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BayWISS-Kolleg Digitalisation www.baywiss.de

Our Doctoral Projects Digitalisation

© Alexander Debieve

Robuste Prädiktion von Verkehrsteilnehmern in einer urbanen Umgebung



Supervisor Technische Hochschule Deggendorf:

Prof. Dr.-Ing. Nikolaus Mueller

  • Autonomes Fahren, vor allem modellbasierter Ansatz
  • Ansteuerverfahren von mehrphasigen elektrischen Maschinen

Robuste Prädiktion von Verkehrsteilnehmern in einer urbanen Umgebung

Supervisor Universität Regensburg:
Stefan Kerscher

Stefan Kerscher

Technische Hochschule Deggendorf

Intention Recognition and Prediction of Vulnerable Traffic Participants

Understanding the intentions of traffic participants and predicting their future motion is an important task in the domain of autonomous driving. The knowledge of the future states of the traffic participants enriches the internal environment model of the car and supports the planning of the own motion. The better the current situation is understood, the more safety critical situations can be avoided by the autonomous vehicle.

For predicting non-holonomic and inert objects like cars, motion models can give us a good short-term prediction. Including environment models and the fact that vehicles are mostly bound to streets, we can shrink the solution space and conclude for future maneuvers. Doing this for pedestrians which have more degrees of freedom in their motion might require additional features. This work includes the research on suitable feature sets for predicting vulnerable traffic participants and improve the robustness and precision of the prediction results.


Get in touch. We look forward to your questions and ideas for our Joint Academic Partnership Digitalisation.

Dr. Karin Streker

Dr. Karin Streker

Koordinatorin BayWISS-Verbundkolleg Digitalisierung

Julius-Maximilians-Universität Würzburg
Graduate Schools of Science and Technology
Beatrice-Edgell-Weg 21
97074 Würzburg

Telephone: +49 931 3189695