# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 1 Introduction
IoT and machine learning empower vehicles to react to real-time road conditions, improving safety, comfort, and efficiency. This transition has the potential to save lives, reduce economic losses, and promote sustainable, inclusive transportation. While numerous studies examine road conditions, there is less focus on vehicle stabilization mechanisms [17; 18]. We have created a system that integrates IoT and machine learning to detect and respond to road conditions. Advanced sensors assess road conditions, identify features such as hill height or pothole depth, and determine which wheels are affected. The servo motor regulates the vehicle's top, ensuring stability on uneven roads. This guarantees a smooth ride, decreases passenger discomfort, and lowers the risk of vehicle instability. In contrast to conventional standalone vehicle stabilization systems, our architecture integrates real-time edge processing with cloud-based feedback mechanisms. This combination facilitates continuous learning and over-the-air (OTA) adaptation driven by telemetry data collected during operation, creating a closed-loop IoT system. Consequently, this framework goes beyond localized detection to enhance scalable, cross-vehicle generalization--an essential capability for smart mobility and autonomous maintenance systems. Research Questions and Overview :  
* **RQ1: How can IoT-based technologies improve vehicle safety and enable real-time road maintenance in areas with varied topographies, especially in Asia? **
* **RQ2: How can low-cost sensors like the GY-521 sensor and machine learning algorithms like YOLOv8 be used optimally to identify and respond to road surface anomalies? **
* **RQ3: How can real-time pothole detection and vehicle stabilization systems enhance road safety and passenger comfort in low- and middle-income countries? **
* **RQ4: How can a dual-part vehicle design with real-time stabilization capabilities reduce accidents and safeguard vehicle components in areas with poor road conditions? **
* **RQ5: What are the main obstacles to implementing IoT-based vehicle safety systems in Bangladesh, and how can they be addressed to enhance data processing, communication protocols, and network effectiveness reliability? **  
**Contributions:** This study introduces an innovative solution to address the critical challenges of vehicle maintenance, passenger comfort, and road safety, particularly in challenging driving conditions like those in Bangladesh and other Asian nations. **I. Enhanced Passenger Comfort and Safety:** The system enhances passenger comfort and safety by utilizing real-time stabilization mechanisms that adjust the vehicle's balance in response to uneven road surfaces. **II. Cost-effective and Easy Installation:** One of the most notable features of this solution is its affordability, making advanced road safety and stabilization technology available to a wider audience. **III. Real-Time Road Monitoring:** The system's integration of YOLOv8, a state-of-the-art machine learning model, facilitates accurate and efficient real-time detection of road hazards, including potholes, cracks, and bumps. By continuously monitoring the road surface, the system anticipates and addresses anomalies before they threaten vehicle stability. **IV. Environmental and Economic Sustainability:** By tackling the challenges caused by uneven road surfaces, the system lessens the wear and tear on essential vehicle components like suspension systems, tires, and chassis. **V. Supports Public Health and Safety:** Road safety is crucial for public health, and this system significantly contributes by lowering the risk of accidents caused by uneven road surfaces. This paper is organized into several key sections to comprehensively understand the proposed IoT-based solution for uneven roads and vehicle stabilization. The abstract offers a concise summary of the problem, methodology, and key outcomes.