Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要