Citation

Yim YU, Oh SY. IEEE Trans. Intel. Transp. Syst. 2003; 4(4): 219-225.

Copyright

(Copyright © 2003, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2003.821339

PMID

unavailable

Abstract

Three-feature based automatic lane detection algorithm (TFALDA) is a new lane detection algorithm which is simple, robust, and efficient, thus suitable for real-time processing in cluttered road environments without a priori knowledge on them. Three features of a lane boundary - starting position, direction (or orientation), and its gray-level intensity features comprising a lane vector are obtained via simple image processing. Out of the many possible lane boundary candidates, the best one is then chosen as the one at a minimum distance from the previous lane vector according to a weighted distance metric in which each feature is assigned a different weight. An evolutionary algorithm then finds the optimal weights for combination of the three features that minimize the rate of detection error. The proposed algorithm was successfully applied to a series of actual road following experiments using the PRV (POSTECH research vehicle) II both on campus roads and nearby highways.