Robust Lane Recognition Based on Arc Splines

Yeniaydın, Yasin
Schmidt, Klaus Verner
This paper develops a general lane detection method and proposes new techniques for feature extraction and lane modeling. The proposed method first determines a static region of interest. Then, feature extraction is used to establish candidate lane pixels in a binary image. Next, the binary image is transformed to a bird's eye view (BEV) via inverse perspective mapping. After that, a reliable region for the detection of the left or right lane markings is chosen based on the distribution of the candidate lane pixels on the BEV. Finally, a lane model is fitted to the extracted lane pixels. The paper further proposes a new Neighborhood AND operator for feature extraction and uses arc-splines as a lane model. In order to evaluate the quality of the proposed method, the paper performs an extensive comparison using different feature extraction methods and a second-order lane model. The experimental results show that the Neighborhood AND operator for feature extraction and arc-spline lane modeling are superior to the other techniques.
Citation Formats
Y. Yeniaydın and K. V. Schmidt, “Robust Lane Recognition Based on Arc Splines,” Ankara, Türkiye, 2018, vol. 1, p. 1, Accessed: 00, 2021. [Online]. Available: