Fatigue Driver Alert System

Authors

  • Dr. Mallikarjun M Kodabagi Director of School of Computing and Information Technology, School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India Author
  • Afrah Ayub Director of School of Computing and Information Technology, School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India Author
  • Mahima BK Director of School of Computing and Information Technology, School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India Author
  • Shruthi Siva Director of School of Computing and Information Technology, School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India Author
  • Apurva Korni Director of School of Computing and Information Technology, School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India Author

DOI:

https://doi.org/10.32628/CSEIT24102112

Keywords:

Drowsy Driving, Alertness Monitoring, Drowsiness Prevention

Abstract

The rise in accidents caused by drivers who are too sleepy to drive has made advanced driver alert systems necessary. This research proposes a novel method for identifying drowsy drivers through the integration of GPS technology and physiological data, offering an inventive solution to this pressing problem. We explore the complexities of drowsiness detection, highlighting the difficulties and introducing an improved approach to improve performance. Using real-time GPS data, our system not only warns drivers when something is wrong, but it also suggests appropriate rest spots depending on the driver's present location. Our method, which emphasizes proactive measures to limit the risks associated with drowsy driving, pioneers safer driving habits by seamlessly combining physiological measurements, GPS technology, and algorithmic enhancements. Performance assessments show encouraging outcomes, highlighting the potential advantages and importance of these devices in lowering driver fatigue-related incidents. This research advances vehicle safety and emphasizes the need of taking preventative action to reduce the risks associated with sleepy driving.

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References

Chalermkiat Chanachan, Patthachaput Thanesmaneerat, Thanrada Mahasukon, Jumpol Povichai " Car Driver’s Behaviors Detections using Ensemble Model" in IEEE; <2023 ITC-CSCC, DOI: 10.1109>, March 2023. DOI: https://doi.org/10.1109/ITC-CSCC58803.2023.10212694

Binyan Zhang, “Design of Face Recognition Fatigue Driving Detection System Based on Improved YOLO Algorithm” ICMIII, July 2023. DOI: https://doi.org/10.1109/ICMIII58949.2023.00126

Afsar P, Dr Shanid Malayil, Aasha Verghese, Aysha Sithara, Amithab Pakarath, Bijoy, Ashmin K T “Real Time Student Emotion and Drowsy Detection using yolo v5 and CNN for Enhanced Learning ICSP, July 2023.

Li Lou, Tiantian Yue “Fatigue Driving Detection Based on Facial Features” ICSP, May 2023. DOI: https://doi.org/10.1109/ICSP58490.2023.10248862

Jinzhao Zhang “Human-computer Interaction for Driver Fatigue Detection through Micro-expressions”, ICSECE, April 2023 DOI: https://doi.org/10.1109/ICSECE58870.2023.10263425

Hassanat, A., Albustanji, A. A., Tarawneh, A. S., Alrashidi, M., Alharbi, H., & Alanazi, M. Deepveil: deep learning for identification of face, gender, expression recognition under veiled conditions. International Journal of Biometrics, vol. 2022, no. 3/4, pp. 14, 2022 DOI: https://doi.org/10.1504/IJBM.2022.124683

Zhang, Y., Xu, W., Yang, S., Xu, Y., & Yu, X. Improved yolox detection algorithm for contraband in x-ray images. Applied optics, vol. 2022, no. 21, pp. 61, 2022

Zhang, Y., Xu, W., Yang, S., Xu, Y., & Yu, X. Improved yolox detection algorithm for contraband in x-ray images. Applied optics, vol. 2022, no. 21, pp. 61, 2022 DOI: https://doi.org/10.1364/AO.461627

Yang, B., & Wang, J. An improved helmet detection algorithm based on yolo v4. International Journal of Foundations of Computer Science, vol. 33, no. 6, pp. 887-902, 2022. DOI: https://doi.org/10.1142/S0129054122420205

Zhang, Y., Wu, P., Zhao, J., Feng, H., & Liao, R. The model of fast face recognition against age interference in deep learning. International Journal of Biometrics, vol. 2022, no. 3/4, pp. 14, 2022. DOI: https://doi.org/10.1504/IJBM.2022.124668

Ma, L., & Zhang, Y. Research on vehicle license plate recognition technology based on deep convolutional neural networks. Microprocessors and microsystems, vol. 2021, no. 8, pp. 82, 2021. DOI: https://doi.org/10.1016/j.micpro.2021.103932

Kim, J., Ra, M., & Kim, W. Y. A dcnn-based fast nir face recognition system robust to reflected light from eyeglasses. IEEE Access, vol. 2020, no. 99, pp. 1-1, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2991255

Vinodini, R., & Karnan, M. Face detection and recognition system based on hybrid statistical, machine learning and nature-based computing. International Journal of Biometrics, vol. 2022, no. 1, pp. 14, 2022. DOI: https://doi.org/10.1504/IJBM.2022.119543

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Published

30-04-2024

Issue

Section

Research Articles

How to Cite

[1]
Dr. Mallikarjun M Kodabagi, Afrah Ayub, Mahima BK, Shruthi Siva, and Apurva Korni, “Fatigue Driver Alert System”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 2, pp. 735–740, Apr. 2024, doi: 10.32628/CSEIT24102112.

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