# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 1 Introduction  
The growing population has led to a significant increase in vehicle demand, resulting in increased traffic congestion. This heavy traffic exerts additional pressure on the road infrastructure, which deteriorates over time [1]. Potholes, depressions, and uneven surfaces have become commonplace, making vehicular movement increasingly challenging and prone to accidents [2]. Even advanced vehicles, such as solar cars, can be rendered immobile due to the presence of potholes [3]. Daily accidents caused by jolting and instability of vehicles on uneven roads lead to serious injuries and loss of life, highlighting the urgent need to address these issues [4]. Poorly maintained roads exacerbate vehicle instability, causing drivers to lose control and significantly increasing the risk of accidents [5]. Countries with extreme weather and challenging terrain face significant challenges in road safety. Mountainous regions, such as Bhutan, Pakistan, and Switzerland, experience traffic jams and high accident rates due to rough terrain, sharp bends, and adverse weather conditions. Heavy snow in Canada and Switzerland exacerbates these risks, leading to slippery, icy roads and increased vehicle malfunctions [6; 7].In regions with extreme terrain, routine travel becomes dangerous, requiring robust solutions to mitigate risk. The Himalayan regions of Nepal and India face poorly maintained roads that damage structural vehicles and disrupt traffic. Developing countries like Bangladesh face similar problems, with inadequate infrastructure leading to accidents, compromised safety, and high vehicle maintenance costs [8; 9]. These challenges affect low-income areas with limited resources for road repair and technological advancement [10]. On a global scale, the statistics paint a dire picture. According to the World Health Organization, approximately 1.3 million lives are lost yearly to road accidents [11]. This alarming figure underscores the urgent need for improvements in road conditions and vehicle safety mechanisms. Developing nations like Bangladesh report staggering numbers, with 24,954 fatalities attributed to road accidents annually [12]. Additionally, over 50 million people suffer injuries or disabilities caused by road accidents each year, highlighting the far-reaching consequences of this issue [13]. Road accidents have significant economic and social impacts, including medical costs, loss of productivity, trauma, and stress on healthcare systems. An important reason is that drivers are unable to understand real-time road conditions. Hidden irregularities, such as potholes or bumps, often go unnoticed until the vehicle is impacted, leading to accidents or damage, especially in rough or hilly areas [14; 15]. Areas with dense fog, snow, or poor lighting increase the risk of rapidly changing road conditions. Advances in technology, such as the Internet of Things (IoT) and machine learning, offer solutions for real-time road anomaly detection and stabilization. Sensors like the GY-521 can detect vibration and tilt from potholes, while machine learning models like the YOLOv8 visually detect road irregularities. These technologies enable vehicles to adapt to different conditions, ensuring a smoother ride and improved safety [16]. These systems offer advantages beyond passenger comfort by safeguarding the goods being transported, minimizing vehicle wear and tear, and reducing maintenance costs. This results in cost savings and enhanced efficiency for public transport and logistics. However, implementation is constrained by high costs and limited awareness. Developing countries encounter additional challenges that necessitate affordable, customized solutions. As IoT and machine learning advance, more innovative, safer, and more adaptive transportation systems are becoming increasingly feasible. Uneven roads and inadequate maintenance demand technological innovation and strategic infrastructure investment.