IoT-based Vehicle Black Box with Cloud Analytics and Emergency Response

The Road accidents are among one of the serious problems in 21'st century and proper accident analysis is required to improve vehicle safety and emergency response. In this research project the 'Car black Box System' for accident analysis using IoT is designed to record important data related to the vehicle, driver and its surrounding conditions during an accident. Several sets of systems have been used with multiple sensors to monitor real-time parameters such as alcohol level in driver's blood, vehicle motion, obstacle distance and location. A MEMS sensor is used to detect sudden impacts and abnormal movements, while an alcohol sensor checks driver intoxication. Ultrasonic sensors help in monitoring nearby obstacles to reduce accident risk. A potentiometer and push-button switch are used to simulate vehicle speed and gear conditions for testing purposes. An APR9600 voice recording module is used to record audio data before and during an accident, which provides additional evidence, such as driver reactions and surrounding sounds. GPS and GSM modules are used to track the vehicle location and automatically send emergency alert messages. Sensor data is uploaded to the cloud using Node MCU for monitoring and future analysis. This system acts as a digital witness and helps in accurate accident investigation and improved road safety.