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

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

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
• Public URL: https://durham-repository.worktribe.com/output/1228930
• Publisher URL: https://ieeexplore.ieee.org/document/8686262