representative operational contexts. This work marks an initial step toward integrating noise factor analysis and situational coverage to support principled formulation and completeness assessment of robustness requirements for camera-based AI perception.