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Rasmus Adler

Rasmus Adler has been involved in the safety of software-intensive systems since 2006. He initially received his doctorate as a scholarship holder on the topic of "Safety of Adaptive Systems". He then spent several years supporting safety engineering in industrial and research projects and became familiar with safety standards from various domains ("automotive", "automation technology", "railway technology", "medical technology"). In particular, he supported projects with model-based representations of safety artifacts such as hazard and risk analyses, fault tree analyses or safety concepts and their integration into the company-specific model-based development process. Today he leads various projects in the field of safety engineering and researches suitable model-based solutions for the safety engineering of highly automated / autonomous and networked systems".


AI-based generation of safety-critical scenarios for automated driving

Proof of functional safety for automated driving must be based on evidence from virtual validation, as the necessary evidence cannot be generated with test drives solely. A challenge is generating relevant test cases, as there are infinite simulation scenarios and variations. RevoAI and Fraunhofer IESE present a solution to find relevant and previously unknown scenarios efficiently. They have developed an AI that learns the behavior of the test subject of an automated driving function in its environment during simulation and generates challenging and critical scenarios for the test subject. This approach allows an efficient safety evaluation of the test subject.