End-to-End Learning-based Driving System with Branches by Emphasizing Target Direction

Abstract

End-to-end driving refers to deep learning methods for generating control signals directly from external sensors. Previous methods use a direction vector towards the target to select and turn at intersections. However, the vector has a smaller dimension than the image, and thus it is ignored during training. In this study, we propose a learning method to emphasize that vector by using L2 regularization, which enables a robot to follow trajectories with branches. We validate the system's performance by conducting experiments using several driving scenarios. Our approach allowed an autonomous robot to successfully follow trajectories, including unknown outdoor trajectories.