Fraunhofer Institute for Cognitive Systems IKS

This article 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.

A solution from Fraunhofer IKS is helping to uncover and predict difficult and unknown situations for autonomous vehicles:

At the virtual Fraunhofer Solution Days Philipp Schleiß will present a new approach for validating AI in automated vehicles.

How can you develop and evaluate dependable cloud-based systems? Learn more in our workshop.

How can we design conntected autonomous systems to simultaneously provide resilience, intelligence and flexibility?

Together with Continental, Fraunhofer IKS was able to create a concept for the dynamic distribution of vehicle functions and develop a technical safety concept that describes an implementation of the identified safety requirements.

Reconciling safety, performance and reliability in cloud-based cognitive systems is no easy task. Especially in safety-critical autonomous systems such as driverless cars, expectations rightly assume that all three requirements are implemented at the highest level. The Fraunhofer IKS has developed an new approach to solve the challenge: flexilience.

Deep Neural Networks have proven their potential. This applies above all to their use in the laboratory. Their performance in real-world applications leaves a lot to be desired. One major problem is the estimation of uncertainty. A closer look at methods that meet this challenge.

Addressing the challenges in dependable cloud-based cyber-physical systems of systems (CPSoS) requires new approaches that are automated and efficient enough to shift some of the system’s elements to runtime. To design and evaluate the system architecture, Fraunhofer IKS has developed an iterative and automated process.

To determine a system’s dependability, scientists at Fraunhofer IKS are developing an adaptive, dynamic and flexible safety analysis process that overcomes the limitations of current methods.