Extracting High Definition Map Information from Aerial Images

Guan Wen Chen, Hsueh Yi Lai, Tsì Ui Tsi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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%.

Original languageEnglish
Title of host publication51st International Conference on Parallel Processing, ICPP 2022 - Workshop Proceedings
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450394451
DOIs
StatePublished - 29 Aug 2022
Event51st International Conference on Parallel Processing, ICPP 2022 - Virtual, Online, France
Duration: 29 Aug 20221 Sep 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference51st International Conference on Parallel Processing, ICPP 2022
Country/TerritoryFrance
CityVirtual, Online
Period29/08/221/09/22

Keywords

  • HD map
  • numeralization
  • semantic image segmentation

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