## 1 Introduction  
In recent years, with the continuous development of emerging scientific and technological advancements, such as big data and mobile connectivity, the utilization of onboard sensors has been widely applied. However, under adverse weather conditions, the recognition accuracy of most onboard sensors significantly decreases, posing higher demands on drivers for safety, efficiency, and comfort during the driving experience. For instance, in rainy or snowy weather, the capability of lidar sensors in obstacle detection is noticeably limited, thereby severely impacting driving safety. To ensure the safety and stability of driving in various environments, research on drivers' perceptions and understanding of the environment has become critically important [1]. Nevertheless, environmental research faces two major challenges. Firstly, the complexity of weather conditions makes their quantification a formidable task. Weather phenomena involve multiple variables and interactions, making it difficult to accurately describe weather phenomena. Secondly, the variability of weather makes conducting real-world testing highly challenging. The constantly changing weather conditions increase the uncertainty of the actual testing environment, thereby limiting research on driving environments. Currently, there is relatively limited work on driving environments, especially concerning weather research. This is mainly due to the difficulty of accurately describing the environment and the challenges associated with conducting practical tests. The decision-making processes and behavioral patterns exhibited by drivers during the act of driving are subject to the influence of numerous factors, predominantly encompassing the external surroundings and the driver's intrinsic attributes. Consequently, these factors give rise to the emergence of potential hazards and risks within the driving context. The external environment involves moving objects (motor vehicles, nonmotor vehicles, and pedestrians) [2; 3], static environmental elements (road boundaries, lane separation lines) [4; 5], dynamic traffic control signals (traffic lights) [6], and weather [7; 8; 9]. [PERSON] et al. constructed a unified driving safety field model that utilizes field theory to represent risks caused by drivers, vehicles, road conditions, and other traffic factors [10; 11]. [PERSON] et al. conducted a unified model of driving behavior in different scenarios. The use of field theory to link the risk steady-state theory and the predictive following theory, two behavioral theories [12], did not consider the impact of environmental factors on driving behavior. There are many studies on the driver's own factors in relevant studies. The driver's own factors include the driver's driving experience [13; 14], the driver's driving style [15; 16; 17], the driver's physiological and psychological state, etc. [PERSON] et al. considered the personalized factors of human drivers and the traffic environment and derived a personalized humanoid lane change trajectory planning model [18]. There have been relevant articles proving that the same driver may exhibit different behaviors under different emotions because, under the influence of emotions, the activation direction, intensity, and depth of the nervous system have changed [19]. Under different weather conditions, the driving behavior of the same driver can also change. [PERSON] et al. analyzed the driving behavior changes and influencing factors of drivers under three different visibility conditions: no fog, light fog, and heavy fog [20]. [PERSON] introduced a method of using visual data to detect weather conditions and proposed a weather detection system based on deep learning technology, but did not consider the relationship between driver behavior and the environment [21]. Indeed, weather conditions exert an influence on drivers' perceptual and cognitive faculties, encompassing visual, tactile, and other sensory modalities.