Learning to Drive: End-to-End Off-Road Path Prediction

Holder, C.J.; Breckon, T.P.

doi:10.1109/mits.2019.2898970

Learning to Drive: End-to-End Off-Road Path Prediction

Holder, C.J.; Breckon, T.P.

Authors

C.J. Holder

Professor Toby Breckon toby.breckon@durham.ac.uk
Professor

Abstract

Autonomous driving is a field currently gaining a lot of attention, and recently ?end to end? approaches, whereby a machine learning algorithm learns to drive by emulating a human driver, have demonstrated significant potential. However, recent work has focused on the on-road environment, rather than the more challenging off-road environment. In this work we propose a new approach to this problem, whereby instead of learning to predict immediate driver control inputs, we train a deep convolutional neural network (CNN) to predict the future path that a vehicle will take through an off-road environment visually, addressing several limitations inherent in existing methods. We combine a novel approach to automatic training data creation, making use of stereoscopic visual odometry, with a state-of-the-art CNN architecture to map a predicted route directly onto image pixels, and demonstrate the effectiveness of our approach using our own off-road data set.

Citation

Holder, C., & Breckon, T. (2021). Learning to Drive: End-to-End Off-Road Path Prediction. IEEE Intelligent Transportation Systems Magazine, 13(2), 217-221. https://doi.org/10.1109/mits.2019.2898970

Journal Article Type	Article
Online Publication Date	Apr 11, 2019
Publication Date	2021
Deposit Date	Oct 6, 2021
Publicly Available Date	Oct 6, 2021
Journal	IEEE Intelligent Transportation Systems Magazine
Print ISSN	1939-1390
Electronic ISSN	1941-1197
Publisher	Institute of Electrical and Electronics Engineers
Peer Reviewed	Peer Reviewed
Volume	13
Issue	2
Pages	217-221
DOI	https://doi.org/10.1109/mits.2019.2898970
Publisher URL	https://ieeexplore.ieee.org/document/8686262

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