Skip to main content

Design of an Intelligent Driving Support System for Detecting Distracted Driving

  • Conference paper
  • First Online:
Complex, Intelligent and Software Intensive Systems (CISIS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 278))

Included in the following conference series:

Abstract

In this paper, we propose an Artificial Intelligence (AI)-based driving support system for detecting distracted driving and increasing the safe driving. We classify the hands of driver and smartphones for detecting the distracted status. We evaluate the proposed system by experiments. The experimental results show that YOLOv5-based distracted driving detection method has a good performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kaggle: Data science community. https://www.kaggle.com/

  2. Bergasa, L.M., Almeria, D., Almazan, J., Yebes, J.J., Arroyo, R.: DriveSafe: an app for alerting inattentive drivers and scoring driving behaviors. In: 2014 Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 240–245 (2014)

    Google Scholar 

  3. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. Computer Vision and Pattern Recognition (cs.CV), April 2020. https://arxiv.org/abs/2004.10934

  4. Ersal, T., Fuller, H.J.A., Tsimhoni, O., Stein, J.L., Fathy, H.K.: Model-based analysis and classification of driver distraction under secondary tasks. IEEE Trans. Intell. Transp. Syst. 11(3), 692–701 (2010)

    Article  Google Scholar 

  5. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  6. Le, Q.V.: Building high-level features using large scale unsupervised learning. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2013 (ICASSP-2013), pp. 8595–8598, May 2013

    Google Scholar 

  7. Liu, T., Yang, Y., Huang, G.B., Yeo, Y.K., Lin, Z.: Driver distraction detection using semi-supervised machine learning. IEEE Trans. Intell. Transp. Syst. 17(4), 1108–1120 (2016)

    Article  Google Scholar 

  8. McCall, J.C., Trivedi, M.M.: Driver behavior and situation aware brake assistance for intelligent vehicles. Proc. IEEE 95(2), 374–387 (2007)

    Article  Google Scholar 

  9. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)

    Article  Google Scholar 

  10. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2016), pp. 779–788, June 2016

    Google Scholar 

  11. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)

    Article  Google Scholar 

  12. Silver, D., et al.: Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017)

    Article  Google Scholar 

  13. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR-2015), May 2015

    Google Scholar 

  14. State Farm: Dataset of state farm distracted driver detection (2016). https://www.kaggle.com/c/state-farm-distracted-driver-detection/

  15. Vicente, F., Huang, Z., Xiong, X., la Torre, F.D., Zhang, W., Levi, D.: Driver gaze tracking and eyes off the road detection system. IEEE Trans. Intell. Transp. Syst. 16(4), 2014–2027 (2015)

    Article  Google Scholar 

  16. Wang, Y.K., Jung, T.P., Lin, C.T.: EEG-based attention tracking during distracted driving. IEEE Trans. Neural Syst. Rehabil. Eng. 23(6), 1085–1094 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Makoto Ikeda .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Miwata, M., Tsuneyoshi, M., Tada, Y., Ikeda, M., Barolli, L. (2021). Design of an Intelligent Driving Support System for Detecting Distracted Driving. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_37

Download citation

Publish with us

Policies and ethics