###### Abstract
These techniques seek to solve safety problems while improving the precision and effectiveness of item identification on roadways and streets. Vehicle-based surveillance is a widely used technique since it is a quick and efficient way to examine objects. However, it has always been difficult for autonomous programs to identify objects under adverse weather conditions such as rain, fog, snow, haze, storms, and low lighting effects. The weather has a number of negative consequences for mobility and traffic. Precipitation is recorded roughly 11.0% of the time worldwide on average [7]. Studies have unequivocally shown that, in comparison to typical weather, rainfall can cause a 70% increase in the likelihood of accidents [8]. Moreover, snowfall occurs in 77% of the countries worldwide. For example, according to US national data, annually, 24% of vehicle accidents related to weather conditions take place on slippery, snowy, slushy, or icy roads, while 15% occur when snow is actively falling or mixed with sleet [9], highlighting the real risks associated with snowy weather. Visibility is greatly reduced by environmental variables such as fog, haze, sandstorms, and strong sunlight, which presents real difficulties for drivers [10]. Figure 1: The global self-driving cars market size 2022–2032 in USD Million [3]. Traditional and deep learning (DL) algorithms are crucial for enhancing OD in autonomous vehicles, especially in adverse weather. While traditional AI, based on statistical learning, has been foundational, it faces limitations due to manual feature engineering and limited adaptability to dynamic environments, requiring extensive retraining and hindering its effectiveness in real-time applications. DL offers a powerful alternative to traditional machine learning methods for OD in AVs, addressing the limitations of manual feature engineering and adaptability in dynamic environments. DL's multi-layer neural networks automatically extract features and learn from data, providing a more flexible and adaptable solution that enhances the performance of surveillance systems, self-driving vehicles, and smart city applications [11]. DL's neural network-based approach is particularly adept at handling complex models that traditional techniques cannot [12]. In AV object detection, hybrid models combining one-stage and two-stage detectors are effective, with two-stage models focusing on accuracy and one-stage models on speed, although the reasons for the latter's lower accuracy are not fully understood. The research significance of this paper lies in its comprehensive analysis of the challenges and advancements in OD for AVs in adverse weather conditions. It provides a critical review of both traditional and DL approaches, offering insights into the limitations and potential improvements of the current detection algorithms. The paper contributes to the broader field of intelligent transportation systems by emphasizing the need for robust and reliable detection systems that can operate effectively in a variety of weather scenarios, which is crucial for the safe deployment of AVs in real-world conditions. Building on this significance, this paper also contributes to the discussion regarding AV architecture and challenges in adverse weather, and reviews the literature on detecting pedestrians, vehicles, and road lanes using traditional and DL methods. It also summarizes common evaluation metrics for OD. In this paper, we contribute to the field by examining the fundamental architecture of AVs and the specific challenges they face in adverse weather conditions. We have compiled comprehensive datasets, leveraging real-world statistics from LiDAR and camera sensors, to provide a robust foundation for our analysis. We detail the core structure of OD systems and elucidate both traditional and DL methodologies for AVs. Building upon these approaches, we provide a critical review of the existing literature, focusing on the detection of three primary objects--pedestrians, vehicles, and road lanes--under challenging weather conditions.