AI-Driven Smart Vehicle for Real-Time Road Damage Detection

The AI-Driven Smart Vehicle for Real-Time Road Damage Detection project explores the integration of YOLO, deep learning techniques and Advanced Driver Assistance Systems (ADAS) to enhance road safety and infrastructure monitoring. This project leverages deep learning models and computer vision techniques to process real-time sensor and camera data from vehicles, enabling the dynamic identification and assessment of road conditions. By utilizing generative models trained on vast datasets of road damage images and real-world sensor data, the system can detect and classify road anomalies such as potholes, cracks, and surface degradation with high accuracy. The smart vehicle system employs AI-driven anomaly detection to operate effectively under varying environmental conditions. Beyond classification, the AI estimates damage severity and predicts its potential impact on traffic safety. Additionally, predictive analytics enable the system to forecast future road deterioration based on historical data, facilitating proactive maintenance and repair planning. Integration with ADAS functionalities enhances driver safety by providing real-time alerts, suggesting alternative routes, and assisting autonomous navigation in hazardous conditions. Furthermore, the system autonomously communicates detected road damage to local authorities, optimizing infrastructure management and reducing long-term maintenance costs. By establishing a continuous feedback loop between vehicles and urban infrastructure, this project contributes to safer, smarter, and more efficient transportation networks, aligning with the vision of modern smart cities. IndexTerms: AI-Driven Smart Vehicle, Real-Time Road Damage Detection, YOLO (You Only Look Once), Deep Learning, Road Safety, Infrastructure Monitoring, Sensor and Camera Data, Pothole Detection, Predictive Analytics.