AI to improve automotive radar data quality

November 04, 2019 //By Christoph Hammerschmidt
AI to improve automotive radar data quality
Automated vehicles must be able to detect and understand their surroundings independently. In adverse weather conditions such as heavy rain or snowfall, however, cameras and laser sensors are blind. Radar systems function much better and more robustly under such conditions. In the research project AuRoRaS - Automotive Robust Radar Sensing - new simulation methods and artificial intelligence methods are to be developed to make radar systems more efficient and autonomous driving safer.

In the next two years, Astyx GmbH (project coordination), BIT Technology Solutions GmbH and the German Research Center for Artificial Intelligence (DFKI) will implement an automotive radar system for use in highly automated (Level 4) and autonomous driving (Level 5). The aim of the project is to significantly increase the quality of the measurement data of the continuous wave radar. With this radar technology, radar signals are emitted continuously during the measurement and reflections are measured.

The big advantage of radar-based sensing over camera-based methods or laser sensors (lidar) is the direct measurement of the object speed and the robustness against weather influences such as fog or snow. Disadvantages are possible errors in signal processing. These can be caused by speed ambiguities or multipath propagation. However, highly automated driving functions require very high accuracy and robustness. In the project AuRoRaS the physically caused disadvantages of radar sensors are to be recognized and eliminated by innovative AI methods.

In addition to its high-resolution radar equipment, Astyx also contributes its specialist knowledge in software-supported object recognition. This know-how in the areas of 3D object recognition from radar point clouds and deep learning-based object recognition will be used to improve the AI-based point cloud extraction from the radar raw data. Astyx is also developing synchronized data recording, geometric calibration of sensors and the development of data interfaces and tools for annotating real training and test data.


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