Title
Vision and GPS-based autonomous vehicle navigation using templates and artificial neural networks
Abstract
This paper presents a vehicle control system capable of learning to navigate autonomously. Our approach is based on image processing, road and navigable area identification, template matching classification for navigation control, and trajectory selection based on GPS way-points. The vehicle follows a trajectory defined by GPS points avoiding obstacles using a single monocular camera. The images obtained from the camera are classified into navigable and non-navigable regions of the environment using neural networks that control the steering and velocity of the vehicle. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.
Year
DOI
Venue
2012
10.1145/2245276.2245332
SAC
Keywords
Field
DocType
trajectory selection,experimental test,neural network,single monocular camera,image processing,gps-based autonomous vehicle navigation,gps way-points,vehicle control system,different environmental condition,navigation control,navigable area identification,artificial neural network,gps,compass,template matching
Template matching,Computer vision,Compass,Computer science,Image processing,Monocular camera,Global Positioning System,Artificial intelligence,Template,Artificial neural network,Trajectory
Conference
Citations 
PageRank 
References 
3
0.55
9
Authors
6
Name
Order
Citations
PageRank
Jefferson R. Souza1467.19
Gustavo Pessin216423.10
Gustavo B. Eboli330.55
Caio C. T. Mendes430.55
Fernando S. Osório5485.92
Denis F. Wolf631130.16