Title :
Vision-based neural network road and intersection detection and traversal
Author :
Jochem, Todd M. ; Pomerleau, Dean A. ; Thorpe, Charles E.
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
The use of artificial neural networks in the domain of autonomous driving has produced promising results. ALVINN has shown that a neural system can drive a vehicle reliably and safely on many different types of roads, ranging from paved paths to interstate highways. The next step in the evolution of autonomous driving systems is to intelligently handle road junctions. In this paper the authors present an addition to the basic ALVINN driving system which makes autonomous detection of roads and traversal of simple intersections possible. The addition is based on geometrically modelling the world, accurately imaging interesting parts of the scene using this model, and monitoring ALVINN´s response to the created image
Keywords :
computer vision; computerised navigation; image sensors; mobile robots; neural nets; road vehicles; ALVINN; artificial neural networks; autonomous driving; geometric model; vision-based neural network intersection detection; vision-based neural network road detection; Artificial neural networks; Intelligent systems; Layout; Neural networks; Remotely operated vehicles; Road transportation; Road vehicles; Solid modeling; Vehicle driving; Vehicle safety;
Conference_Titel :
Intelligent Robots and Systems 95. 'Human Robot Interaction and Cooperative Robots', Proceedings. 1995 IEEE/RSJ International Conference on
Conference_Location :
Pittsburgh, PA
Print_ISBN :
0-8186-7108-4
DOI :
10.1109/IROS.1995.525907