Designing a Vehicle Accident Detection Device for Two-Wheeled Vehicles Using Vibration Values and Tilt Angles Based on the Internet of Things (IoT) System
Keywords:
Internet of things, NodeMCU ESP8266, GY-521 MPU-6050, SW-420, GPS BN 220Abstract
Traffic accidents in Indonesia have been increasing every year. To address and minimize the high number of traffic accidents, especially involving two-wheeled vehicles, a device is needed to warn drivers when an accident is about to occur. The purpose of this study is to develop an accident detection device utilizing Internet of Things (IoT) technology by observing the angle of inclination and vibration values of two-wheeled vehicles. The research method used is R&D Research and Development, which follows the Borg and Gall development model. This model includes: 1) Literature review, 2) Planning, 3) Design creation, 4) Limited testing, 5) Revision, 6) Testing and feasibility testing, 7) Final revision, and 8) Dissemination and implementation. The research results explain that to measure vehicle vibration values, the SW-420 sensor was used with vibration values determined by the researcher at 2,000 Hz, 10,000 Hz, 30,000 Hz, and 40,000 Hz. For measuring the tilt angle of the vehicle, the GY 521 MPU-6050 sensor was used with tilt angle values determined by the researcher at 10°, 30°, 45°, and 50°. In conclusion, the accident detection device operates normally under normal vehicle conditions, and the vehicle tilt angle range remains between 10° and 30°. Under normal vehicle conditions, the vibration sensor on the vehicle does not fall within the range of 6.016–8.120. Meanwhile, the accident detection device will immediately activate and send an automatic message via WhatsApp to the user when the vehicle's tilt angle is between 45° and 50°, indicating that the vehicle has fallen. Regarding vibration values, the accident detection device will send an automatic message via the user's WhatsApp when the vibration range is between 10,558 and 60,850, indicating that the vehicle has fallen.
References
[1] V. Goud, "Vehicle Accident Automatic Detection and Remote Alarm Device," International Journal of Reconfigurable and Embedded Systems (IJRES), vol. 1, no. 2, 2012, doi: 10.11591/ijres.v1.i2.pp49-54.
[2] M. K. J. Al Mukhaini and K. Al Harthy, "A Proposal for an Advanced Smart System of Vehicle Accidents Detection," Journal of Student Research, 2020, doi: 10.47611/jsr.vi.959.
[3] C. Dashora, P. E. Sudhagar, and J. Marietta, "IoT Based Framework for the Detection of Vehicle Accident," Cluster Computing, vol. 23, no. 2, 2020, doi: 10.1007/s10586-019-02989-z.
[4] N. Pathik, R. K. Gupta, Y. Sahu, A. Sharma, M. Masud, and M. Baz, "AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities," Sustainability (Switzerland), vol. 14, no. 13, 2022, doi: 10.3390/su14137701.
[5] M. Balfaqih, S. A. Alharbi, M. Alzain, F. Alqurashi, and S. Almilad, "An Accident Detection and Classification System Using Internet of Things and Machine Learning Towards Smart City," Sustainability (Switzerland), vol. 14, no. 1, 2022, doi: 10.3390/su14010210.
[6] R. A. Jangle, S. S. Girpunje, and G. M. Thorat, "An IoT Based Vehicle Accident Detection and Alert System," Turkish Journal of Computer and Mathematics Education, vol. 12, no. 7, 2021.
[7] J. A. J. Alsayaydeh, M. F. Bin Yusof, M. A. A. Bin Abdillah, A. J. A. Al-Gburi, S. G. Herawan, and A. Oliinyk, "Enhancing Vehicle Safety: A Comprehensive Accident Detection and Alert System," International Journal of Advanced Computer Science and Applications, vol. 14, no. 11, 2023, doi: 10.14569/IJACSA.2023.0141104.
[8] R. Kumar, A. Tomar, and M. Lal, "Enhancing Vehicle Safety: Automated Accident Detection and Emergency Alert System," Journal of Universal Computer Science, no. June 2023.
[9] B. Rani, R. Praveen Sam, and G. R. Kamatam, "A Review on Vehicle Tracking and Accident Detection System Using Accelerometer," International Journal of Applied Engineering Research, vol. 13, no. 11, 2018, doi: 10.37622/ijaer/13.11.2018.9215-9217.
[10] A. Mateen, M. Z. Hanif, N. Khatri, S. Lee, and S. Y. Nam, "Smart Roads for Autonomous Accident Detection and Warnings," Sensors, vol. 22, no. 6, 2022, doi: 10.3390/s22062077.
[11] V. Saritha, S. L. Chandana, U. S. B. K. Mahalaxmi, D. Shamia, P. Chawla, and A. Chaturvedi, "An Intelligent Sensing and Detection System for Accident Preventions in Four-Wheeler Vehicles," Materials Today: Proceedings, vol. 80, 2023, doi: 10.1016/j.matpr.2021.05.353.
[12] N. Kumar, D. Acharya, and D. Lohani, "An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion," IEEE Internet of Things Journal, vol. 8, no. 2, 2021, doi: 10.1109/JIOT.2020.3008896.
[13] A. Vangala, B. Bera, S. Saha, A. K. Das, N. Kumar, and Y. Park, "Blockchain-Enabled Certificate-Based Authentication for Vehicle Accident Detection and Notification in Intelligent Transportation Systems," IEEE Sensors Journal, vol. 21, no. 14, 2021, doi: 10.1109/JSEN.2020.3009382.
[14] M. I. Basheer Ahmed, et al., "A Real-Time Computer Vision Based Approach to Detection and Classification of Traffic Incidents," Big Data and Cognitive Computing, vol. 7, no. 1, 2023, doi: 10.3390/bdcc7010022.
[15] D. Tian, C. Zhang, X. Duan, and X. Wang, "An Automatic Car Accident Detection Method Based on Cooperative Vehicle Infrastructure Systems," IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2939532.
[16] U. Alvi, M. A. K. Khattak, B. Shabir, A. W. Malik, and S. R. Muhammad, "A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles," IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3006887.
[17] P. Josephinshermila, S. Sharon Priya, K. Malarvizhi, R. Hegde, S. Gokul Pran, and B. Veerasamy, "Accident Detection Using Automotive Smart Black-Box Based," Measurement: Sensors, vol. 27, 2023, doi: 10.1016/j.measen.2023.100721.
[18] W. J. Chang, L. B. Chen, and K. Y. Su, "DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification," IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2946468.
