Today, autonomous vehicles function reasonably well in test situations since the conditions are severely restricted and thus easy to manage. A key issue however is how to design autonomous vehicles so that they operate dependably even in complex and previously unknown situations. A solution from Fraunhofer IKS is helping to uncover and predict such difficult situations.

In autonomous systems or medical engineering, uncertainty in machine learning (ML) processes needs to be reliably determined. That is why we are comparing four state-of-the-art methods of quantifying the uncertainty of deep neural networks (DNNs) in the context of safety-critical systems — so that the AI can learn when there is scope for doubt.

Complex machine learning (ML) processes present researchers with a problem: Eliminating errors before autonomous systems are put into operation and reliably identifying errors at runtime are laborious and challenging processes. At present, this severely restricts the use of machine learning in safety-critical systems and necessitates new approaches in order to benefit from the advantages offered by ML in these areas in the future.

In this interview apl. Prof. Dr. habil. Mario Trapp, Director of the Fraunhofer Institute for Cognitive Systems IKS, talks about the competencies of the institute that make cognitive systems safe.

Dr. Sabine Sickinger, Director of Organizational Strategy and Administration at the Fraunhofer Institute for Cognitive Systems IKS, talks in the following interview about how dynamic research work succeeds through agile project organization.