ALVINN: An Autonomous Land Vehicle in a Neural Network

ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINN takes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perform the task differs dramatically when the network is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand.

[1]  E. D. Dickmanns,et al.  A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision , 1987, Other Conferences.

[2]  Takeo Kanade,et al.  Vision and Navigation for the Carnegie-Mellon Navlab , 1987 .

[3]  Darwin T. Kuan,et al.  Autonomous Robotic Vehicle Road Following , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  S. N. Srihari,et al.  Neural network models and their application to handwritten digit recognition , 1988, IEEE 1988 International Conference on Neural Networks.

[5]  Alex Waibel,et al.  Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[6]  D. S. Touretzky,et al.  Neural network simulation at Warp speed: how we got 17 million connections per second , 1988, IEEE 1988 International Conference on Neural Networks.

[7]  D. Henderson,et al.  An application of neural net chips: handwritten digit recognition , 1988, IEEE 1988 International Conference on Neural Networks.

[8]  Michael I. Jordan Supervised learning and systems with excess degrees of freedom , 1988 .

[9]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..