IoT-based Black Box Monitoring for Vehicle Crash Data Analysis and Improving Safety


Year : 2024 | Volume : 12 | Issue : 03 | Page : 7-13
    By

    Akash Sanjay Patil,

  • Hashmi Syed naim,

  • Reshal Naresh Waghmare,

  • Suvarna Rohile,

  1. Student, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, Maharashtra, India
  2. Student, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, Maharashtra, India
  3. Student, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, Maharashtra, India
  4. Assistant Professor, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, Maharashtra, India

Abstract

Road accidents are a significant global concern. This project proposes an Internet of Things (IoT)-based black box monitoring system for vehicles to enhance road safety through comprehensive crash data analysis. The system expands on traditional black boxes by incorporating various sensors (accelerometers, gyroscopes, GPS) and potentially in-cabin cameras (with privacy safeguards). This data offers a deeper understanding of crash dynamics, including impact severity, vehicle motion, and safety system deployment timing. The system works by continuously monitoring the vehicle’s performance and the driver’s behavior using various sensors. In the event of an accident, the system will automatically detect the impact force using the piezoelectric sensor, take a photo using the camera, record the vehicle’s location using the GPS, and measure the speed using the DC motor as a wheel. The system will then send this data to the cloud using Blynk IoT, where it can be accessed by emergency services and other authorized personnel. The examination of the gathered data will be utilized to enhance forthcoming vehicle safety functionalities, including automated emergency braking and alerts for lane departure. Additionally, the system can optionally monitor driver behavior (with user consent) to identify risky habits and provide feedback for safer driving practices. This project has the potential to significantly improve road safety by providing valuable insights for developing safer vehicles and promoting better driving habits. Nevertheless, factors such as safeguarding data privacy, ensuring security, managing storage, and establishing industry-wide standardization must be dealt with for effective execution.

Keywords: IoT-based black box, vehicle crash data analysis, ESP32 microcontroller, GSP module, piezoelectric sensor, accelerometer sensor, Blynk IoT platform

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications ]

How to cite this article:
Akash Sanjay Patil, Hashmi Syed naim, Reshal Naresh Waghmare, Suvarna Rohile. IoT-based Black Box Monitoring for Vehicle Crash Data Analysis and Improving Safety. Research & Reviews: A Journal of Embedded System & Applications. 2024; 12(03):7-13.
How to cite this URL:
Akash Sanjay Patil, Hashmi Syed naim, Reshal Naresh Waghmare, Suvarna Rohile. IoT-based Black Box Monitoring for Vehicle Crash Data Analysis and Improving Safety. Research & Reviews: A Journal of Embedded System & Applications. 2024; 12(03):7-13. Available from: https://journals.stmjournals.com/rrjoesa/article=2024/view=172085


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 24/04/2024
Accepted 21/06/2024
Published 14/09/2024
Total Visits: 152


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