A lane marker estimation method for improving lane detection

In this paper we present a new lane markers detection and estimation algorithm aiming to improve lane detection methods. We first estimate the area of lane marking using the profile of the lane estimation in a confidence map. After that a fitting method is applied to improve the lane marker detection accuracy. To track our lane markers over time and make the association between two iteration, we use transferable belief model. The final lane markers are then used to filter out noise and to improve the lane estimation. The presented method is validated on both real and synthetic datasets. Without the integration of lane markers, we obtain a 98% ego detection rate on synthetic data for the lane detection algorithm presented in [8]. With the use of our lane marker detection algorithm, we are able to improve ours results by 1.5% thus reaching a detection rate of 99.5%. For the real validation, we use a highway sequence of about 4000 pictures and get 95.02% of good detection with 0.45% of false alarm for the first thousand images and 99% for ego-lane detection with no false alarm for all the sequence.