Paper Info
Title
Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction.
Abstract
Autonomous driving is a fieldcurrently gaining a lot of attention, and recently ‘end to end’ approaches, whereby a machine learning algorithm learns to drive by emulating human drivers, have demonstrated significant potential. However, recent work has focused on the on-road environment, rather than the much 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 offroad 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 ap a predicted route directly onto image pixels, and demonstrate the effectiveness of our approach using our own off-road data set.
Year
Venue
Field
2018
Intelligent Vehicles Symposium
Computer vision,Visual odometry,Stereoscopy,Convolutional neural network,End-to-end principle,Computer science,Image segmentation,Artificial intelligence,Pixel,Deep learning,Decoding methods
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Christopher J. Holder110.69
T. P. Breckon227839.16