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 t h e 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 &VI" to drive in a variety of circumstanm including singlelane 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.