Vision and navigation for the Carnegie-Mellon navlab
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Autonomous Robotic Vehicle Road Following
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of road directions using feedback neural nets
Signal Processing - Intelligent systems for signal and image understanding
Efficient training of artificial neural networks for autonomous navigation
Neural Computation
Evolution of an artificial neural network based autonomous landvehicle controller
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved radial basis function network for visual autonomous road following
IEEE Transactions on Neural Networks
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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The paper presents an original approach for visual identification of road direction of an autonomous vehicle using an improved Radial Basis Function (RBF) neural network. We present the results of designing, software implementation, training, and testing of our RBF model for automatic road direction detection as a function of the input image. The path to be identified was quantified in 5 output directions. For training and testing the neural model, we used two lots of real road scenes: 50 images for training and other 50 images for test. The score of correct road recognition was of 100% both for estimation lot and also for the test lot. We have also designed a driving simulator to evaluate the performances of the neural road follower.