  The interest of the automotive industry has progressively focused on subjects
related to driver assistance systems as well as autonomous cars. Cars combine a
variety of sensors to perceive their surroundings robustly. Among them, radar
sensors are indispensable because of their independence of lighting conditions
and the possibility to directly measure velocity. However, radar interference
is an issue that becomes prevalent with the increasing amount of radar systems
in automotive scenarios. In this paper, we address this issue for frequency
modulated continuous wave (FMCW) radars with fully convolutional neural
networks (FCNs), a state-of-the-art deep learning technique. We propose two
FCNs that take spectrograms of the beat signals as input, and provide the
corresponding clean range profiles as output. We propose two architectures for
interference mitigation which outperform the classical zeroing technique.
Moreover, considering the lack of databases for this task, we release as open
source a large scale data set that closely replicates real world automotive
scenarios for single-interference cases, allowing others to objectively compare
their future work in this domain. The data set is available for download at:
http://github.com/ristea/arim.
