Learning to Drive: End-to-End Off-Road Path Prediction
Holder, C.J.; Breckon, T.P.
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. https://doi.org/10.1109/mits.2019.2898970
|Journal Article Type||Article|
|Online Publication Date||Apr 11, 2019|
|Deposit Date||Oct 6, 2021|
|Publicly Available Date||Oct 6, 2021|
|Journal||IEEE Intelligent Transportation Systems Magazine|
|Publisher||Institute of Electrical and Electronics Engineers|
|Peer Reviewed||Peer Reviewed|
Accepted Journal Article
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