Self-supervised drivable area detection for autonomous driving vehicles

Abstract

For autonomous vehicles, knowing where they can drive and where they cannot is of utmost importance. The drivable area detection module serves precisely this purpose, ensuring that the vehicle operates within areas where it is permissible to drive while avoiding non-drivable regions. Recent efforts in deep neural networks (DNNs) have significantly improved drivable area detection performance for autonomous driving. Nevertheless, the majority of DNN-based approaches require a substantial volume of data to train their models. Acquiring extensive datasets with manually annotated ground truth can be an expensive, laborious, and time-consuming process, often necessitating the involvement of domain experts. As a result, the practical implementation of DNN-based methods in realworld applicatio...[ Read more ]