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).
Two leading experts from the Fraunhofer IKS and the University of York in the safety of complex systems have published a paper proposing a new approach to assure the safety of cognitive cyber-physical systems (CPS), including automated driving systems.
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.
