Holder, C.J. and Breckon, T.P. (2021) 'Learning to Drive: End-to-End Off-Road Path Prediction.', IEEE Intelligent Transportation Systems Magazine, 13 (2). pp. 217-221.
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.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://ieeexplore.ieee.org/document/8686262|
|Publisher statement:||© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||No date available|
|Date deposited:||06 October 2021|
|Date of first online publication:||11 April 2019|
|Date first made open access:||06 October 2021|
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