Development of a Driver Assistant and Vehicle Sensory System with Vehicle Fine Management

Dulanjaya N.K.C.Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaAbeysiriwardena V.C.Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaMadushanka R.M.R.A.Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaNimesh K.C.Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaMrs. Hansika MahadikaraFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaMs. Suranjini SilvaFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 7 No 10 (2023): Volume 7, Issue 10, October 2023 | Pages: 108-114

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 26-10-2023

doi Logo doi.org/10.47001/IRJIET/2023.710014

Abstract

Innovative solutions targeted at improving traffic safety and driver wellbeing have been made possible by developments in computer vision and artificial intelligence. Through the creation of a driver warning and road sign recognition system as well as an enhanced eye health monitoring module, this study offers a holistic strategy to address important aspects of road safety. This project develops a coherent and efficient driver assistance system by integrating real-time image processing, neural networks, and driver behavior assessment using Python and OpenCV.

Keywords

OpenCV, Neural Networks, Image Processing, real-world adoption, COLOV Neural Network


Citation of this Article

Dulanjaya N.K.C., Abeysiriwardena V.C., Madushanka R.M.R.A., Nimesh K.C., Mrs. Hansika Mahadikara, Ms. Suranjini Silva, “Development of a Driver Assistant and Vehicle Sensory System with Vehicle Fine Management” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 10, pp 108-114, October 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.710014

References
  1. M. Chen et al., “Disease Prediction by Machine Learning over Big Healthcare Data,” IEEE Access, vol. 5, no. 1, 2017, pp. 8869–79.
  2. E. Fotopoulou et al., “Linked Data Analytics in Interdisciplinary Studies: The Health Impact of Air Pollution in Urban Areas,” IEEE Access, vol. 4, 2016, pp. 149–164.
  3. D. Santi, E. Magnani, M. Michelangeli, R. Grassi, B. Vecchi, G. Pedroni, L. Roli, M. C. De Santis, E. Baraldi, and M. Setti, “Seasonal variation of semen parameters correlates with environmental temperature and air pollution: A big data analysis over 6 years,” Environ. Pollut., vol. 235, pp. 806–813, 2018.
  4. P. Kumar, L. Morawska, C. Martani, G. Biskod, M. Neophytou, S. D. Sabatino, M. Bell, L. Norford, and R. Britter, “The rise of low-cost sensing for managing air pollution in cities,” Journal of Environment International, vol. 75, no. 2, pp. 199 – 205, December 2014.
  5. Kgoputjo Simon Elvis Phala, Anuj Kumar, and Gerhard P.Hancke, “Air Quality Monitoring System Based on ISO/IEC/IEEE 21451 Standards”, IEEE Sensors Journal, Vol. 16, No. 12, June 15, 2016.
  6. S. Dhingra, R. B. Madda, A. H. Gandomi, R. Patan and M. Daneshmand, "Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5577-5584, June 2019, doi: 10.1109/JIOT.2019.2903821.
  7. L. Spinelle, M. Gerboles, M. G. Villani, M. Aleixandre, and F. Bonavitacola, “Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2,” Sensors Actuators B Chem., vol. 238, pp. 706–715, 2017.
  8. Chen, Y., Wang, T., Zeng, W., & Qin, S. (2020). A Survey of Autonomous Vehicles: Recent Advances, Challenges, and Prospects. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3315-3337.
  9. Li, J., Liu, Y., & Zhang, T. (2019). Deep Learning for Object Detection: A Comprehensive Review. Neurocomputing, 338, 149-167.
  10. Bhuvaneshwari, G., Kumaravel, A., & Jeyanthi, N. (2021). Blockchain-based Secure and Privacy-Preserving E-Toll Collection System for Vehicular Ad-hoc Networks. Computers & Security, 105, 102241.
  11. Wang, X., & Wang, F. (2019). Autonomous Vehicle Simulators: A Comprehensive Survey. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3640-3658.
  12. Zhang, Y., & Lin, L. (2021). A Survey on Traffic Sign Detection and Recognition. IEEE Transactions on Intelligent Transportation Systems, 22(5), 2851-2872.
  13. Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767.
  14. Wong, A., Lee, V. C., & Abdullah, A. S. (2020). Real-Time Traffic Sign Detection Using Deep Learning Techniques. IEEE Access, 8, 91594-91606.
  15. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
  16. Zhang, T., Wang, K., Yin, H., & Jiang, L. (2020). A Review of Driver Assistance Systems: Functions, Advances, and Challenges. IEEE Transactions on Intelligent Transportation Systems, 21(7), 2966-2986.
  17. Wang, C., Ding, C., Sun, Y., & Jiang, X. (2021). Driver Fatigue Detection and Identification Based on Machine Learning. IEEE Access, 9, 104104-104117.
  18. Sanyal, S., & Mitra, S. (2020). Detection of Drowsiness in Real-time from Face Images using Convolutional Neural Networks. Expert Systems with Applications, 138, 112843.
  19. American Academy of Ophthalmology. (2021). How to Protect Your Eyes During Long Road Trips. [Online]. Available: https://www.aao.org/eye-health/tips-prevention/road-trip-eyes.
  20. National Highway Traffic Safety Administration (NHTSA). (2021). Drowsy Driving. [Online]. Available: https://www.nhtsa.gov/risky-driving/drowsy-driving