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Extracting High Definition Map Information from Aerial Images

Published: 13 January 2023 Publication History

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%.

References

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Matthias Althoff, Stefan Urban, and Markus Koschi. 2018. Automatic conversion of road networks from opendrive to lanelets., 157–162 pages.
[2]
ASAM. 2021. ASAM OpenDRIVE. https://www.asam.net/standards/detail/opendrive/
[3]
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille. 2014. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. https://doi.org/10.48550/ARXIV.1412.7062
[4]
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille. 2018. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 4(2018), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184
[5]
L. Chen, G. Papandreou, F. Schroff, and H. Adam. 2017. Rethinking Atrous Convolution for Semantic Image Segmentation. https://doi.org/10.48550/ARXIV.1706.05587
[6]
L. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation., 801–818 pages.
[7]
Mark Everingham, SM Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. 2015. The pascal visual object classes challenge: A retrospective. International journal of computer vision 111, 1 (2015), 98–136.
[8]
Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation., 3431–3440 pages.
[9]
F. Poggenhans, J. Pauls, J. Janosovits, S. Orf, M. Naumann, F. Kuhnt, and M. Mayr. 2018. Lanelet2: A high-definition map framework for the future of automated driving., 1672-1679 pages.

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ICPP Workshops '22: Workshop Proceedings of the 51st International Conference on Parallel Processing
August 2022
233 pages
ISBN:9781450394451
DOI:10.1145/3547276
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 January 2023

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Author Tags

  1. HD map
  2. numeralization
  3. semantic image segmentation

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  • Research-article
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ICPP '22
ICPP '22: 51st International Conference on Parallel Processing
August 29 - September 1, 2022
Bordeaux, France

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Overall Acceptance Rate 91 of 313 submissions, 29%

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