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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2016  >  16(3): 149-158
XU Mei-hua, ZHANG Kai-xin, JIANG Zhou-long. Algorithm design and implementation for a real-time lane departure pre-warning system[J]. Journal of Traffic and Transportation Engineering, 2016, 16(3): 149-158. doi: 10.19818/j.cnki.1671-1637.2016.03.018
Citation: XU Mei-hua, ZHANG Kai-xin, JIANG Zhou-long. Algorithm design and implementation for a real-time lane departure pre-warning system[J]. Journal of Traffic and Transportation Engineering, 2016, 16(3): 149-158. doi: 10.19818/j.cnki.1671-1637.2016.03.018
shu

Algorithm design and implementation for a real-time lane departure pre-warning system

doi: 10.19818/j.cnki.1671-1637.2016.03.018

  • School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

More Information
  • Author Bio:

    XU Mei-hua(1957-), female, professor, PhD, +86-21-56331632, mhxu@shu.edu.cn

  • Received Date: 2016-02-01
  • Publish Date: 2016-06-25
    Abstract
  • Aiming at the real-time lane departure pre-warning question, a lateral erosion operator was used to corrode the image after edge detection, and the irrelevant edge information in image was reduced and eliminated to decrease the follow-up processing data quantity significantly.A threshold selection method of edge gradient image block based on Otsu algorithm was proposed to effectively partition road edge image under asymmetrical illumination.A lane line voting selection and scoring algorithm combining the geometrical characteristics of lane line distribution on road, Hough voting results, the correlation of road images and the width characteristics of lane line was proposed to recognize the lane line in multilane scenes.Kalman filter method was applied to track the lane line.The algorithm software of lane departure pre-warning system was used to carry out the test verification.Test result shows that total frame number of road images is 24 661, the frame number of right detection is 23 483, and the frame number of wrong detection is 1 178, and the average detection accuracy is 95.22%.The test verified the correctness and effectiveness of the algorithm, which can satisfy the real-time feature and robustness of lane departure prewarning system.

     

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