# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 1 Introduction  
Environmental perception is the core of autonomous vehicles. Autonomous vehicles, also known as driverless cars, refer to vehicles capable of perceiving their surroundings and navigating safely with little or no human input [1]. Although Autonomous Driving Systems (ADS) have potential advantages in improving traffic safety, reducing congestion, lowering transportation costs, and positively impacting the environment, incidents and casualties involving ADS are still on the rise. To gain wider identification of the advantages of autonomous vehicles, it is imperative to address the pressing issues currently faced by ADS, especially the capability of environmental identification under adverse weather conditions [2]. Vehicular networks, through intelligent technology, have enabled efficient information exchange between vehicles, infrastructure, pedestrians, and network services, creating an intelligent transportation system [3]. In this system, accurately identifying the environment around the vehicle is crucial for the operation of intelligent vehicles. Intelligent vehicles must adjust their speed based on road conditions, traffic congestion, and the specific areasthey are in, such as urban or rural regions. Particularly under adverse weather conditions like heavy rain, precise environmental identification becomes even more important, as statistics show that the risk of accidents in rainy weather is 70% higher than in normal conditions [4]. The introduction of V2X (Vehicle-to-Everything) communication technology further enhances the functionality of vehicular networks. V2X communication is not just a part of vehicular networks; it also expands the sensory range of vehicles, enabling autonomous vehicles to communicate in real an additional an additional time with surrounding vehicles, infrastructure, and pedestrians. This capability of real-time information sharing and perception allows autonomous vehicles to understand their environment more accurately, leading to more rational decision-making and action planning. This is of significant importance for achieving higher levels of autonomous driving and enhancing traffic safety and efficiency [5; 6; 7]. Rainy conditions pose additional challenges to the operation of intelligent vehicles, as weather conditions directly affect the environmental state, further complicating the task of environmental identification by ADS [8]. Therefore, a new method for vehicle environmental identification under rainy weather conditions that does not require specific sensors is proposed in this paper. It utilizes Cooperative Awareness Messages (CAM) exchanged between vehicles to explore channel characteristics, which are then used to identify the vehicle's environment. This enables vehicles to automatically determine the appropriate driving speed, thereby enhancing the safety and reliability of ADS in rainy weather conditions, as shown in Figure 1.  
The main contributions of our work are as follows:  
A wireless vehicular communication network has been established. Considering the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, an innovative approach that adds a multipath component has been proposed to simulate the channel characteristics of vehicular environments in rainy conditions. This new multipath component specifically represents the effect of raindrop scattering and is characterized by path delay, path gain, and Doppler shift parameters. An equalization strategy for the receiver end of OFDM systems has also been proposed. It adjusts the signal for each subcarrier to counteract channel distortion, thereby enhancing the reliability of the signal. A deep learning-based method for rainy environment identification in vehicles is proposed. This method does not rely on specific sensors but utilizes the wireless channel characteristics shared among vehicles in the vehicular network for environmental perception.