DOI QR코드

DOI QR Code

A Study on Lane Sensing System Using Stereo Vision Sensors

스테레오 비전센서를 이용한 차선감지 시스템 연구

  • 하건수 (한양대학교 기계공학부) ;
  • 박재식 (다이모스㈜ 기술연구소) ;
  • 이광운 (㈜우영 생산기술센터) ;
  • 박재학 (한양대학교 대학원 정밀기계공학과)
  • Published : 2004.03.01

Abstract

Lane Sensing techniques based on vision sensors are regarded promising because they require little infrastructure on the highway except clear lane markers. However, they require more intelligent processing algorithms in vehicles to generate the previewed roadway from the vision images. In this paper, a lane sensing algorithm using vision sensors is developed to improve the sensing robustness. The parallel stereo-camera is utilized to regenerate the 3-dimensional road geometry. The lane geometry models are derived such that their parameters represent the road curvature, lateral offset and heading angle, respectively. The parameters of the lane geometry models are estimated by the Kalman filter and utilized to reconstruct the lane geometry in the global coordinate. The inverse perspective mapping from the image plane to the global coordinate considers roll and pitch motions of a vehicle so that the mapping error is minimized during acceleration, braking or steering. The proposed sensing system has been built and implemented on a 1/10-scale model car.

Keywords

Lane Sensing;Robustness;Kalman Filter;Stereo Vision Sensor;Inverse Perspective Mapping

References

  1. Grewal, M. S. and Andrews, A .P., 1993, Kalman Filtering Theory and Practice, Prentice Hall, pp. 112-119.
  2. Jain, R., Kasturi, R. Schunck, B. G. 1995, Machine Vision, McGraw-Hill.
  3. Jeong, S. G., Kim, I. S., Kim, S. H., Lee, D. H., Yun, K. S., and Lee, M. H., 2001, 'Real-Time Lane Detection Based on Inverse Perspective Transform and Search Range Prediction,' Journal of the KSPE, Vol. 18, No. 3, pp. 68-74.
  4. Jhang, K. Y., Song, J. Y. and Park, J. W., 1997, 'Detection of Lane and Distance to the Forward Vehicle by using Machine Vision,' The KSAE Academic lecture of I.T.S., pp. 10-17.
  5. Bertozzi, M., and Broggi, A., 1998, 'GOLD:A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection,' IEEE Transactions on Image Processing, Vol. 7, No. 1, pp. 62-81. https://doi.org/10.1109/83.650851
  6. Lin, C. F., Ulsoy, A. G. and LeBlanc, D. J., 1995, 'Lane Geometry Reconstruction:Least Square Curve Fit Versus Kalman Filter,' ASME Advanced Automotive Technologies, DSC-Vol. 56/DE-Vol.86, pp. 63-70.
  7. Park,Y. J., Huh, K. S., 2002, 'Development of a lane sensing algorithm using vision sensors,' Transaction of the KSME, A. Vol. 26, No. 8, pp. 1666-1671.
  8. Dickmanns, E. D. and Mysliwetz, B. D., 1992, 'Recursive 3-D Road and Relative Ego-State Recognition,' IEEE Trans. on PAMI, Vol. 14, No. 2, pp. 199-213. https://doi.org/10.1109/34.121789
  9. Takahashi, A., Ninomiya, Y., Ohta, M. and Tange, K., 1999, 'A Robust Lane Detection using Real-time Voting Processor,' Proc. of the IEEE Intelligent Transportation Systems, pp. 577-580. https://doi.org/10.1109/ITSC.1999.821123
  10. Takahashi, A., and Ninomiya, Y., 1996, 'Model-Based lane recognition,' Proc. of the IEEE Intelligent Vehicles 96, pp. 162-166.