Driving Me Around the Bend: Learning to Drive From Visual Gist

Pugeault, N. and Bowden, R. (2011) Driving Me Around the Bend: Learning to Drive From Visual Gist. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, 06-13 Nov 2011, pp. 1022-1029. ISBN 9781467300636 (doi: 10.1109/ICCVW.2011.6130363)

Full text not currently available from Enlighten.

Abstract

This article proposes an approach to learning steering and road following behaviour from a human driver using holistic visual features. We use a random forest (RF) to regress a mapping between these features and the driver's actions, and propose an alternative to classical random forest regression based on the Medoid (RF-Medoid), that reduces the underestimation of extreme control values. We compare prediction performance using different holistic visual descriptors: GIST, Channel-GIST (C-GIST) and Pyramidal-HOG (P-HOG). The proposed methods are evaluated on two different datasets: predicting human behaviour on countryside roads and also for autonomous control of a robot on an indoor track. We show that 1) C-GIST leads to the best predictions on both sequences, and 2) RF-Medoid leads to a better estimation of extreme values, where a classical RF tends to under-steer. We use around 10% of the data for training and show excellent generalization over a dataset of thousands of images. Importantly, we do not engineer the solution but instead use machine learning to automatically identify the relationship between visual features and behaviour, providing an efficient, generic solution to autonomous control.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Pugeault, Dr Nicolas
Authors: Pugeault, N., and Bowden, R.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781467300636
Published Online:16 January 2012

University Staff: Request a correction | Enlighten Editors: Update this record