PRYSTINE

Echtzeitsignalverarbeitung verteilter Radarsysteme im Bereich des autonomen Fahrens

Real-time signal processing of distributed radar systems in the field of autonomous driving

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Title of the overall project: Programmable Systems for Intelligence in Automobiles

Project start: 16/06/2018
Acronym: PRYSTINE
Funders: Bundesministerium für Bildung und Forschung (BMBF)

Abstract

On May 1, 2018, the research and innovation project PRYSTINE started, under joint funding of the European Union by ECSEL and the national governments of the ECSEL member states. The Chair of Technical Electronics represents FAU in the consortium of over 50 European partners.
Among the real trends that will impact society in the coming years, autonomous driving stands out in particular for its potential to change the automotive industry as we know it today. Subsequently, this will also strongly influence the semiconductor industry and open up new market opportunities, as semiconductors play an indispensable role as “enablers” for autonomous vehicles. Autonomous driving has been identified as one of the most important prerequisites for meeting the societal challenges of safe, clean and efficient mobility. To achieve this, fail-safe behavior is essential to handle safety-critical situations on its own. This is not achieved with current approaches, in part due to a lack of reliable environment awareness and insufficient sensor fusion.
In the project titled “Programmable Systems for Intelligence in Automobiles” (PRYSTINE), the general goal is to realize a robust and fail-safe all-around perception of the environment of vehicles. By means of robust sensor data fusion of radar, LiDAR and camera data, as well as fail-safe control functions, autonomous driving in urban and rural environments should be made possible as safely as possible.
At the Chair of Technical Electronics, robust environmental sensing and imaging using MIMO radar sensors will be performed within the framework of PRYSTINE. Different influences and scenarios, such as radio interference or detection in the near field for automotive radars, will be considered. Furthermore, parts of the traditional radar signal processing chain, from interference reduction to detection, classification and tracking of road users, will be gradually replaced by machine learning.
Complete information about this project can be found on the official website: www.prystine.eu .


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Publications

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