## 1 Introduction  
The development of autonomous driving technology has made significant strides over the past few years. However, the reliability and safety of these systems under adverse weather conditions remain critical challenges. Adverse weather, such as rain, snow, and fog, can significantly impact the performance of environmental perception sensors, thereby increasing the operational risks of autonomous driving systems [1, 2]. In particular, optical sensors, such as cameras and LiDAR, experience a marked decline in perception capabilities under these conditions, leading to reduced accuracy in object detection and decision-making robustness [3, 4]. Studies have shown that under conditions like heavy rain, snowstorms, or dense fog, LiDAR sensorsmay mistakenly detect airborne droplets or snowflakes, which not only affects the overall accuracy of the perception system but can also lead to erroneous decisions being made by the autonomous driving system [5, 6]. Additionally, adverse weather conditions can cause slippery road surfaces, increasing braking distances and further complicating the operation of autonomous driving systems [7]. To address these challenges, recent research has focused on developing new algorithms and technologies to enhance the performance of autonomous driving systems in complex environments. For instance, multi-sensor data fusion techniques, which combine data from different sensors, have significantly improved object detection accuracy in adverse weather conditions [8, 9]. Moreover, deep learning models such as ResNet-50 and YOLOv8 have demonstrated excellent performance in processing images and point cloud data under challenging weather conditions [10; 11; 12]. Building on multi-sensor fusion, vehicle-to-infrastructure cooperative perception systems are considered a critical approach to improving the perception capabilities of autonomous driving systems in the future [13; 14]. By integrating perception data from vehicles and infrastructure, this method can significantly expand the scope of environmental perception and provide more stable perception information under adverse weather conditions [15]. Research has shown that cooperative perception systems outperform traditional single-sensor systems in snowy and rainy conditions [16; 17]. Table 1 compares the algorithmic focus and capabilities across different levels of autonomous driving, from Level 0 (fully manual) to Level 5 (full automation). As the level of autonomy increases, the complexity of the algorithms involved also increases, shifting from essential driver assistance features at Level 1 to advanced deep learning and AI-based decision-making at Levels 4 and 5 [10; 11; 12; 13; 14; 15]. The table highlights the progression from simple rule-based systems to sophisticated AI-driven models capable of handling a wide range of driving tasks independently, reflecting the increasing reliance on machine learning, sensor fusion, and predictive control as vehicles move toward full autonomy. Despite these technological advancements, further research is needed to tackle the multiple challenges of complex environments. For example, optimizing multi-sensor data fusion algorithms, designing more robust deep learning models, and developing training  
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datasets specifically tailored to adverse weather are critical future research directions [18; 19; 20]. Moreover, validating perception systems in real-world adverse weather conditions is crucial. Researchers have developed several datasets, such as SID and DAWN2020, to test and validate these algorithms in complex environments [21; 22; 23]. These datasets encompass various road, vehicle, and pedestrian scenarios under different weather conditions, providing a solid foundation for algorithm training and evaluation [24; 25; 26]. The reliability and safety of autonomous driving technology under adverse weather conditions remain critical challenges for its widespread adoption.