Pomerleau, Dean

Life in the Fast Lane: The Evolution of an Adaptive Vehicle Control System

AI Magazine

Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.

Neural Network Perception for Mobile Robot Guidance


See also: A Reply to Towell's Book Review of Neural Network Perception for Mobile Robot GuidanceKluwer

ALVINN: An Autonomous Land Vehicle in a Neural Network


Advances in Neural Information Processing Systems, Vol 1, pp. 305-313, San Francisco: Morgan Kaufmann Publishers