## 1 Introduction  
Autonomous driving systems are advocated for their potential to enhance safety by addressing issues such as drunk or reckless driving, rule violations, and making improved decisions in challenging road conditions. Additionally, these systems are expected to contribute to congestion relief through efficient decision-making processes [1]. With the rapid growth in the global economy, the living standard has improved [2, 3, 4]. With the rapid growth of vehicles on the roadways, there are some significant causes of traffic safety issues such as vehicle accidents, drunk driving, road conditions, and traffic violations [5]. Road traffic congestion in urban areas is the most common issue due to the density of traffic and the growth of the urban population [6].  
Therefore, vehicle recognition and tracking are necessary for conventional algorithms that use machine learning and deep learning-based algorithms to forecast the volume of traffic flow [7]. However, with the rapid improvement in computer vision and deep learning, deep learning-based algorithms replaced the traditional object detection methods for real-time object detection [8; 9; 10]. Deep learning methods are more effective and accurate, and they perform better in complex environments to detect objects [11]. Convolutional neural network (CNN)-based deep learning techniques for object recognition [12; 13; 14] are categorized into two types: the two-stage method and one-stage method [15; 16; 17]. In the one-stage method, deep-learning-based object detection, You Only Look Once (YOLO) v5 achieves high accuracy, speed, and high reliability in object detection compared to the other one-stage-based detectors [18]. However, the object detection system still has a few of the problems frequently encountered in detection such as occluded vehicles, flickering, and challenging illumination.  
The fusion of Deep SORT and YOLOv5 for effective vehicle detection and tracking in real-time traffic management stems from several compelling motivations. First and foremost, there is the overarching goal of enhancing road safety. By combining the unique strengths of Deep SORT, which is known for its robust tracking capabilities, and YOLOv5, which is renowned for accurate and rapid object detection, the fusion aims to mitigate the risks associated with reckless driving, violations, and adverse road conditions.  
Another critical motivation lies in the pursuit of optimizing traffic flow and reducing congestion. The integration of these advanced technologies enables a more comprehensive understanding of the dynamic traffic environment. This, in turn, facilitates precise decision-making processes, leading to smoother traffic patterns and improved overall traffic management efficiency.  
Moreover, the increasing demand for sustainable transportation systems propels the fusion of Deep SORT and YOLOv5. Achieving sustainable traffic solutions involves not only enhancing safety and efficiency but also minimizing the environmental impact. By deploying a sophisticated vehicle detection and tracking scheme, the system contributes to a sustainable approach to traffic management, aligning with global efforts to promote eco-friendly transportation practices.