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Carnegie Mellon Robotics Institute

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Abstract

Artificial neural networks are capable of performing the reactive aspects of autonomous driving, such as staying on the road and avoiding obstacles. This paper describes an efficient technique for training individual networks to perform these reactive driving tasks. But driving requires more than a collection of isolated capabilities. To achieve true autonomy, a system must determine which capabilities should be employed in the current situation to achieve its objectives. Such goal-directed behavior is difficult to implement in an entirely connectionist system. This paper describes a rule-based technique for combining multiple artificial neural networks with map-based symbolic reasoning to achieve high-level behaviors. The resulting system is not only able to stay on the road, it is able to follow a route to a predetermined destination, turning appropriately at intersections and stopping when it has reached its goal.

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

Dean Pomerleau, Jay Gowdy, and Chuck Thorpe, "Combining artificial neural networks and symbolic processing for autonomous robot guidance," Engineering Applications of Artificial Intelligence, Vol. 4, No. 4, 1991, pp. 279 - 285.

BibTeX Reference

@article{Pomerleau_1991_1627,
   author = "Dean Pomerleau and Jay Gowdy and Chuck Thorpe",
   title = "Combining artificial neural networks and symbolic processing for autonomous robot guidance",
   journal = "Engineering Applications of Artificial Intelligence",
   pages = "279 - 285",
   year = "1991",
   volume = "4",
   number = "4",
}

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