This paper provides a comprehensive review of traditional and deep learning (DL) approaches for object detection (OD) in autonomous vehicles (AVs) under adverse weather conditions. It discusses the challenges faced by AVs in adverse weather, such as rain, fog, snow, and low visibility, and evaluates the effectiveness of various sensor technologies, including LiDAR, radar, ultrasonic sensors, and cameras. The paper also explores the architecture of AVs, the role of perception, planning, and control systems, and the impact of weather on these systems. It reviews the performance of traditional and DL methods for detecting vehicles, pedestrians, and road lanes, and summarizes common evaluation metrics for OD. The paper highlights the limitations of traditional methods, such as reliance on manual feature engineering and limited adaptability to dynamic environments, and emphasizes the advantages of DL methods, which can automatically extract features and learn from data, providing more flexible and adaptable solutions. The paper also discusses the importance of sensor fusion and the challenges of implementing reliable detection systems in adverse weather conditions. It concludes that DL approaches are more effective in handling the challenges of adverse weather, offering better performance and accuracy in object detection for AVs.This paper provides a comprehensive review of traditional and deep learning (DL) approaches for object detection (OD) in autonomous vehicles (AVs) under adverse weather conditions. It discusses the challenges faced by AVs in adverse weather, such as rain, fog, snow, and low visibility, and evaluates the effectiveness of various sensor technologies, including LiDAR, radar, ultrasonic sensors, and cameras. The paper also explores the architecture of AVs, the role of perception, planning, and control systems, and the impact of weather on these systems. It reviews the performance of traditional and DL methods for detecting vehicles, pedestrians, and road lanes, and summarizes common evaluation metrics for OD. The paper highlights the limitations of traditional methods, such as reliance on manual feature engineering and limited adaptability to dynamic environments, and emphasizes the advantages of DL methods, which can automatically extract features and learn from data, providing more flexible and adaptable solutions. The paper also discusses the importance of sensor fusion and the challenges of implementing reliable detection systems in adverse weather conditions. It concludes that DL approaches are more effective in handling the challenges of adverse weather, offering better performance and accuracy in object detection for AVs.