Artificial intelligence (AI) is gaining attention and importance within the healthcare system. AI can help to make tedious routine tasks easier - including forecasting staffing requirements in hospitals? Fraunhofer IKS is addressing this question in a current project – and will soon present its findings at the Healthcare Hackathon in Mainz, Germany.

As an emerging technology, quantum computing is expected to unleash disruptive potential in numerous industrial application areas. Those are for example in logistics, the pharmaceutical industry and the medical sector, or in all areas where machine learning (ML) is used. In practice, however, it is clear that numerous developments still have to be made in both, the field of quantum hardware as well as the associated software.

The development of autonomous driving (AD) technologies has reached the stage where the safety of such systems is a dominating factor in defining their success. For verification and validation of autonomous vehicles in a fixed operational design domain (ODD), simulation-based testing is one of the highly recommended methods for modular testing of planning and control systems.

Quantum computing promises nothing less than to revolutionize data processing. But what exactly does this mean for a specific company and its business model? Fraunhofer IKS provides answers to this and other questions by working closely together with companies in tailored workshops.

Quantum computing still has a reputation as a very experimental cutting-edge technology. And yet, the Fraunhofer Institute for Cognitive Systems (IKS) is working on harnessing its huge potential for industry. One example of this is the QuaST project.

Modern AI models have demonstrated remarkable capabilities on various computer vision tasks such as image classification. Their decision-making process, though, remains mostly opaque (black box problem). This can be particularly problematic for safety-critical applications like medical imaging or autonomous driving. Fraunhofer IKS explores ways to increase the transparency of this black box.

Achieving safety with AI systems requires a comprehensive and collaborative approach, including technical, ethical, and regulatory considerations. A central aspect is designing AI systems that comply with safety requirements defined in constraints.

Digital twins are designed with the goal to create precise replicas of our physical world, but how exactly are they being used? For example, in medicine, vehicle construction and industrial production? Read on to find out.

The safetronic® is the industry's premier conference for the holistic safety of road vehicles. In November 2022, experts from the automotive industry met again to discuss challenges, solutions and trends. Although the safety of automated vehicles and, in particular, the associated safety assurance of AI are currently in the public spotlight, the experts at the conference had a different perspective. For them, a well-known topic is experiencing a revival and is emerging as a new trend topic: model-based system safety engineering (MBSE). Having worked on model-based safety engineering for well over a decade now, I'm personally as intrigued by this trend as I am skeptical about whether it's going to take off this time. That's reason enough for me to take a closer look at this potential trend from my individual perspective in this article.

In our previous article, we introduced some standardized terminology related to Autonomous Driving and Operational Design Domain (ODD). In this article, we look at the safety implications of transitioning through multiple ODDs while executing the Dynamic Driving Task (DDT).