Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving
SCIE
SCOPUS
- Title
- Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving
- Authors
- Yim, YU; Oh, SY
- Date Issued
- 2003-12
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGI
- 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 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.
- Keywords
- evolutionary algorithm; lane boundary candidate; lane detection; lane vector; road following; VISION SYSTEM
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/18135
- DOI
- 10.1109/TITS.2003.82
- ISSN
- 1524-9050
- Article Type
- Article
- Citation
- IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 4, no. 4, page. 219 - 225, 2003-12
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