Autors: Filipova-Petrakieva, S. K., Yanev P.
Title: Driver Assistance System, based on the Convolutional Neural Network
Keywords: Convolutional Neural Network (CNN), deep learning, Driver Assistance Systems (DAS), machine learning

Abstract: The continuously rising number of deaths due to car accidents on the roads is a critical problem nowadays. To reduce the number of risky situations on the road, it is necessary to increase the attention of drivers when driving motor vehicles. In this context, an advisory system to assist driving is proposed in this paper. The mathematical model uses a convolutional neural network to recognize traffic signs and detect real road situations. It is implemented using Python in the Jupyter Notebook environment. The following libraries were used: Tensorflow (Tensors and Keras libraries, for machine learning and deep learning, respectively), Tkinter (GUI library), Pillow (image processing library), matplotlib (data visualization library), NumPy (library for performing complex computations), and Pandas (data analysis and processing library). The developed application is installed on the onboard vehicle computer. The detected sign is displayed in an appropriate form on the automobile dashboard. Simulations of real traffic situations are implemented in the system. All this will facilitate driving and help drivers with less experience acquire the necessary driving reactions.

References

  1. F. Arena, G. Pau, “An Overview of Vehicular Communications”, Future Internet, vol. 11, no. 27, DOI: 10.3390/fi11020027, 2019
  2. К. Lim, Y. Hong, Y. Choi, H. Byun, „Real-time traffic sign recognition based on a general purpose GPU and deep-learning“, PLoS One, vol. 12, no. 3, 2017.
  3. https://cecas.clemson.edu/cvel/auto/systems/sign-recognition.html
  4. M. Galvani, "History and future of driver assistance," IEEE Instrumentation & Measurement Magazine, vol. 22, no. 1, e-ISSN: 1941-0123, Print ISSN: 1094-6969, DOI: 10.1109/MIM.2019.8633345, 2019, pp. 11-16.
  5. https://download.tomtom.com/open/banners/Whitepaper-ADAS-MapSpeed-Limits-Improve-mandatory-Intelligen-Speed-Assistance-andvehicle-safety-ratings.pdf
  6. https://www.extrapolate.com/blog/top-10-advanced-driver-assistancesystem-companies-2024
  7. R. Venkatesan, B. Li., Convolutional Neural Networks in Visual Computing: A Concise Guide, CRC Press, ISBN: 978-1-351-65032-8, 2017.
  8. N. Christianini, J. Shawe-Taylor, An introduction to support vector machines: and other kernel-based learning methods, Cambridge and New York: Cambridge University Press, e-ISBN: 9780511801389
  9. D. Meyer, Fr. Leisch, and K. Hornik, “The support vector machine under test”, Neurocomputing, vol. 55, no. 1–2, ISSN: 0925-2312, DOI: 10.1016/S0925-2312(03)00431-4, 2003, pp. 169-186.
  10. B. Klein, "GUI Programming with Python: Events and Binds www.python-course.eu
  11. K. Packard, The Snek Programming Language: A Python-inspired Embedded Computing Language, Version v1.9, 2022-12-20, p. 89, 2024 https://sneklang.org/doc/snek.pdf
  12. K. J. Millman, M. Aivazis, "Python for Scientists and Engineers”, Computing in Science and Engineering, vol. 13, no. 2, pp. 9–12, DOI: 10.1109/MCSE.2011.36., 2011.
  13. J. M. Perkel, "Why Jupyter is data scientists' computational notebook of choice", Nature, 563 (7729), pp. 145–146, 2018.
  14. K. Sherrer, "Google Colab vs Jupyter Notebook: Compare data science software", TechRepublic, 2022.
  15. M. Abadi, P. Barham, J. Chen, Zh. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, Sh. Moore, D. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, TensorFlow: A System for Large-Scale Machine Learning, p. 18, DOI: 10.48550/arXiv https://arxiv.org/abs/1605.08695
  16. D. Fleck, Tkinter – GUIs in Python, CS112, George Mason University https://cs.gmu.edu/~dfleck/classes/cs112/spring08/slides/tkinter.pdf
  17. https://pillow.readthedocs.io/en/stable/
  18. https://matplotlib.org/stable/index.html
  19. https://numpy.org/doc/stable/
  20. W. McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd ed., Sebastopol: O'Reilly. ISBN: 978-1-4919-5766-0, 2017.

Issue

2024 16th Electrical Engineering Faculty Conference, BulEF 2024, 2024, , https://doi.org/10.1109/BULEF63204.2024.10794866

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Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus