Rudzits, R. and Pugeault, N. (2015) Efficient learning of pre-attentive steering in a driving school framework. KI - Kunstliche Intelligenz, 29(1), pp. 51-57. (doi: 10.1007/s13218-014-0340-1)
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Abstract
Autonomous driving is an extremely challenging problem and existing driverless cars use non-visual sensing to palliate the limitations of machine vision approaches. This paper presents a driving school framework for learning incrementally a fast and robust steering behaviour from visual gist only. The framework is based on an autonomous steering program interfacing in real time with a racing simulator: hence the teacher is a racing program having perfect insight into its position on the road, whereas the student learns to steer from visual gist only. Experiments show that (i) such a framework allows the visual driver to drive around the track successfully after a few iterations, demonstrating that visual gist is sufficient input to drive the car successfully; and (ii) the number of training rounds required to drive around a track reduces when the student has experienced other tracks, showing that the learnt model generalises well to unseen tracks.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Pugeault, Dr Nicolas |
Authors: | Rudzits, R., and Pugeault, N. |
College/School: | College of Science and Engineering > School of Computing Science |
Journal Name: | KI - Kunstliche Intelligenz |
Publisher: | Springer |
ISSN: | 0933-1875 |
ISSN (Online): | 1610-1987 |
Published Online: | 20 December 2014 |
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