Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Lane Recognition Using Lane Prominence Algorithm for Unmanned Vehicles

무인차량 적용을 위한 차선강조기법 기반의 차선 인식

  • 백준영 (부산대학교 기계공학부 제어자동화시스템) ;
  • 이민철 (부산대학교 기계공학부)
  • Received : 2010.03.15
  • Accepted : 2010.04.30
  • Published : 2010.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes lane recognition algorithm using lane prominence technique to extract lane candidate. The lane prominence technique is combined with embossing effect, lane thickness check, and lane extraction using mask. The proposed lane recognition algorithm consists of preprocessing, lane candidate extraction and lane recognition. First, preprocessing is executed, which includes gray image acquisition, inverse perspective transform and gaussian blur. Second, lane candidate is extracted by using lane prominence technique. Finally, lane is recognized by using hough transform and least square method. To evaluate the proposed lane recognition algorithm, this algorithm was applied to the detection of lanes in the rainy and night day. The experiment results showed that the proposed algorithm can recognize lane in various environment. It means that the algorithm can be applied to lane recognition to drive unmanned vehicles.

Keywords

References

  1. A. Broggi, "Robust real-time lane and road detection in critical shadow condtions," IEEE International Symposium on Computer Vision, pp. 353-358, 1993.
  2. J. P. Gonzalez and U. Ozguner, "Lane detection using histogram based segmentation and decision trees," IEEE Intelligent Transportation System, pp. 346-351, 2000.
  3. 김덕래, 문호선, 김용득, "B-Snake를 이용한 차선 검출 및 추적 알고리즘에 관한 연구," 전자공학회 논문지, 제42권 제4호, pp. 21-30, 2005.
  4. M. Betozzi and A. Broggi, "Real-time lane and obstacle detection the GOLD system," IEEE Intelligent Vehicles Symposium, pp. 213-218, 1993.
  5. M. Tian, F. Liu, and Z. Hu, "Single camera 3D lane detection and tracking based on EKF for urban intelligent vehicle," IEEE Vehicular Electronics and Safety, pp. 413-418, 2006.
  6. M. A. Sotelo and F. J. Rodriguez, "A color vision-based lane tracking sustem for autonomous driving in unmarked road," Autonomous Robot, vol. 16, pp. 95-116, Jan. 2004. https://doi.org/10.1023/B:AURO.0000008673.96984.28
  7. 이준웅, 이운근, "차선관련 파라미터의 대칭성과 선형 회귀에 기반한 차선이탈 인식," 제어 . 자동화 . 시스템 공학회 논문지, 제11권 제5호, pp. 435-444, 2005. https://doi.org/10.5302/J.ICROS.2005.11.5.435
  8. 서승범, 강연식, 노치원, 강성철, "도심 자율주행을 위한 비전기반 차선 추종주행 실험," 제어 . 로봇 . 시스템 학회 논문지, 제15권 제5호, pp. 480-487, 2009. https://doi.org/10.5302/J.ICROS.2009.15.5.480
  9. K. R. S. Kodagoda, W. S. Wijesom, and A. P. Balasuriya, "CuTE : Curb tracking and estimation," IEEE Transaction on Control System Technology, vol. 12, no. 5, pp. 951-957, Sep. 2006.
  10. K. R. S. Kodagoda, S. S. Ge, W. S. Wihesom, and A. P. Balasuriya, "IMMPDAF approach for road-boundary tracking," IEEE Transaction on Vehicular Technology, vol. 56, no. 2, pp. 478-153, Mar. 2007. https://doi.org/10.1109/TVT.2007.891426
  11. X. Chen, E. L. Dagless, S. J. Zhang, and B. T. Tomas, "A real-time plan-view method for following bending road," IEEE Intelligent Vehicles Symposium, pp. 219-224, 1993.
  12. A. Broggi, "A massively parallel approach to real-time cision-based road makring detection," IEEE Intelligent Vehicles Symposium, pp. 84-89, 2000.