TY  - JOUR
AU  - Antonio Lopez
AU  - Joan Serrat
AU  - Cristina Cañero
AU  - Felipe Lumbreras
AU  - T. Graf
PY  - 2010//
TI  - Robust lane markings detection and road geometry computation
T2  - IJAT
JO  - International Journal of Automotive Technology
SP  - 395–407
VL  - 11
IS  - 3
PB  - The Korean Society of Automotive Engineers
KW  - lane markings
N2  - Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known.
SN  - 1229-9138
L1  - http://158.109.8.37/files/LSC2010.pdf
UR  - http://dx.doi.org/10.1007/s12239-010-0049-6
N1  - ADAS
ID  - Antonio Lopez2010
ER  -