Transactions of the Korean Society of Automotive Engineers (한국자동차공학회논문집)

The Korean Society of Automotive Engineers (한국자동차공학회)
권호 표지
DOI QR코드

DOI QR Code

Road Surface Marking Detection for Sensor Fusion-based Positioning System

센서 융합 기반 정밀 측위를 위한 노면 표시 검출

  • Kim, Dongsuk (The Research Institute of Automotive Electronics and Control, Hanyang University) ;
  • Jung, Hogi (Department of Automotive Engineering, Hanyang University)
  • 김동석 (한양대학교 자동차전자제어연구소) ;
  • 정호기 (한양대학교 미래자동차공학과)
  • Received : 2014.04.25
  • Accepted : 2014.07.18
  • Published : 2014.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents camera-based road surface marking detection methods suited to sensor fusion-based positioning system that consists of low-cost GPS (Global Positioning System), INS (Inertial Navigation System), EDM (Extended Digital Map), and vision system. The proposed vision system consists of two parts: lane marking detection and RSM (Road Surface Marking) detection. The lane marking detection provides ROIs (Region of Interest) that are highly likely to contain RSM. The RSM detection generates candidates in the regions and classifies their types. The proposed system focuses on detecting RSM without false detections and performing real time operation. In order to ensure real time operation, the gating varies for lane marking detection and changes detection methods according to the FSM (Finite State Machine) about the driving situation. Also, a single template matching is used to extract features for both lane marking detection and RSM detection, and it is efficiently implemented by horizontal integral image. Further, multiple step verification is performed to minimize false detections.

Keywords

References

  1. I. Skog and P. Handel, "In-car Positioning and Navigation Technologies-A Survey," Intelligent Transportation Systems, IEEE Trans., Vol.10, No.1, pp.4-21, 2009. https://doi.org/10.1109/TITS.2008.2011712
  2. Z. Wu, M. Yao, H. Ma and W. Jia, "Improving Accuracy of the Vehicle Attitude Estimation for Low-cost INS/GPS Integration Aided by the GPS-measured Course Angle," Intelligent Transportation Systems, IEEE Trans., Vol.14, No.2, pp.553,564, 2013.
  3. G. Gao and L. Gerard, "INS-assisted High Sensitivity GPS Receivers for Degraded Signal Navigation," Proceedings of the 19th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2006), pp.2977-2989, 2006.
  4. S. Nedevschi, V. Popescu, R. Danescu, T. Marita and F. Oniga, "Accurate Ego-vehicle Global Localization at Intersections through Alignment of Visual Data with Digital Map," Intelligent Transportation Systems, IEEE Trans., Vol.14, No.2, pp.673-687, 2013. https://doi.org/10.1109/TITS.2012.2228191
  5. M. Schreiber, C. Knoppel and U. Franke, "Lane-Loc: Lane Marking Based Localization Using Highly Accurate Maps," Intelligent Vehicles Symposium (IV), pp.449-454, 2013.
  6. S. Yenikaya, Y. Gokhan and D. Ekrem, "Keeping the Vehicle on the Road: A Survey on On-road Lane Detection Systems," ACM Computing Surveys (CSUR), 2013.
  7. P. Viola and M. Jones, "Robust Real-time Object Detection," International Journal of Computer Vision, Vol.57, Issue 2, pp.137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  8. M. A. Fischler and R. C. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," CACM, Vol.24, Issue 6, pp.381-395, 1981. https://doi.org/10.1145/358669.358692
  9. L. Ziqiong, W. Shengjin and D. Xiaoqing, "ROI Perspective Transform Based Road Marking Detection and Recognition," International Conference on Audio, Language and Image Processing (ICALIP), pp.841-846, 2012.
  10. I. M. Chira, C. Ancuta and G. D. Radu, "Realtime Detection of Road Markings for Driving Assistance Applications," International Conference on Computer Engineering and Systems (ICCES), pp.158-163, 2010.
  11. M. Bertozzi, A. Broggi and A. Fascioli, "Stereo Inverse Perspective Mapping: Theory and Applications," Image and Vision Computing, Vol.16, Issue 8, pp.585-590, 1998. https://doi.org/10.1016/S0262-8856(97)00093-0
  12. The Office of Legislation (Republic of Korea), Enforcement Regulations Appendix 6, The Road Traffic Act, 2014.
  13. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Vol.1, pp.886-893, 2005.
  14. M. Enzweiler and D. M. Gavrila, "Monocular Pedestrian Detection: Survey and Experiments," Pattern Analysis and Machine Intelligence, IEEE Trans., Vol.31, No.12, pp.2179-2195, 2009. https://doi.org/10.1109/TPAMI.2008.260
  15. K. Crammer and Y. Singer, "On the Algorithm Implementation of Multiclass Kernel-based Vector Machines," The Journal of Machine Learning, Vol.2,3, pp.265-292, 2002.

Cited by

  1. An approach for speed limit determination for vehicle tracking in case of GID ambiguity and lack of information in navigation maps vol.13, pp.3, 2017, https://doi.org/10.1108/IJPCC-02-2017-0020
  2. Corner Detection-based Road Sign Detection for Low-cost Sensor Fusion-based Precise Localization System vol.26, pp.1, 2018, https://doi.org/10.7467/KSAE.2018.26.1.020