Approach for Argumenting Safety on Basis of an Operational Design Domain

• Published in: 2024 IEEE/ACM 3rd International Conference on AI Engineering – Software Engineering for AI (CAIN) pp. 184 - 193
• Main Authors: Weiss, Gereon, Zeller, Marc, Schoenhaar, Hannes, Fraunhofer, Christian Drabek, Kreutz, Andreas
• Format: Conference Proceeding
• Language: English
• Published: ACM 14.04.2024
• Subjects: Artificial intelligence; Automated Train Operation; Autonomous System; Buildings; ISO Standards; Machine Learning; Maintenance; Operational Design Domain; Rail transportation; Safety; Safety Assurance; Standardization
• DOI: 10.1145/3644815.3644944

The Operational Design Domain (ODD) is a representative model of the real world in which an Automated Driving System (ADS) is intended to operate. The definition of the ODD is a crucial part of the development process for such an artificial intelligence (AI)-enabled system. This is due to the fact that the ODD is the basis for several critical development activities, like defining system-level requirements, test & verification, and building a well-founded safety case for an AI-based ADS. Since an inadequately defined ODD poses a major safety concern for the entire development, an ODD must be defined completely and consistently during the development process. In this work, we present an approach for the ODD definition and maintenance during the development of safety-critical AI-based ADS functionalities and provide evidences to argue the sufficient completeness and consistency. We demonstrate the feasibility of our approach by an industrial use case of a fully automated system in the railway domain.