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