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Design and development of an AI-powered vehicle accident detection system using IoT.
The AI-Powered Vehicle Accident Detection System is designed to improve road safety by detecting accidents in real time and notifying the relevant authorities immediately. This system uses IoT sensors to capture impact data and an AI model to analyze the severity of the accident. By automating the emergency response process, this project aims to save lives by reducing the time taken for authorities to act during critical incidents.
The system uses an ESP32 microcontroller integrated with a vibration sensor and an accelerometer (ADXL345) to detect accidents. The ESP8266 Wi-Fi module connects the system to the internet, allowing it to upload sensor data to Firebase, a cloud platform. The data is analyzed by a TensorFlow Lite AI model to determine the severity of the accident. Once an accident is detected, real-time alerts are sent to emergency services and the victim’s contacts. The system ensures faster response times during emergencies, potentially saving lives.
The sensors continuously monitor vibration and acceleration data from the vehicle. When an abnormal pattern indicating an accident is detected, the ESP32 microcontroller processes the data and sends it to the Firebase cloud platform. The TensorFlow Lite AI model further analyzes the data to confirm the accident's severity. Once confirmed, alerts are sent in real-time to predefined emergency contacts and authorities. This automation significantly reduces the response time, improving the chances of survival for accident victims.
