Advertisement

Robust Road Lane Detection for High Speed Driving of Autonomous Vehicles

  • Hyunhee ParkEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

With the Hough transform and region of interest, how to improve the processing time of high-speed driving is being actively investigated. This study proposes a road lane detection algorithm based on expressway driving videos through a computer vision-based image processing system without using sensors. The proposed method detects straight lines that are estimated to be lanes using the Hough transform. When lanes are detected from actual images, the scope of left and right lanes is limited to reduce computational load. Extensive simulation results are given to show the effects of Hough transform method for high speed driving and region of interest for processing time on actual expressways.

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1C1B5017556).

References

  1. 1.
    Park, H.: Implementation of lane detection for self-driving vehicles using GPS. J. Korean Inst. Commun. Inf. Sci. 43, 18 (2018)Google Scholar
  2. 2.
    Labayrade, R., Douret, J., Aubert, D.: A multi-model lane detector that handles road singularities. In: Proceedings of the IEEE Conference on Intelligent Transportation Systems, Toronto, Canada, pp. 1143–1148 (2006)Google Scholar
  3. 3.
    McDonald, J., Shorten, R., Franz, J.: Application of the Hough transform to lane detection in motorway driving scenarios. In: Proceedings of the Irish Signals and Systems Conference (2001)Google Scholar
  4. 4.
    Yadav, S., Patra, S., Arora, C., Banerjee, S.: Deep CNN with color lines model for unmarked road segmentation. In: Proceedings of the IEEE International Conference on Image Processing (2017)Google Scholar
  5. 5.
    Guo, C., Mita, S., McAllester, D.: Robust road detection and tracking in challenging scenarios based on Markov random fields with unsupervised learning. IEEE Trans. ITS 13, 1338–1354 (2012)Google Scholar
  6. 6.
    Tsai, L., Hsieh, J., Chuang, C., Fan, K.: Lane detection using directional random walks. In: Proceedings of IEEE Intelligent Vehicles Symposium (2008)Google Scholar
  7. 7.
    Li, Q., Zheng, N., Cheng, H.: Spring robot: a prototype autonomous vehicle and its algorithms for lane detection. IEEE Trans. Intell. Transp. Syst. 5, 300–308 (2004)CrossRefGoogle Scholar
  8. 8.
    Wang, J., Ji, Z., Su, Y.: Unstructured road detection using hybrid features. In: Proceedings of the International Conference on Machine Learning and Cybernetics (2009)Google Scholar
  9. 9.
    Yun, S., Guo-Ying, Z., Yong, Y.: A road detection algorithm by boosting using feature combination. In: Proceedings of the Intelligent Vehicles Symposium (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer SoftwareKorean Bible UniversitySeoulSouth Korea

Personalised recommendations