Publications

The EAI IC4S international conference has awarded the prize for the best paper to Fraunhofer IKS for the paper "Concept for Safe Interaction of Driverless Industrial Trucks and Humans in Shared Areas". The topic of the paper is a concept for safe and efficient collaboration between autonomous mobile robots (AMR) and people in shared areas.

It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution. This paper explores this out-of-distribution (OOD) detection problem for image classification using clusters of semantically similar embeddings of the training data and exploit the differences in distance relationships to these clusters between in- and out-of-distribution data.

Model compression is widely employed to deploy convolutional neural networks on devices with limited computational resources or power limitations. For high stakes applications, it is, however, important that compression techniques do not impair the safety of the system. This paper investigates the changes introduced by three compression methods that go beyond the test accuracy.

Together with the University of York, Fraunhofer IKS has published a white paper presenting a new approach to assure the safety of cognitive cyber-physical systems (CPS), including automated driving systems.

How is it possible to develop safe software systems based on artificial intelligence? As simple as the question sounds, a comprehensive answer would be complex. Therefore, the white paper "Safe AI - How is it possible?" focuses on the technical design and the engineering principles of safe AI-based systems.

Together with the Huawei Research Center Munich, Fraunhofer IKS has written a white paper on Artificial Intelligence (AI) for Intelligent Transport Systems (ITS). It summarizes current and future challenges of introducing AI into Intelligent Traffic Systems in a trustworthy manner. Here, special focus is laid on Smart Cities.