Artificial intelligence is becoming increasingly important in medicine. How can AI help your business and how much would it cost? Our experts for Medical AI can help you answer these questions in a free of charge consultation meeting till end of December.
Will the automobiles of the future drive autonomously? This vision of the future will become reality only when autonomous driving is safe. With this in mind, Fraunhofer IKS works on adaptive automobile software architectures.
The goal of Fraunhofer IKS is to take advantage of machine learning technologies in order to design a future that is safe. AI technologies must be 100 percent dependable, especially when it comes to safety-critical applications.
The term Industry 4.0 refers to the digitalization of production and manufacturing. In order to safely exploit the corresponding advantages, connected and automated production systems must function first and foremost in a dependable manner.
The electronics inside vehicles and machinery are growing increasingly complex. It takes more advanced safety mechanisms to tame this complexity. This is why safety engineering is a focal point of research at Fraunhofer IKS.
Smart Farming makes it possible to simultaneously render agriculture more sustainable, efficient and resilient. Fraunhofer IKS supports manufacturers of agricultural machinery, technology or software in the development of solutions for smart farming.
The 41st International Conference on Computer Safety, Reliability and Security, SafeComp, will take place next year in Munich. For its 2022 edition, the annual event has the motto »New frontiers of safety assurance«. It will be hosted by the Fraunhofer Institute for Cognitive Systems IKS and Friedrich-Alexander-Universität Erlangen-Nuremberg (FAU).
Machine Learning Methods for Enhanced Reliable Perception
As part of the ADA Lovelace Center for Analytics, Data and Applications, Fraunhofer IKS has developed a technical white paper on machine learning methods for reliable perception of autonomous systems. It reviews, develops and evaluates new methods for quantifying uncertainty in deep neural networks.
In their new paper, Franziska Schwaiger and her colleagues analyze the miscalibration of object detection models with respect to image location and box scale. Read more about the results of their experiments in »From Black-box to White-box: Examining Confidence Calibration under different Conditions«.
Collaborative driving maneuvers as a success factor for autonomous driving
For autonomous vehicles to move smoothly and safely through road traffic in the future, they must be able to communicate with other road users and with the traffic infrastructure. To support the communication, Fraunhofer IKS has developed a new approach for such hybrid perception.
A sticker on a give way sign. Branches hanging in front of a stop sign. Graffiti on a speed limit sign. These are all completely normal sights on our roads, aren’t they? But things that wouldn’t generally be a problem for people can really make life difficult for artificial intelligence.
Artificial intelligence (AI) has to be able to handle uncertainty before we can trust it to deliver in safety-critical use cases, for example, autonomous cars. The Fraunhofer Institute for Cognitive Systems IKS is investigating ways to help AI reason with uncertainty, one being the operational design domain.