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 \textit{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.

翻译：行人是道路交通中最易受伤害的参与者之一。虽然正常条件下的行人检测技术已较为成熟，但在恶劣天气条件下，传感器性能及其行人检测效果会显著下降。了解雨雾天气对特定雷达和激光雷达传感器的影响需要大量测试，且若传感器规格发生改变则需要重新测试。本文通过两方面解决这些挑战：首先在受控环境中开展综合测量，收集不同雨雾强度下行人检测性能的经验数据；其次引入专用天气滤波器（Weather Filter, WF）模型，该模型可预测雨雾天气对用户指定的雷达和激光雷达行人检测性能的影响。我们采用代表传感器规格物理关系的最先进基线模型，但该模型缺乏对次级天气效应（如行人反射率变化或传感器表面水滴附着）的表征，因此我们利用经验数据对其进行校正。研究发现，测量结果与现有关于天气退化的文献结论一致，且WF模型在预测天气对行人检测影响方面优于基线模型，同时仅需极少的测试开销。