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

Rudzits, R and Pugeault, N (2015) Efficient Learning of Pre-attentive Steering in a Driving School Framework KI - Künstliche Intelligenz, 29 (1). pp. 51-57.

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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: Article
Authors :
AuthorsEmailORCID
Rudzits, RUNSPECIFIEDUNSPECIFIED
Pugeault, NUNSPECIFIEDUNSPECIFIED
Date : 1 February 2015
Identification Number : https://doi.org/10.1007/s13218-014-0340-1
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 10:53
Last Modified : 28 Mar 2017 10:53
URI: http://epubs.surrey.ac.uk/id/eprint/807248

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