Research project leans on AI to improve automotive radar: Page 2 of 2

November 05, 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. The research project AuRoRaS – Automotive Robust Radar Sensing – is developing new simulation and artificial intelligence methods to make radar systems more efficient and autonomous driving safer.

BIT Technology Solutions develops a synthetic, physically based simulation of the radar sensor data as well as the required reference data (Ground Truth). This simulation environment serves as a basis for a scalable and efficient training of the AI and the qualitative validation of the AI algorithms. The concept of BIT Technology enables research in the field of simulation of the entire spectrum of electromagnetic waves and the subsequent generation of corresponding synthetic data for training and validation. By combining simulated and real radar measurement data, the accuracy and robustness of the AI methods is significantly improved when cleaning up the measurement data and thus also the environment recognition of an autonomous vehicle based on it.

Based on its experience in the field of machine learning, the German Research Center for Artificial Intelligence (DFKI) is responsible for a large part of the improvement of data quality through newly researched signal processing steps using deep neural networks. These learning procedures require a large representative database, which otherwise could only be completely covered by real data in a very complex and costly way. The effect of multipath propagation in radar measurement is to be detected and compensated by machine learning methods. The measurement data of the radar sensor are analyzed with the aid of neural networks and incorrect values are subsequently removed.


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