DocumentCode :
1210923
Title :
A discriminating feature tracker for vision-based autonomous driving
Author :
Schneiderman, Henry ; Nashman, Marilyn
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume :
10
Issue :
6
fYear :
1994
fDate :
12/1/1994 12:00:00 AM
Firstpage :
769
Lastpage :
775
Abstract :
A new vision-based technique for autonomous driving is described. This approach explicitly addresses and compensates for two forms of uncertainty: uncertainty about changes in road direction and uncertainty in the measurements of the road derived in each image. Autonomous driving has been demonstrated on both local roads and highways at speeds up to 100 km/h. The algorithm has performed well in the presence of non-ideal road conditions including gaps in the lane markers, sharp curves, shadows, cracks in the pavement, and wet roads. It has also performed well in rain, dark, and nighttime driving with headlights
Keywords :
feature extraction; mobile robots; path planning; robot vision; 100 km/h; cracks; dark; discriminating feature tracker; highways; local roads; nighttime driving; nonideal road conditions; rain; road direction; shadows; sharp curves; uncertainty; vision-based autonomous driving; wet roads; Cameras; Data mining; Least squares approximation; Measurement uncertainty; Rain; Recursive estimation; Road transportation; Robot vision systems; Robustness; Vehicles;
fLanguage :
English
Journal_Title :
Robotics and Automation, IEEE Transactions on
Publisher :
ieee
ISSN :
1042-296X
Type :
jour
DOI :
10.1109/70.338531
Filename :
338531
Link To Document :
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