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The standardized architecture of the Fraunhofer IKS integrates any automated driving function into an existing automated verhicle.

Safety is a property of a system and its environment. That's why predicting a system's context is so important to safety engineers. However, we must acknowledge that it is impossible to sufficiently predict and specify a system's context when it must operate outside of a controlled environment. In the previous parts of this series, we've seen how we can use self-adaptivity to build systems for uncertain, dynamic contexts. In this fourth and final part, we add the missing ingredient by taking safety engineering to the next level.

In everyday life, algorithms already support us in many activities, and in healthcare, too, artificial intelligence (AI) is considered a key technology and driver for progress and development. Classic treatment paths but also diagnostics will change and the use of AI will play a crucial role in this.

Where humans get stuck, artificial intelligence can help: for example, in the treatment of patients suffering from coronary artery disease (CAD).

Compared with the human brain, artificial neural networks consume much more power — even when completing simple discrimination tasks. Event-based vision offers a promising approach to solving this problem. Yet there are still a number of challenges to overcome.

What are the basic principles of self-adaptive systems? This is the basic prerequisite for bringing the concept of resilience to life. Part 3 of our series.

Using artificial intelligence (AI) in the rail industry could potentially increase both efficiency and quality. At a workshop hosted by Fraunhofer IKS, experts from Deutsche Bahn, Siemens Mobility and the institute itself gathered to discuss the possible uses and challenges associated with AI in rail applications, as well as strategies for ensuring system safety.

If uncertainties in an autonomous system are not dealt with, they impair the system’s functionality and can lead to safety risks. In part 1 of our series on uncertainty, we presented a system for classifying uncertainties. In part 2, we will look at specific sources of uncertainty that have been noted in the relevant literature.

Resilience is the key ingredient for the next generation of cognitive cyber-physical systems. After all, many visions of the future can only become reality if the systems become aware of themselves, their goals and their context in order to be able to adapt to unknown and changing environments. And in doing so always optimize utility while preserving safety. But resilience can only be bought with increased complexity. In this article, part 2 of our series on resilience, we look at the character of complexity to better understand where to start engineering in order to manage this complexity.

Cognitive systems are often affected by the problem of uncertainty, particularly if those systems use machine learning to perceive their environments. A system’s sensors are not the only source of uncertainty, however. The life cycle of any highly complex system involves a large number of decisions that are vulnerable to the effects of uncertain knowledge. Part 1 of our series on uncertainty focuses on the latest developments in this technology.