Abstract
The rising self-driving technological innovations are viewed as brimming with challenges and opportunities because of its tremendous research territory. One of the challenges for the autonomous vehicle is straight and curve line detection to enhance the assistance in the autonomous characteristics. We will use a unique way of detecting a curve line algorithm in the vehicle based on the Kalman filter as well as the parabola equation model to calculate the parameters of the curve lane. For robust stability and performance, we will use an on-line sequential extreme learning machine method. We present our proposed result through the simulation study.
This research was supported by Unmanned Vehicles Advanced Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science, ICT & Future Planning, the Republic Of Korea (No. 2016M1B3A1A01937245). It is also supported by the Development Program through the National Research Foundation of Korea (NRF) (No. 2016R1D1A1B03935238). Also, this research was funded and conducted under 『the Competency Development Program for Industry Specialists』 of the Korean Ministry of Trade, Industry and Energy (MOTIE), operated by Korea Institute for Advancement of Technology (KIAT). (No. N0002428, HRD program for 00000).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Jeong-Gyu, K.: Changes of speed and safety by automated speed enforcement systems. IATSS Res. 26(2), 38–44 (2002)
Sehestedt, S.A., Kodagoda, S., Alempijevic, A., Dissanayake, G.: Efficient lane detection and tracking in urban environments. In: European Conference on Mobile Robots (2007)
Qiu, C.: An edge detection method of lane lines based on mathematical morphology and MATLAB. In: Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), vol. 2, pp. 1266–1269 (2011)
Assidiq, A.A.M., Khalifa, O.O., Islam, M.R., Khan, S.: Real time lane detection for autonomous vehicles. In: 2008 International Conference on Computer and Communication Engineering, ICCCE 2008, pp. 82–88 (2008)
Kano, H., Asari, K., Ishii, Y., Hongo, H.: Precise top view image generation without global metric information. IEICE Trans. Inf. Syst. 91(7), 1893–1898 (2008)
Dorj, B., Lee, D.J.: A precise lane detection algorithm based on top view image transformation and least-square approaches. J. Sens. 2016, Article ID 4058093, 13p. (2016). https://doi.org/10.1155/2016/4058093
Ganokratanaa, T., Ketcham, M., Sathienpong, S.: Real-time lane detection for driving system using image processing based on edge detection and Hough transform. In: The Third International Conference on Digital Information and Communication Technology and its Applications (DICTAP2013), pp. 104–109 (2013)
Tseng, C.-C., Cheng, H.-Y., Jeng, B.-S.: A lane detection algorithm using geometry information and modified Hough transform. In: 18th IPPR Conference on Computer Vision, Graphics and Image Processing, Taipei, Taiwan (2005)
Jung, C.R., Kelber, C.R.: A lane departure warning system based on a linear-parabolic lane model. In: 2004 Intelligent Vehicles Symposium. IEEE, pp. 891–895 (2004)
Markovsky, I., Willems, J.C., Van Huffel, S., De Moor, B.: Exact and Approximate Modeling of Linear Systems: A Behavioral Approach, vol. 11. SIAM (2006)
Podpora, M., Korbas, G.P., Kawala-Janik, A.: YUV vs RGB-choosing a color space for human-machine interaction. In: FedCSIS Position Papers, pp. 29–34 (2014)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9(1), 62–66 (1979)
Lim, K.H., Seng, K.P., Ang, L.-M., Chin, S.W.: Lane detection and Kalman-based linear-parabolic lane tracking. In: 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics. IHMSC, vol. 2, pp. 351–354 (2009)
Jung, C.R., Kelber, C.R.: An improved linear-parabolic model for lane following and curve detection. In: 2005 18th Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI 2005, pp. 131–138 (2005)
Liang, N.-Y., Huang, G.-B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17(6), 1411–1423 (2006)
Matias, T., Souza, F., Araújo, R., Antunes, C.H.: Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing 129, 428–436 (2014)
Doukhi, O., Fayjie, A.R., Lee, D.J.: Supervisory control of a multirotor drone using on-line sequential extreme learning machine. In: Proceedings of SAI Intelligent Systems Conference 2018, pp. 914–924 (2018)
Acknowledgment
My special thanks and gratitude to my supervisor Professor Deok-jin Lee for his guidance and support through this paper. I would also like to pay my deep sense of gratitude to all CAIAS (Center for Artificial Intelligence and Autonomous System) lab members for their support and CAIAS lab for providing me all the facilities that were required from the lab. I would also like to mention that the core idea for this paper comes from this paper referred at reference no. 17 written by Oualid Doukhi, Abdur R. Fayjie and Deok-jin Lee which was published in Proceedings of SAI Intelligent Systems Conference on page 914 to page 924 in Springer, Cham on 6th September 2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hossain, S., Doukhi, O., Lee, I., Lee, Dj. (2020). Real-Time Lane Detection and Extreme Learning Machine Based Tracking Control for Intelligent Self-driving Vehicle. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-29513-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29512-7
Online ISBN: 978-3-030-29513-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)