Focus projects

Here you will find a selection of our projects.

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  • KI Framework
    © Shutter2U - stock.adobe.com

    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 - iStock.com

    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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  • autonome systeme absichern
    © metamorworks - stock.adobe.com

    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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  • datensegementierung als KI-anwendung
    © vegefox.com - stock.adobe.com

    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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  • datengetriebene lokalisierung von objekten
    © Unique Vision - stock.adobe.com

    As a fundamental requirement for many applications in logistics and Industry 4.0, object positioning is an area in which Fraunhofer IIS has not only considerable expertise but also many years of experience. Applications frequently rely on radio-based positioning solutions, which typically measure the transit time of radio signals from mobile objects. To a large extent, the accuracy of the positioning system depends on the optimization of fusion and system parameters in different and sometimes changing environments. Fraunhofer IIS is therefore working to develop more robust positioning methods.

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  • Foto zu Digitalisierte innerbetriebliche Transporte
    © Kzenon - Fotolia

    At most companies, efficiency assessment of in-house transport using industrial trucks is performed manually, with sub-optimal results. We are tapping into this potential for optimization by developing »intelligence« for industrial trucks that enables analysis of individual intralogistics transport processes.

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  • sportdaten tracken
    © Sergey Nivens - stock.adobe.com

    The task of finding decisive – or otherwise interesting – scenes in the tracking data recorded during games such as soccer or ice hockey, evaluating them, and presenting potential solutions for situations that arise during the game, is a fundamental task for game analysis in sports clubs and associations, and for media coverage of sporting events. The current standard procedure involves manual entry of appropriate labels during a game, or time-consuming searches of video footage after the event.

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  • Foto vom Demonstrator zu Fehlerfreies Kommissionieren
    © Fraunhofer IIS/Ibrahim Ibrahim

    Every false move in manual order picking leads to extra work, but assistance systems that reduce the burden on pickers can be very complex to install. We are therefore developing a versatile wireless information system for order pickers as well as technology for the integrated validation of picking processes.

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  • KI in der Medizin und Automotive
    © science photo/nd3000 - stock.adobe.com

    When it comes to the subject of machine learning and deep neural networks (DNNs), there are usually more questions than answers, as data analysis by ML-based systems remains an enigmatic process for developers and users alike. Nevertheless, it is vital that the systems provide transparency and interpretability – particularly with regard to safety issues in the automotive sector, such as driver drowsiness detection, or in medicine, with automated screening of tissue samples. The use of automatic processes in critical strategic decision-making is dependent on the explainability of data analysis, which also underpins the general acceptance of these processes.

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