Focus projects

Here you will find a selection of our projects.

  • KI Framework
    © Shutter2U -

    The key feature of autonomous systems is that they use sensors to map their environment and can interact with it independently using actuators. For example, this paves the way for self-driving cars, robots that perform tasks autonomously, and systems that regulate themselves adaptively. Autonomous systems are made up of sensors for mapping the environment and components for the aggregation, analysis, and interpretation of data, as well as situation assessment, action planning, and actuators. A method known as deep reinforcement learning (DRL) is used to implement decision-making in autonomous systems or agents.

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  • KI bei fahrerassistenzsystemen
    © den-belitsky -

    Traction energy consumption is the primary cost factor in the electricity bills of rail transport companies, and is largely determined by how the trains are driven. Energy-efficient speed profiles can therefore lead to significant reductions in power consumption. This includes making the greatest possible use of coasting phases, during which the train consumes no energy. However, it is also vital to coordinate rail traffic with an eye to energy efficiency – for instance by avoiding excessive numbers of simultaneous departures, which result in high peak loads on the electricity grid and therefore additional charges. Moreover, it is important to synchronize arriving and departing trains so that energy recovered while one train is braking can be used to accelerate another.

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  • The availability of land is the most important factor when it comes to developing major new business locations. In reality, however, municipalities and companies wishing to locate here are struggling with an increasing shortage of land, especially in metropolitan areas. In addition, for reasons of sustainability, the net new sealing of commercial areas is to be reduced, so that in many cases new settlements can only be realized by reactivating commercial areas that are no longer used, so-called brownfields. These have further advantages: they are often already connected to local supply networks, have good infrastructure links and are easily accessible for employees. But where are these brownfields located and how many do they actually have? And what legacy issues can be expected on site? While some municipalities have a detailed insight here, other regions up to federal states have only sporadic and decentralized information on this.

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  • autonome systeme absichern
    © metamorworks -

    As progressive automation brings us closer to autonomous systems, machine learning methods for mapping and processing complex and unknown situations have become indispensable. Autonomous systems with an advanced degree of automation – i.e., highly automatic to fully autonomous systems – often employ neural networks for context recognition. Early results in the field of deep learning appear highly promising for this task, enabling driverless vehicles to recognize objects, interpret traffic events and issue driving instructions. These techniques, which are based on machine learning, can either be used system-wide, that is from the sensor to the actuator, or merely to solve individual aspects of autonomous driving.

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  • © Have a nice day /

    There are numerous commercial and open source tools on the market for ML solution development in the industry. However, the choice is complex due to application-specific constraints such as license agreements, data security and system compatibility. The integration of multiple tools for a specific functionality leads to challenges in usability, operability, maintenance and integration into the existing infrastructure. Fraunhofer IIS develops customized MLOps solutions for the supply chain that take into account the specific requirements of our industry partners. These solutions optimize the strengths of different ML tools while being easy to use and maintain.

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  • datensegementierung als KI-anwendung
    © -

    When it comes to preparing an inventory of entire vehicles, the automatic segmentation of three-dimensional datasets from X-ray computed tomography (CT) remains an unresolved challenge. Classical methods are unable to separate different parts and components into voxels and identify them with sufficient reliability. At present, this virtual »dismantling« process can only be performed manually, which is enormously expensive and time-consuming for the industry. There is, however, a great deal of interest on the part of industry, and strong demand for corresponding CT measurements there is an urgent need for solutions that can automatically break the data down into subgroups and convert the resulting volume images of individual assemblies into CAD-compatible formats.

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  • Data-centric AI

    Learning with few data or few annotated data

    Few Labels Learning
    © Fraunhofer IIS

    Die bahnbrechenden Erfolge von künstlicher Intelligenz (KI) bei Aufgaben wie Spracherkennung, Objekterkennung oder maschineller Übersetzung sind u.a. auf die Verfügbarkeit von enorm großen, annotierten Datensätzen zurückzuführen. Annotierte Daten, auch gelabelte Daten genannt, enthalten die Label-Informationen, die die Bedeutung einzelner Datenpunkte ausmachen und sind essentiell für das Training von Machine Learning Modellen. In vielen realen Szenarien, besonders im Industrieumfeld, liegen zwar oftmals große Datenmengen vor, diese sind aber nicht oder nur wenig annotiert. Dieses Fehlen annotierter Trainingsdaten stellt eine der großen Hürden für die breite Anwendung von KI-Methoden im Industrieumfeld dar. Daher wird in der Kompetenzsäule »Few Labels Learning« das Lernen mit wenig annotierten Daten innerhalb von drei Schwerpunkten und verschiedenen Bereichen erforscht: Meta-Lernstrategien, Semi-supervised Learning und Datensynthetisierung.

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