Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving

TFALDA is a lane detection algorithm which is simple, robust, and efficient, thus suitable for real-time processing in cluttered road environments without a priori knowledge of them. Out of the many possible lane boundary candidates, the best one is 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 that minimize the misclassification rate. The proposed algorithm was successfully applied to a series of road following experiments using the PRV (Postech Research Vehicle) II