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

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

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C.J. Holder


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.


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

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
Publisher URL


Accepted Journal Article (2.2 Mb)

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