@article{ref1,
title="Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving",
journal="IEEE transactions on intelligent transportation systems",
year="2003",
author="Yim, Young Uk and Oh, Se-Young",
volume="4",
number="4",
pages="219-225",
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.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2003.821339",
url="http://dx.doi.org/10.1109/TITS.2003.821339"
}