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With the Fraunhofer IKS Playground, the institute offers access to experimental prototypes and innovative concepts from cutting-edge research. The Fraunhofer IKS Playground invites you to learn about new approaches, experiment with our prototypes and tools, and test ideas freely.
Visit the Fraunhofer IKS Playground and immerse yourself in the research approaches and solutions of Fraunhofer IKS!
If you have any questions or require further information, please do not hesitate to contact us. We also greatly appreciate your feedback.
And, of course, we will support you in applying our approaches in your company.
Here you will find the latest research concepts, prototypes and tools. And development continues. We are currently working on further concepts, which you will soon be able to experience on the Fraunhofer IKS Playground.
Here we present the concepts, prototypes and tools currently available:
The Fuzzy Cause Tree (FCT) allows you to analyze cause-and-effect relationships between triggering conditions and system failures with greater precision, especially in the face of uncertainties. The Fuzzy Cause Tree (FCT) is an innovative approach developed by Fraunhofer IKS that significantly reduces risks and improves the safety of the intended functionality (SOTIF).
The Fuzzy Cause Tree method uses the principles of fuzzy logic to manage the complexity of real-world systems. Unlike traditional logic, which is limited to absolute true or false values, fuzzy logic allows for a differentiated assessment of truth values on a spectrum. This makes it possible to capture uncertainties and vague conditions more effectively and make smarter decisions.
The Fuzzy Cause Tree offers a flexible and intuitive approach to modeling complex causal relationships that goes beyond the limitations of conventional methods. It enables a better understanding of risks and promotes informed decisions in safety-critical systems, especially in the automotive industry.
Robuscope, an application developed by Fraunhofer IKS, allows you to easily evaluate the reliability of your AI models. With just one click, you can gain valuable insights into the robustness and performance of your AI systems.
Robuscope is a self-service platform that answers the question: "Can I trust my AI?" Robuscope provides you with detailed information on potential improvements to ensure that your AI models are reliable, efficient, and ready for use in safety-critical applications.
With Robuscope, the days of AI black boxes are over. Robuscope enables you to address potential issues early on and increase the trustworthiness of your AI applications. This allows you to minimize risks and maximize performance.
The Fraunhofer IKS Safety Assistant is an innovative front-end solution that optimizes safety engineering tasks through the use of generative AI (GenAI). Given the increasing complexity of software development for safety-critical systems, AI-powered assistants can provide support in overcoming these challenges.
The Safety Assistant leverages the capabilities of large language models to bridge the gap between technological progress and safety assurance. To do this, it automates essential aspects of the safety engineering workflow, thereby changing the approach to safety engineering.
The Fraunhofer IKS Safety Assistant enables you to master the complexity of software development for safety-critical systems. This allows you to bridge the gap between innovation and safety.
With the Fraunhofer IKS FAST Framework, you can effectively and efficiently handle decision-making tasks driven by visual information such as quality inspections or medical diagnoses – even when only limited data is available.
FAST stands for "Feedback-guided Automation of Sub-tasks." The framework improves decision-making through the use of advanced automation techniques. To do this, FAST identifies sub-tasks that can be automated, while the remaining more complex tasks are delegated to experts.
The FAST framework enables increased operational efficiency, reduced errors, and improved overall productivity by combining the strengths of automation with human expertise. This hybrid approach not only speeds up decision-making but also ensures high-quality results.
With Fraunhofer IKS's AS-PHALT proof of concept, you can use the power of AI-driven large language models (LLMs) to perform hazard analysis and risk assessment (HARA). This enables you to use resources more efficiently and improve the reliability of safety assessments.
AS-PHALT stands for “Automation Systems – Pro Human Assistant LLM Tool.” Using the example of hazard analysis and risk assessment, we illustrate how AI can help identify and evaluate hazards through an interactive, user-friendly interface. The concept aims to optimize the hazard analysis process through innovative automation techniques.
The AS-PHALT concept makes it possible to increase efficiency in safety technology and reduce the complexity of safety assessments in automated applications. The use of generative AI makes safety assessment more agile and innovative.
Introducing the Enhanced CADx Framework, an advanced computer-aided diagnosis (CADx) system developed to empower clinicians with AI support that is transparent, interpretable, and easy to validate. Moving beyond black-box diagnostics, this framework provides multi-layered explanations – both visual and concept-based – through a single intuitive interface.
The Enhanced CADx Framework bridges the gap between clinical expertise and artificial intelligence by making AI reasoning clear, explainable, and clinically meaningful. Rather than providing opaque outputs, the system enables clinicians to explore how and why the AI arrived at its diagnosis –supporting trust, efficiency, and collaboration in healthcare workflows.
Traditional CADx systems often operate as “black boxes”, offering limited insight into their diagnostic rationale. The Enhanced CADx Framework eliminates this opacity, reducing the need for manual validation and strengthening clinician confidence in AI-assisted healthcare. By revealing the logic behind AI diagnoses, the system transforms AI from a mere analytical tool into a trusted, collaborative partner in patient care.
Analyze and enhance safety in industrial workspaces efficiently and pragmatically. Gain reliable insights into the interaction between humans and machines and establish a solid foundation for safe, productive processes.
With Safety Net, we support organizations in deploying AI-based person detection in safety-critical industrial applications and systematically reducing operational risks.
The Safety Net approach leverages modern artificial intelligence methods to reliably detect people in industrial environments and assess safety-relevant situations at an early stage. Instead of relying solely on rigid protective measures, Safety Net enables a context-aware and situation-dependent evaluation of risks.
By taking real-world operating conditions into account, uncertainties can be captured more effectively, and safety functions can be applied precisely where they are needed.
Safety Net offers a flexible and transparent approach to safeguarding human–machine interactions in industrial environments. By combining AI-driven perception with structured safety reasoning, risks can be better understood and well-informed decisions can be made.
Especially in dynamic and automated industrial settings, Safety Net helps organizations balance safety and productivity while enabling the responsible use of artificial intelligence.
With our Safety Assurance approach, you can analyze and substantiate the safety of AI-based vehicle dynamics functions throughout the entire development process – even under uncertain conditions and in open operating environments. The approach was developed at Fraunhofer IKS and supports you in reducing risks and providing a sound safety argument for the use of intelligent systems.
The Safety Assurance approach extends established development processes in a targeted manner to make them suitable for the use of artificial intelligence. Instead of addressing safety only at selected points, it is considered systematically across all phases – from analysis and design to verification and validation.
This makes it possible to structure and justify even complex interactions between AI, the system, and its environment in a transparent and comprehensible way.
The Safety Assurance approach provides a clear and comprehensible framework for assuring complex AI systems. It helps developers identify risks at an early stage, implement targeted safety measures, and convincingly argue system safety even in open environments. Especially in safety-critical domains such as vehicle dynamics, this approach builds trust and supports confident decision-making.
Quantum-enhanced artificial intelligence opens up new possibilities for more precise and efficient diagnostics in medical imaging. Our approach combines classical AI methods with quantum technologies to systematically address existing limitations—particularly in scenarios with limited training data. This work is developed at the Fraunhofer Institute for Cognitive Systems IKS with the goal of enabling powerful and trustworthy AI solutions for medical applications.
Quantum-enhanced AI leverages hybrid quantum–classical algorithms that combine the strengths of both computing paradigms. By integrating quantum-specific computational operations into classical AI models, complex patterns in medical imaging data can be captured more effectively.
This approach opens up new perspectives for the use of AI in medical diagnostics—even under challenging conditions such as limited data availability.
The use of AI in medical imaging comes with high requirements for accuracy, reliability, and data quality, while large, well-annotated datasets are often difficult to obtain. Quantum-enhanced AI offers a forward-looking approach to systematically address these challenges and unlock new performance potential for medical diagnostic systems.
The Quantum Zoo offers an intuitive and interactive entry point into the world of quantum computing. The approach makes it possible to understand fundamental concepts and mechanisms of quantum-based computation and to explore their potential for real-world applications. The Quantum Zoo is developed at Fraunhofer IKS with the goal of making complex quantum technologies tangible and accessible.
The Quantum Zoo is a playful, interactive environment for demonstrating gate-based quantum computing. It illustrates how quantum operations act on quantum states that can represent more than one classical state at the same time.
By using suitable algorithms, it demonstrates how quantum parallelism can be leveraged to perform computations that can theoretically outperform classical computing methods exponentially.
Quantum computing holds enormous promise, but it also presents significant challenges—particularly when mapping real-world problems onto efficient quantum algorithms. The Quantum Zoo provides a low-threshold yet technically sound introduction to this field. It helps users gain a better understanding of how quantum-based systems work and to realistically assess the limitations of current quantum hardware.
Fraunhofer Institute for Cognitive Systems IKS