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Efficient Learning of Pre-attentive Steering in a Driving School Framework

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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.

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Notes

  1. https://plus.google.com/+GoogleSelfDrivingCars/posts

  2. http://mrg.robots.ox.ac.uk/robotcar/

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Authors and Affiliations

  1. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK

    Reinis Rudzits & Nicolas Pugeault

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  1. Reinis Rudzits
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  2. Nicolas Pugeault
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Correspondence to Nicolas Pugeault.

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Rudzits, R., Pugeault, N. Efficient Learning of Pre-attentive Steering in a Driving School Framework. Künstl Intell 29, 51–57 (2015). https://doi.org/10.1007/s13218-014-0340-1

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