  Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a
crucial role in facilitating planning and decision-making for mobile robots.
Although recent vision-only methods have demonstrated notable advancements in
performance, they often struggle under adverse illumination conditions such as
rain or nighttime. While active sensors offer a solution to this challenge, the
prohibitively high cost of LiDARs remains a limiting factor. Fusing camera data
with automotive radars poses a more inexpensive alternative but has received
less attention in prior research. In this work, we aim to advance this
promising avenue by introducing BEVCar, a novel approach for joint BEV object
and map segmentation. The core novelty of our approach lies in first learning a
point-based encoding of raw radar data, which is then leveraged to efficiently
initialize the lifting of image features into the BEV space. We perform
extensive experiments on the nuScenes dataset and demonstrate that BEVCar
outperforms the current state of the art. Moreover, we show that incorporating
radar information significantly enhances robustness in challenging
environmental conditions and improves segmentation performance for distant
objects. To foster future research, we provide the weather split of the
nuScenes dataset used in our experiments, along with our code and trained
models at http://bevcar.cs.uni-freiburg.de.
