Sample-efficient Online Learning in a Physical Environment
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Autonomous driving has been seen as the next breakthrough in transportation.
Autonomous vehicles employ a variety of sensors to understand their surroundings, for
example multiple cameras, ultrasound sensors, and LiDARs. In this work, a much smaller
scale radio-controlled cars, that only carry a central camera, are used. Their effectiveness
as a test-bed for validating autonomous driving methods is evaluated. Multiple neural
network architectures were proposed, among which a convolutional neural network
was selected as the best candidate. The network was then trained using both supervised
learning and online learning, the results of which were then compared. Experiments show
that online learning in a physical environment, while costly, is a significant improvement
over pure supervised learning. Additionally the radio-controlled cars proved to be a
good comparative test-bed for evaluating model performance in an interactive physical
environment.
Description
Keywords
autonomous driving, deep learning, online learning