# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 2 Related Work  
Perception and sensing in adverse weather conditions have been a challenge for autonomous vehicles to achieve higher levels of automation. The impacts that weather poses to ADS sensors are significant; hence, solutions for dealing with adverse weather conditions are mainly discussed in this paper. Weather challenges have always been an obstacle to the deployment of ADS, and it is necessary to recognize their impacts on sensors. LiDAR is one of the core perception sensors in the field of autonomous driving. The use of 3D-LiDAR on cars has not been widespread for more than a decade but has already proven to be indispensable in ADAS (advanced driver-assistance systems) with high measurement accuracy and independent sensing capabilities regardless of illumination. [PERSON] et al. [9] studied the impact of rain on pulsed LiDAR systems with small apertures and narrow laser beam cross-sections. [PERSON] et al. [10] quantified the impact of rainfall on different LiDAR parameters: range, intensity, and number of detection points. [PERSON] et al. [11] researched the impact of rain on automotive laser scanner sensor systems and introduced a new theoretical model based on a hierarchical model to describe the impact of rain on sensor behavior. Regarding radar sensors, the automotive radar system consists of a transmitter and a receiver. [PERSON] et al. [12] described the impact of rainfall on millimeter-wave radar, considering rain attenuation and backscatter effects. As for cameras, the camera is one of the most widely used sensors in perception tasks but also one of the most vulnerable in adverse weather conditions. Cameras in the rain, no matter how high the resolution, are easily damaged by a drop of water on the transmitter or lens. [PERSON] et al. [13] proposed a camera loop method to evaluate the performance of object detection algorithms under different weather conditions. With the widespread use of machine learning and the rapid development of powerful sensors, multi-sensor modes and additional sensor components are used to help mitigate the impact of weather. A single sensor does not provide sufficient safety assurance for navigation under adverse weather conditions. [PERSON] et al. [14] utilized multi-sensor information fusion technology to improve the perception accuracy and reliability of autonomous vehicles in adverse weather conditions. The fusion scheme uses millimeter-wave radar as the main sensor and a monocular camera as an auxiliary sensor. [PERSON] et al. [15] developed a robust fusion model for multi-modal sensor inputs under unseen adverse weather conditions, laying the foundation for the development and evaluation of future autonomous vehicle technologies. [PERSON] et al. [16] proposed a three-dimensional (3D) object detection method based on a post-fusion architecture that performs well in foggy conditions. Sensors are key to the safe navigation of autonomous vehicles in adverse weather; enhancing their perception capabilities is crucial. [PERSON] et al. [17] proposed a Complementary Cascade Network (CCN) architecture capable of uniformly removing raindrops and streaks from images, introducing a new real-world rain dataset, RainDS. [PERSON] et al. [18] introduced a rain intensity control network, RICNet, which can achieve bidirectional control of rain intensity from clear to downpour images while preserving the characteristics of specific scenes. [PERSON] et al. [19] introduced a new semi-supervised video deraining method that processes labeled synthetic data and unlabeled real data using a dynamic rain generator and different prior formats. This method has successfully achieved better deraining effects on real datasets. The perception capabilities of intelligent vehicles are not limited to object identification; they also encompass a comprehensive understanding of their location and surrounding environment, especially the ability to accurately classify and locate in adverse weather conditions, which is crucial for ensuring the safe operation of the vehicle.