## 2 Related Survey Papers  
This section critically evaluates the existing literature on autonomous vehicle (AV) sensor performance under challenging weather conditions, with a focus on the integration of traditional and DL detection techniques. One study [13] presents an in-depth analysis of the impact of inclement weather on AV sensors, particularly the attenuation and backscatter effects on millimeter-wave radar in heavy rain. Despite the study's novel modeling approach, it does not comprehensively address the multifaceted challenges posed by other weather phenomena, such as fog and snowfall, which is crucial for a holistic sensor performance evaluation. Research [14] offers a comprehensive review of sensor performance in adverse weather conditions, emphasizing the importance of sensor fusion for improved vehicle perception. However, the study's focus on range and resolution does not fully explore the nuanced challenges of object classification and sensor reliability in cold weather conditions, which is critical for AV operation. The study in [15] aims to develop a robust operational system for AVs in various weather conditions, advocating for sensor fusion and infrastructure support. Yet, the study's discussion on sensor reliability does not adequately address the practical considerations of sensor installation costs and benefits, which are significant factors in the practical implementation of AV technology. The study in [16] investigates the effects of adverse weather on LiDAR and camera lenses in AVs, suggesting improvements in sensor hardware and the application of sophisticated machine learning methods. While the study's focus on sensor fusion and network infrastructure is commendable, it does not provide a comprehensive evaluation of these technologies across different weather conditions. The research in [17] emphasizes the role of weather recognition and categorization systems in AV decisionmaking, focusing on the detection of road lanes, vehicles, and pedestrians. While the study provides valuable insights, it ac  
Figure 3: Paper organization, where OD = object detection. knowledges that detecting smaller, obscured, or partially visible objects remains a challenge, indicating a need for further research in this area. The study in [18] serves as a tutorial on advanced techniques addressing the impact of rainy conditions on AV object detection. The study evaluates the performance of detection methods under clear and rainy scenarios, highlighting the efficacy of deraining methods, DL-based domain adaptation, and image translation frameworks. However, it does not comprehensively address the limitations of these methods in other adverse weather conditions. The study in [19] examines vehicle detection approaches in video-based traffic surveillance, highlighting the challenges posed by dim illumination, variable weather, occlusion, and shadows. The study's exploration of traditional approaches is valuable, but it does not fully integrate the advancements in DL methods for improved detection. Other [20] research focuses on DL methods for detecting on-road vehicles, providing a valuable list of works. However, the study's limited scope does not encompass the full range of challenges faced by AVs in adverse weather conditions. Another paper [21] exclusively examines 3D methods for vehicle detection, offering insights into a specific detection approach. Yet, the study's narrow focus does not address the broader context of AVs and their performance under various weather conditions. In summary, while the surveyed literature provides a valuable foundation for understanding AV sensor technologies and detection techniques, there is a critical gap in the holistic examination of both traditional and DL approaches under adverse weather conditions. This survey paper focuses on traditional approaches, deep learning approaches, and their applications in object detection in AVs under adverse weather conditions.