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Towards Safer Roads: AI-Powered Accident Prevention for Real Time Driver Monitoring and Pothole Detection

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Towards Safer Roads: AI-Powered Accident Prevention for Real Time Driver Monitoring and Pothole Detection

Towards Safer Roads: AI-Powered Accident Prevention for Real Time Driver Monitoring and Pothole Detection

 

 

R. Madhuri Devi1, A. Kavya2, B. Mohana3, Ch. Varshitha4

1Assistant Professor, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR

2Student, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR

3Student, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR

4Student, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR

 

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Abstract - Road accidents and dangerous driving conditions, such as potholes, are major cause to accidents and injuries worldwide. This Paper presents an AI-driven road safety system that combines an approach to reduce accidents with real-time pothole detection aims to enhance road safety and improve driving experiences. This system collects data from different sources, which includes cameras, road sensors, and weather updates. Using deep learning models, like Long Short-Term Memory (LSTM) networks, it identifies potential risks such as sudden braking, dangerous driving, or hazardous road conditions. These help to enable real-time, such as issuing driver alerts, notifying emergency response teams. Additionally, the system uses computer vision and sensor-based analytics to detect potholes and other road anomalies, creating a comprehensive safety framework. The system maintains a monitoring distance of 30 meters, continuously capturing real-time scenes using cameras and AI algorithms. Alerts are sent to users to slow down when hazards are detected, ensuring safety. The system also informs local authorities to facilitate timely road maintenance. This dual functionality helps to reduce accidents, enhance traffic flow, and maintain road quality. This intelligent and proactive approach to road safety aims to minimize human errors, reduce accident rates, and enhance overall traffic management. Scalable and adaptable to urban and rural settings, the proposed solution represents a significant step toward building safer, smarter, and more sustainable transportation ecosystems.

Key Words:  Artificial Intelligence, Deep Learning, Long Short-Term Memory (LSTM) network, IOT, Recurrent Neural Networks, Computer Vision, Road Safety, Accident prevention.

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