###### Abstract  
Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research. intelligent transportation system; autonomous vehicles; object detection; deep learning; traditional approaches +
Footnote †: journal: 26  
autonomous vehicles (AVs). AVs, especially self-driving cars, have generated a great deal of interest in the fields of intelligent transportation, computer vision, and artificial intelligence [2]. No other invention since the creation of vehicles has had such a dramatic impact on the automotive industry as self-driving vehicles. As per the forecasts presented in Figure 1[3], the market for self-driving cars is expected to grow from 23.80 million units in 2022 to 80.43 million units by 2032. These vehicles primarily rely on cameras and sensors, and examples include LiDAR (light detection and ranging), radar, ultrasonic, etc., which have a number of autonomous features, including reduced use of fuel, cutting carbon dioxide emissions by up to 10%, decreased traffic congestion [4], decreased road accidents, and utilizing roads more efficiently to optimize the transportation system. AVs can assist in managing and studying traffic by gathering effective and valuable traffic data. Along with other road-related capabilities, they are capable of recognizing static and non-static objects such as vehicles, pedestrians, and road lanes. Object detection (OD) is critical for AVs as it ensures safety by identifying and avoiding moving obstacles in their path. Numerous research works have investigated methods for identifying objects in AVs, broadly divided into manual, semi-automated, and fully automated systems [5], as shown in Figure 2. Roadside artifacts are visually assessed by human inspectors using manual procedures; nonetheless, subjectivity and labor intensity are drawbacks. While semi-automated methods gather data from moving cars and process them afterward, they are still labor-intensive [6]. High-resolution cameras and sensors on cars record pictures and videos in completely autonomous systems. Software-based models that have been trained to detect objects evaluate the data either after they have been collected or in real time.