Instance-level graph modeling for end-to-end vectorized HD map learning

Abstract

The construction of lightweight High-definition (HD) maps containing geometric and semantic information is of foremost importance for the large-scale deployment of autonomous driving. To automatically generate such type of map from a set of images captured by a vehicle, most works formulate this mapping as a segmentation problem, which implies heavy post-processing to obtain the final vectorized representation. Alternative techniques have the ability to generate an HD map in an end-to-end manner but rely on computationally expensive auto-regressive models. To bring camera-based to an applicable level, we propose a fast end-to-end network generating a vectorized HD map via instance-level graph modeling of the map elements. Comprehensive experiments on nuScenes dataset show that our proposed network outperforms state-of-the-art methods by 13.7 mAP and achieves comparable accuracy with 5× faster inference speed.