© Baustädter – Graz University of Technology
Researchers at the Graz University of Technology have now modelled an AI system for automotive radar sensors that filters out interfering signals caused by other radar sensors and dramatically improves object detection. Now the system is to be made more robust to weather and environmental influences as well as new types of interference. Interference from other (radar) equipment as well as extreme weather conditions create noise that negatively affects the quality of the radar measurement.
"The better the denoising of interfering signals works, the more reliably the position and speed of objects can be determined," explains Franz Pernkopf from the Institute of Signal Processing and Speech Communication. Together with his team and with Infineon, he developed an AI system based on neural networks that mitigates mutual interference in radar signals and at the same time far surpasses the current state of the art technology. They now want to optimize this model so that it also works outside of learned patterns and recognises objects even more reliably.
For this purpose, the researchers first developed model architectures for automatic noise suppression based on so-called convolutional neural networks (CNNs). "These architectures are modelled on the layer hierarchy of our visual cortex and are already being used successfully in image and signal processing," says Pernkopf. He expects the technology to be further developed in the coming years to the extent that the first radar sensors can be equipped with it.
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