Name
Abstract | ||
|---|---|---|
This paper presents a real-time long-range lane detection and tracking approach to meet the requirements of the high-speed intelligent vehicles running on highway roads. Based on a linear-parabolic two-lane highway road model and a novel strong lane marking feature named Lane Marking Segmentation, the maximal lane detection distance of this approach is up to 120 meters. Then the lane lines are selected and tracked by estimating the ego vehicle lateral offset with a Kalman filter. Experiment results with test dataset extracted from real traffic scenes on highway roads show that the approaches proposed in this paper can achieve a high detection rate with a low time cost. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIG.2011.116 | ICIG |
Keywords | Field | DocType |
linear-parabolic two-lane highway road,real-time long-range lane detection,maximal lane detection distance,kalman filter,lane marking segmentation,kalman filters,ego vehicle lateral,traffic engineering computing,lane detection,lane tracking approach,image segmentation,roads,novel strong lane,real-time long range lane detection,lane line,object tracking,edge detection,high-speed intelligent vehicles,intelligent vehicle,high detection rate,lane tracking,highway road,ego vehicle lateral offset,real time systems,real time,radar tracking,least squares approximation,feature extraction | Computer vision,Radar tracker,Computer science,Segmentation,Edge detection,Feature extraction,Kalman filter,Image segmentation,Video tracking,Artificial intelligence,Offset (computer science) | Conference |
ISBN | Citations | PageRank |
978-0-7695-4541-7 | 3 | 0.43 |
References | Authors | |
8 | 5 | |