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Carnegie Mellon Robotics Institute

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Abstract

Many real world problems quire a degree of flexibility that is diflicult to achieve using hand programmed algorithms. One such domain is vision-based autonomous driving. In this task, the dual challenges of a constantly changing environment coupled with a real time processing constrain make the flexibility and efficiency of a machine learning system essential. This chapter describes just such a learning system, called ALVINN (Autonomous Land Vehicle In a Neural Network). It presents the neural network architecture and training techniques that allow ALVINN to drive in a variety of circumstances including single lane paved and unpaved roads, multilane lined and unlined roads, and obstacle-ridden on- and off- road environments, at speeds of up to 55 miles per hour.

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, "Knowledge-based Training of Artificial Neural Networks for Autonomous Robot Driving," Robot Learning, J. Connell and S. Mahadevan, ed., 1993

BibTeX Reference

@incollection{Pomerleau_1993_1340,
   author = "Dean Pomerleau",
   editor = "J. Connell and S. Mahadevan",
   title = "Knowledge-based Training of Artificial Neural Networks for Autonomous Robot Driving",
   booktitle = "Robot Learning",
   year = "1993",
}

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