Robust Path Planning in the Wild for Automatic Look-Ahead Camera Control

Finding potential driving paths on unstructured roads is a challenging problem for autonomous driving and robotics applications. Although the rise of autonomous driving has resulted in massive public datasets, most of these datasets focus on urban environments and feature almost exclusively paved roads. To circumvent the problem of limited public datasets of unpaved roads, we combine seven public vehicle-mounted-camera datasets with a very small private dataset and train a neural network to achieve accurate road segmentation on almost any type of road. This trained network vastly outperforms networks trained on individual datasets when validated on our unpaved road datasets, with only a minor performance reduction on the highly challenging public WildDash dataset, which is mostly urban. Finally, we develop an algorithm to robustly transform these road segmentations to road centerlines, used to automatically control a vehicle-mounted PTZ camera.