Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Vision Sensing for the Ego-Lane Detection of a Vehicle

자동차의 자기 주행차선 검출을 위한 시각 센싱

  • Kim, Dong-Uk (Department of Electronic Engineering, Graduate School, Daegu University) ;
  • Do, Yongtae (School of Electronic & Electrical Engineering, Daegu Unversity)
  • 김동욱 (대구대학교 대학원 전자공학과) ;
  • 도용태 (대구대학교 전자전기공학부)
  • Received : 2018.03.16
  • Accepted : 2018.03.28
  • Published : 2018.03.31
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Abstract

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.

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References

  1. S.-C. Huang, et al., "Smart car [Application notes]", IEEE Comput. Intell. Mag., Vol. 11(4), pp. 46-58, 2016. https://doi.org/10.1109/MCI.2016.2601758
  2. A. Najmi, et al., "Pulsed LIDAR for obstacle detection in the automotive field: The measurement of reflectance range data in scene analysis", Sens. Actuators A Phys., Vol. 47(1-3), pp. 497-500, 1995. https://doi.org/10.1016/0924-4247(94)00950-M
  3. J. Hasch, et al., "Millimeter-wave technology for automotive radar sensors in the 77 GHz frequency band", IEEE Trans. Microw. Theory Tech., Vol. 60(3), pp. 845-860, 2012. https://doi.org/10.1109/TMTT.2011.2178427
  4. J. K. Suhr, et al., "Automatic free parking space detection by using motion stereo-based 3D reconstruction", Mach. Vis. Appl., Vol. 21(2), pp. 163-176, 2010. https://doi.org/10.1007/s00138-008-0156-9
  5. N. M. Arshad, et al., "Single infra-red sensor technique for line-tracking autonomous mobile vehicle", Proc. of IEEE 7th Int'l Colloquium on Signal Processing and Its Applications(CSPA), Penang, 2011.
  6. K. Saadeddin, M. F. Abdel-Hafez, M. A. Jarrah, "Estimating vehicle state by GPS/IMU fusion with vehicle dynamics", J. Intell. Robot. Syst., Vol. 74(1-2), pp 147-172, 2014. https://doi.org/10.1007/s10846-013-9960-1
  7. M. H. Sohn, Y. Do, "Vision-based mobile robot navigation by robust path line tracking", J. Sensor Sci. & Tech., Vol. 20(11), pp. 178-186, 2011. https://doi.org/10.5369/JSST.2011.20.3.178
  8. S.-W. Park, B.-S. Song, Y. Do, " Video-based walking distance measurement for the visually impaired", J. Sensor Sci. & Tech., Vol. 15(2), pp. 139-147, 2009. https://doi.org/10.5369/JSST.2006.15.2.139
  9. S. C. Yi, Y. C. Chen, C. H. Chang, "A lane detection approach based on intelligent vision", Comput. Electr. Eng., Vol. 42, pp. 23-29, 2015. https://doi.org/10.1016/j.compeleceng.2015.01.002
  10. W. Phueakjeen, et al., "A study of the edge detection for road lane", Proc. of IEEE Int'l Conf. on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 995-998, 2011.
  11. J. Wang, et al., "Lane detection based on random Hough transform on region of interesting", Proc. of IEEE Int'l Conf. on Information and Automation (ICIA), pp. 1735-1740, 2010.
  12. J. Canny, "A computational approach to edge detection", IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8(6), pp. 679-698, 1986.
  13. https://kr.mathworks.com/help/matlab/ref/rgb2gray.html (retrieved on Mar. 16, 2018).