Parallel LiDARs Meet the Foggy Weather
Yuhang Liu, Yonglin Tian, Boyi Sun, Yiyao Wang, Fei–Yue Wang
Abstract
LiDARs play an important role in autonomous driving. They can provide accurate information about the surroundings, which is essential for perception tasks. Unfortunately, LiDARs’ performance will be seriously influenced by adverse weather. Adverse weather is a big challenge to the development of perception systems in autonomous driving. Because of the high cost and time intensity, the lack of LiDAR data under adverse weather is the main problem. Enhanced data is a proper method to solve the data shortage problem. Parallel LiDARs based on parallel theories can provide high-fidelity data through the precise modeling of weather in artificial systems. This paper focuses on Parallel LiDARs in fog. A more precise physical model is used to improve the existing work. The influence of different fog conditions on models is explained with quantitative results in detail. This work is of great help to the targeted improvements of trained models in adverse weather.