Road accidents result in thousands of fatalities annually, primarily due to delayed emergency response and the absence of systematic event recording in vehicles. This paper presents the design and implementation of an Embedded Blackbox System for Vehicles integrated with multi-sensor event logging and IoT-based cloud monitoring. The system continuously monitors critical vehicle parameters including vibration (accident detection), temperature (overheating), speed (overspeed), and alcohol level (drunk driving) using an ESP32/Arduino microcontroller. Upon detection of an abnormal event, the blackbox activates automatically, logs the sensor data with a timestamp, and transmits an emergency alert to a cloud platform via Wi-Fi. Additionally, the system incorporates automatic speed control in restricted zones such as schools and hospitals, reducing vehicle speed through a motor driver (L298N) upon zone detection. Real-time data is streamed to an IoT dashboard for remote monitoring and post- event analysis. Experimental results demonstrate reliable accident detection within 200ms, timely cloud alert dispatch, and accurate sensor readings under test conditions. The proposed system offers a low- cost, scalable solution for enhancing vehicle safety and improving emergency response time.

This paper presented the design and implementation of an Embedded Vehicle Blackbox system integrating multi-sensor fusion, real-time event logging, IoT-based cloud emergency notification, and automatic speed control in a single low-cost embedded platform. Experimental results confirmed reliable accident detection within 200ms, automatic speed reduction in restricted zones, and real-time data streaming via Wi-Fi/MQTT. The system provides timestamped blackbox records as digital evidence for accident investigation, addressing a critical gap in existing road vehicle technology.

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