Evolution of an artificial neural network based autonomous land vehicle controller

Shumeet Baluja
IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 26, No. 3, June, 1996, pp. 450 - 463.


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Abstract
This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): NavLab
Associated Project(s): Autonomous Land Vehicle In a Neural Network

Text Reference
Shumeet Baluja, "Evolution of an artificial neural network based autonomous land vehicle controller," IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 26, No. 3, June, 1996, pp. 450 - 463.

BibTeX Reference
@article{Baluja_1996_3832,
   author = "Shumeet Baluja",
   title = "Evolution of an artificial neural network based autonomous land vehicle controller",
   journal = "IEEE Transactions on Systems, Man and Cybernetics, Part B",
   pages = "450 - 463",
   month = "June",
   year = "1996",
   volume = "26",
   number = "3",
}