Lightweight Vehicle Black Box System with Automatic Emergency Assistance

Abstract

In the context of increasing road traffic incidents and the demand for smarter transportation systems, the need for advanced vehicle monitoring solutions has become critical. This paper presents the design and development of an IoT-based Vehicle Black Box system that records and transmits real-time vehicular data to support accident analysis and emergency response. The proposed system integrates various sensors, including an accelerometer, GPS module, alcohol sensor, and microcontroller, to collect data such as speed, impact force, location, and driver condition. Through IoT connectivity, this information is stored in a cloud database, enabling remote access and live monitoring. In the event of an accident, the system automatically triggers an emergency alert containing the vehicle’s coordinates, significantly reducing the time taken for rescue and investigation. The system offers a scalable and cost-effective solution for both personal and commercial vehicles, with potential applications in fleet management, insurance validation, and driver behavior analysis. Its modular architecture allows easy integration with additional technologies such as predictive analytics and Vehicle-to-Everything (V2X) communication. Moreover, the secure and tamper-resistant data logging ensures the reliability and integrity of evidence in post-accident scenarios. The experimental results demonstrate the system\'s effectiveness in capturing critical information in real-time, making it a valuable tool in advancing road safety and intelligent transportation frameworks.

Introduction

Road traffic accidents cause millions of injuries and deaths worldwide, but current automotive technologies lack reliable real-time monitoring and data collection, limiting accident investigation and emergency response. To overcome this, the concept of a “vehicle black box”—similar to aviation flight recorders—is gaining popularity. These systems record critical data such as speed, location, acceleration, and impact forces during accidents. However, many existing solutions only store data locally and lack real-time connectivity and affordability.

Integrating Internet of Things (IoT) technology offers a powerful solution by enabling real-time data transmission, cloud storage, and instant emergency alerts. This paper proposes an IoT-enabled vehicle black box system that continuously monitors vehicle parameters using sensors (accelerometer, gyroscope, GPS, alcohol sensor) and a microcontroller (ESP8266). The system detects accidents based on sudden acceleration spikes, automatically sends alerts with GPS coordinates to emergency contacts, and stores data both locally and in the cloud for redundancy and analysis.

The project aims to create a low-cost, scalable solution for accident investigation, driver behavior monitoring, and fleet management. Various related works demonstrate the effectiveness of IoT-based black boxes in improving road safety, accident reconstruction, and emergency response. The proposed system architecture combines hardware components (sensors, microcontroller, GPS, Wi-Fi module) with cloud-based software platforms for remote monitoring and notifications.

Test results show the system successfully tracks vehicle dynamics, detects crashes, and provides accurate real-time location data, with potential for future upgrades like camera integration. Overall, this IoT-enabled black box improves vehicle safety by enabling fast emergency alerts, reliable data collection, and remote monitoring.

Conclusion

This paper presented the development and implementation of an IoT-based Vehicle Black Box System designed to enhance vehicular safety, monitoring, and post-accident analysis. The system successfully logged key parameters, such as acceleration, alcohol, and location data, and transmitted them to a remote cloud server using Wi-Fi technology. The results validate the system\'s effectiveness, reliability, and potential for real-world deployment. Future improvements may include the incorporation of video capture, AI-based accident detection, or integration with emergency response systems for automated alerts.

References

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Copyright

Copyright © 2025 Deeksha K., Jeyamani J., Ranganathan V., Harikrishnan K., Shoba M.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.