Efficient Learning of Pre-attentive Steering in a Driving School Framework
| dc.contributor.author | Rudzits, Reinis | |
| dc.contributor.author | Pugeault, Nicolas | |
| dc.date.accessioned | 2018-01-08T09:17:35Z | |
| dc.date.available | 2018-01-08T09:17:35Z | |
| dc.date.issued | 2015 | |
| dc.description.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. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11446 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 29, No. 1 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.title | Efficient Learning of Pre-attentive Steering in a Driving School Framework | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 57 | |
| gi.citation.startPage | 51 |