A Robust Lane Detection Approach Based on MAP Estimate and Particle Swarm Optimization

  • Yong Zhou
  • Xiaofeng Hu
  • Qingtai Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3802)


In this paper, a robust lane detection approach, that is primary and essential for driver assistance systems, is proposed to handle the situations where the lane boundaries in an image have relatively weak local contrast, or where there are strong distracting edges. The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image, a maximum a posteriori (MAP) formulation of the lane detection problem, and a particle swarm optimization algorithm to maximize the posterior density. The model parameters completely determine the position of the vehicle inside the lane, its heading direction, and the local structure of the lane. Experimental results reveal that the proposed method is robust against noise and shadows in the captured road images.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Driver Assistance System Lane Detection Road Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yong Zhou
    • 1
  • Xiaofeng Hu
    • 1
  • Qingtai Ye
    • 1
  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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