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

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2 Author(s)
Young Uk Yim ; Dept. of Electr. Eng., Rensselaer Polytech. Inst., Troy, NY, USA ; Se-Young Oh

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.

Published in:

IEEE Transactions on Intelligent Transportation Systems  (Volume:4 ,  Issue: 4 )