NVIDIA Corporation
THREE-DIMENSIONAL INTERSECTION STRUCTURE PREDICTION FOR AUTONOMOUS DRIVING APPLICATIONS

Abstract:

In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN--after conversion to 2D space--to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints--e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.