Road detection using convolutional neural networks

The work presented in this paper aims to address the problem of autonomous driving (especially along ill-defined roads) by using convolutional neural networks to predict the position and width of roads from camera input images. The networks are trained with supervised learning (i.e., back-propagation) using a dataset of annotated road images. We train two different network architectures for images corresponding to six colour models. They are tested “off-line” on a road detection task using image sequences not used in training. To benchmark our approach, we compare the performance of our networks with that of a different image processing method that relies on differences in colour distribution between the road and non-road areas of the camera input. Finally, we use a trained convolutional network to successfully navigate a Pioneer 3-AT robot on 5 distinct test paths. Results show that the network can safely guide the robot in this navigation task and that it is robust enough to deal with circumstances much...