  Understanding the scene around the ego-vehicle is key to assisted and
autonomous driving. Nowadays, this is mostly conducted using cameras and laser
scanners, despite their reduced performances in adverse weather conditions.
Automotive radars are low-cost active sensors that measure properties of
surrounding objects, including their relative speed, and have the key advantage
of not being impacted by rain, snow or fog. However, they are seldom used for
scene understanding due to the size and complexity of radar raw data and the
lack of annotated datasets. Fortunately, recent open-sourced datasets have
opened up research on classification, object detection and semantic
segmentation with raw radar signals using end-to-end trainable models. In this
work, we propose several novel architectures, and their associated losses,
which analyse multiple "views" of the range-angle-Doppler radar tensor to
segment it semantically. Experiments conducted on the recent CARRADA dataset
demonstrate that our best model outperforms alternative models, derived either
from the semantic segmentation of natural images or from radar scene
understanding, while requiring significantly fewer parameters. Both our code
and trained models will be released.
