Description: Abstract—Three-feature based automatic lane detection al-
gorithm (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 near
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