Noncontact health monitoring of infectious patient groups
Sensors can be used to monitor health data of infectious patients without contact. This minimizes the risk of infection for the nursing staff. Together with Airbus, the Fraunhofer IKS is testing an ear sensor to ensure that it is safe enough for use in hospitals.
Machine learning means a disruptive challenge for safety assurance. It’s therefore no surprise that AI safety has gained much more attention over the past few months. This article focuses on the basic understanding of what safety actually means. It highlights why it is so important to understand that safe AI has less to do with AI itself and much more to do with safety engineering.
Uncovering difficult situations for autonomous driving systems
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.
For their paper »Benchmarking Uncertainty Estimation Methods for Deep Learning With Safety-Related Metrics« Adrian Schaiger, Maximilian Henne, Karsten Roscher and Gereon Weiß received the Best Paper Award of the SafeAI Workshop in New York.