Name
Abstract | ||
|---|---|---|
With the emergence of intelligent Advanced Driving Assistance Systems (i-ADAS), the need for effective detection of vehicular surroundings is considered a necessity. The effectiveness of such systems directly depends on their performance in various environments such as rural and urban roads, and highways. Most of the current lane detection techniques are not suitable for urban roads with complex lane shapes and frequent occlusions. We propose a map-based lane detection approach which can robustly detect the lanes in urban and rural environments, and highways. We also present an algorithm for detecting obstacle-free areas in detected lanes based on the stereo depth maps of driving scenes. Experiments show that our approach reliably detects lanes and obstacle free areas within them, even in case of partially occluded or worn-off lane markers. |
Year | Venue | Keywords |
|---|---|---|
2014 | 2014 International Conference on Computer Vision Theory and Applications (VISAPP) | Lane Detection,Stereo Vision,Particle Filters,Lane Maps |
Field | DocType | Volume |
Computer vision,Obstacle,Computer science,Robustness (computer science),Feature extraction,Lane detection,Artificial intelligence,Global Positioning System | Conference | 3 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taha Kowsari | 1 | 12 | 1.71 |
| Steven S. Beauchemin | 2 | 81 | 12.00 |
| Michael A. Bauer | 3 | 331 | 78.68 |