Knowledge-based Training of Artificial Neural Networks for Autonomous Robot Driving

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.