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

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Datum
2015
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KI - Künstliche Intelligenz: Vol. 29, No. 1
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Springer
Zusammenfassung
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.
Beschreibung
Rudzits, Reinis; Pugeault, Nicolas (2015): Efficient Learning of Pre-attentive Steering in a Driving School Framework. KI - Künstliche Intelligenz: Vol. 29, No. 1. Springer. PISSN: 1610-1987. pp. 51-57
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