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 nearby highways.-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