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

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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.

Associated center: VASC
Associated lab/group: NavLab
Associated project: Autonomous Land Vehicle In a Neural Network

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

@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"
}