RBF Network Methods for Face Detection and Attentional Frames
Neural Processing Letters
Visual Learning with Navigation as an Example
IEEE Intelligent Systems
A Neural Approach for Detection of Road Direction in Autonomous Navigation
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
Online-learning and Attention-based Approach to Obstacle Avoidance Using a Range Finder
Journal of Intelligent and Robotic Systems
Neuroadaptive output tracking of fully autonomous road vehicles with an observer
IEEE Transactions on Intelligent Transportation Systems
Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
Grey-box radial basis function modelling
Neurocomputing
A new neural network approach for visual autonomous road following
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume I
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We have developed a radial basis function network (RBFN) for visual autonomous road following. Preliminary testing of the RBFN was done using a driving simulator, and the RBFN was then installed on an actual vehicle at Carnegie Mellon University for testing in an outdoor road-following application. In our first attempts, the RBFN had some success, but it experienced some significant problems such as jittery control and driving failure. Several improvements have been made to the original RBFN architecture to overcome these problems in simulation and more importantly in actual road following, and the improvements are described in this paper