  Neural radiance fields (NeRFs) have gained popularity in the autonomous
driving (AD) community. Recent methods show NeRFs' potential for closed-loop
simulation, enabling testing of AD systems, and as an advanced training data
augmentation technique. However, existing methods often require long training
times, dense semantic supervision, or lack generalizability. This, in turn,
hinders the application of NeRFs for AD at scale. In this paper, we propose
NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our
method features simple network design, extensive sensor modeling for both
camera and lidar -- including rolling shutter, beam divergence and ray dropping
-- and is applicable to multiple datasets out of the box. We verify its
performance on five popular AD datasets, achieving state-of-the-art performance
across the board. To encourage further development, we will openly release the
NeuRAD source code. See https://github.com/georghess/NeuRAD .
