Extracting High Definition Map Information from Aerial Images

Abstract

Compared with traditional digital maps, high definition maps (HD maps) collect information in lane-level instead of road-level, and provide more diverse and detailed road network information, including lane markings, speed limits, rules, and intersection junction. HD maps can be used for driving navigation and autonomous driving cars with high-precision information to improve driving safety. However, it takes a lot of time to construct the HD map, so that the HD map cannot be widely used in applications at present. This paper proposes a method to identify road information through semantic image segmentation algorithm from aerial traffic images, and then convert it into the open source HD map standard format, which is OpenDRIVE. Through experiments, 13 categories of lane markings can be identified with mIoU of 84.3% and mPA of 89.6%.