Digital Transformation Smart Systems, 24 - 26 Ekim 2018
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.