  LiDAR sensors are used in autonomous driving applications to accurately
perceive the environment. However, they are affected by adverse weather
conditions such as snow, fog, and rain. These everyday phenomena introduce
unwanted noise into the measurements, severely degrading the performance of
LiDAR-based perception systems. In this work, we propose a framework for
improving the robustness of LiDAR-based 3D object detectors against road spray.
Our approach uses a state-of-the-art adverse weather detection network to
filter out spray from the LiDAR point cloud, which is then used as input for
the object detector. In this way, the detected objects are less affected by the
adverse weather in the scene, resulting in a more accurate perception of the
environment. In addition to adverse weather filtering, we explore the use of
radar targets to further filter false positive detections. Tests on real-world
data show that our approach improves the robustness to road spray of several
popular 3D object detectors.
