  Driver assistance systems as well as autonomous cars have to rely on sensors
to perceive their environment. A heterogeneous set of sensors is used to
perform this task robustly. Among them, radar sensors are indispensable because
of their range resolution and the possibility to directly measure velocity.
Since more and more radar sensors are deployed on the streets, mutual
interference must be dealt with. In the so far unregulated automotive radar
frequency band, a sensor must be capable of detecting, or even mitigating the
harmful effects of interference, which include a decreased detection
sensitivity. In this paper, we address this issue with Convolutional Neural
Networks (CNNs), which are state-of-the-art machine learning tools. We show
that the ability of CNNs to find structured information in data while
preserving local information enables superior denoising performance. To achieve
this, CNN parameters are found using training with simulated data and
integrated into the automotive radar signal processing chain. The presented
method is compared with the state of the art, highlighting its promising
performance. Hence, CNNs can be employed for interference mitigation as an
alternative to conventional signal processing methods. Code and pre-trained
models are available at https://github.com/johanna-rock/imRICnn.
