Category Archives: Teams

Autonomous Driving


The team Autonomous Driving (AD) team conducts research on AI-based environment perception and trajectory planning for autonomous vehicles (Level 4 >).

We consider both subsymbolic AI-techniques used in learning systems (machine learning, deep learning) as well as symbolic techniques such as reasoning or constraint-based methods.

With respect to learning systems, we work with both real and synthetic data in an idealized implementation of the Digital Reality Principle, which is the thematic guideline of the research area ASR.

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The team Agents  investigates these concepts in real-world (commercial) applications as well as in virtual environments. It concentrates on agent-based modelling and software development as well as on specific technologies for the design of agent behaviour. More recently a stronger focus was set on motion modelling and synthesis for agent design in 3D simulations. In this context learning techniques with statistical models and neuronal networks (deep learning) are investigated. Continue reading Agents

Distributed and Web-based Systems

Our research is focused on the enablement of seamless cross-domain interoperability between independently developed systems and applications in complex, dynamic, real-world environments.

In this respect, we envision the Web as a technology convergence platform and are interested in integration techniques that emphasize serendipitous reuse of data and functionality.

We are exploring how to use a Web-centric abstraction layer for platforms, protocols, communication patterns and data models, resulting in decentralized architectures that provide different kinds of interfaces with varying expressivity and enable dynamic vertical interface orientation for complex intersystem interactions.

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Computational 3D Imaging

Team Computational 3D Imaging

The team conducts bleeding edge research on algorithmic questions with different applications in microscopy and three-dimensional imaging. The broader research question is: how can one obtain a maximum of information about a sample? Hereby, we consider several aspects of 3D imaging. How can we incorporate different kind of prior knowledge about the microscope, the specimen, or the physics of image formation into reconstruction algorithms to obtain better reconstructions from existing projections? What are optimal recording schemes to obtain 3D information? Specifically, how can adaptive sampling schemes augment the information content of a dataset already during the acquisition? And finally: how can simulations be used to make  physical quantities that can not easily be observed directly, appear in a three-dimensional dataset. For example, can we use finite element simulations to enhance a tomogram with residual stress vectors?

Scanning Electron Microscope image of cast iron acquired using an adaptive scanning method.

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Smart Living

Team Smart Living

The team focusses on application-oriented research based on the technologies developed in the department Agents & Simulated Reality. It supports business processes in complex industrial settings with an intelligent mix of latest research results from the area of interactive visualization and artificial intelligence. Our research is not limited to a specific technology or business sector but tries to solve relevant cross-section business problems. At the moment, we are focussing on topics like agent-based simulation and interactive 3D visualization in different application domains like smart factories and smart buildings.

Motion Synthesis in industrial environments
Motion Synthesis in industrial environments

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