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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: ICCV 2011: 1st IEEE Workshop on Challenges and Opportunities in Robotic Perception, 2011-11-06 - 2011-11-13, Barcelona, Spain.

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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 or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Pugeault, N
Bowden, R
Date : 2011
DOI : 10.1109/ICCVW.2011.6130363
Contributors :
Additional Information : Copyright 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User : Symplectic Elements
Date Deposited : 22 May 2012 11:49
Last Modified : 31 Oct 2017 14:33

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