  Pedestrians are among the most endangered traffic participants in road
traffic. While pedestrian detection in nominal conditions is well established,
the sensor and, therefore, the pedestrian detection performance degrades under
adverse weather conditions. Understanding the influences of rain and fog on a
specific radar and lidar sensor requires extensive testing, and if the sensors'
specifications are altered, a retesting effort is required. These challenges
are addressed in this paper, firstly by conducting comprehensive measurements
collecting empirical data of pedestrian detection performance under varying
rain and fog intensities in a controlled environment, and secondly, by
introducing a dedicated Weather Filter (WF) model that predicts the effects of
rain and fog on a user-specified radar and lidar on pedestrian detection
performance. We use a state-of-the-art baseline model representing the physical
relation of sensor specifications, which, however, lacks the representation of
secondary weather effects, e.g., changes in pedestrian reflectivity or droplets
on a sensor, and adjust it with empirical data to account for such. We find
that our measurement results are in agreement with existent literature related
to weather degredation and our WF outperforms the baseline model in predicting
weather effects on pedestrian detection while only requiring a minimal testing
effort.
