Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction

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 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 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.