High Definition Maps for Intelligent Transportation Applications

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 5651

Special Issue Editors


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Guest Editor
High Definition Maps Research Center, Department of Geomatics, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan
Interests: inertial navigation system; optimal multi-sensor fusion; seamless mapping and navigation applications; artificial intelligence and collaborative mobile mapping technology
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Guest Editor
Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen 361005, China
Interests: 3D vision; point cloud processing; mobile mapping; artificial intelligence
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Guest Editor
Public Works Department, Faculty of Engineering, Ain Shams University, Cairo11535, Egypt
Interests: HD mapping; vision-based navigation; 5G localization; digital twin; artificial intelligence; geodesy

Special Issue Information

Dear Colleagues,

Autonomous driving vehicles, or self-driving cars, have seen enormous progress in recent years. With advances in computing and sensor technologies, onboard systems can deal with a large amount of data and achieve real-time processes continuously and accurately. These systems also handle several specialized functional schemes such as positioning, mapping, perception, motion planning, and control. These key components are essential for the vehicle to achieve fully autonomous operation. In addition, high-definition (HD) maps provide detailed map information for navigating autonomous vehicles to ensure navigation safety to protect human lives. The map itself serves as an additional “pseudo sensor” of the car and significantly enhances the performance and accuracy of perception and positioning algorithms, which are necessary for the vehicle to drive autonomously.

This Special Issue will cover relevant topics and trends in high-definition (HD) maps for intelligent transportation applications and also introduce the new tendencies of this new paradigm in geospatial science.

We would like to invite you to contribute by submitting articles describing your recent research, experimental work, reviews, and/or case studies related to the field of mobile mapping. Contributions may be from but not limited to the following topics:

  • Automated production and update of static HD maps;
  • Automated production and update of dynamic HD maps;
  • Conventional and crowd sourcing mapping platforms for HD map production;
  • Third-party certified low-cost multisensor mapping systems for ITS applications;
  • Autonomous vehicle navigation with HD point cloud maps;
  • Autonomous vehicle navigation with HD vector maps;
  • Formats, standards, and field practice guidelines;
  • Georeferenced 3D spatial information processing for smart road applications;
  • Georeferenced 3D spatial information manipulation for connected vehicles;
  • Multisensor calibration;
  • Sensor cluster design and platform developments;
  • Multisensor fusion for seamless mapping and navigation applications;
  • Simultaneous localization and mapping with LiDAR or cameras;
  • Point cloud processing;
  • Deep learning for 3D spatial information processing;
  • Feature extraction from georeferenced images and point cloud;
  • Multisensor system assessment and quality validation;
  • GNSS denied and challenging environment validation;
  • Novel application cases.

Prof. Dr. Kai-Wei Chiang
Dr. Chenglu Wen
Dr. Mohamed Elhabiby
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geomatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • HD maps (high-definition maps)
  • MMS (mobile mapping system)
  • SLAM (simultaneous localization and mapping)
  • ITS (intelligent transportation system)
  • Sensor fusion
  • KF (Kalman filter)
  • Autonomous vehicles

Published Papers (1 paper)

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Research

15 pages, 4178 KiB  
Article
Automated Modeling of Road Networks for High-Definition Maps in OpenDRIVE Format Using Mobile Mapping Measurements
by Kai-Wei Chiang, Hao-Yu Pai, Jhih-Cing Zeng, Meng-Lun Tsai and Naser El-Sheimy
Geomatics 2022, 2(2), 221-235; https://doi.org/10.3390/geomatics2020013 - 1 Jun 2022
Cited by 5 | Viewed by 4762
Abstract
With growing attention being devoted to autonomous vehicle (AV) safety, people have recently attached importance to high-definition (HD) maps. HD maps are not limited by environmental factors and can limit AVs driving in certain lanes. HD maps provide accurate auxiliary information on factors [...] Read more.
With growing attention being devoted to autonomous vehicle (AV) safety, people have recently attached importance to high-definition (HD) maps. HD maps are not limited by environmental factors and can limit AVs driving in certain lanes. HD maps provide accurate auxiliary information on factors such as road geometry, traffic sign placement, and traffic topology. Nowadays, most HD maps are made from point clouds data, and this data contains accurate 3D position information. However, the production costs associated with HD maps are substantial. This article proposes an algorithm that reduces a great amount of time and human resource. The algorithm is divided into three phases, lane lines’ extraction from point clouds, modelling lane lines with attributes, and building OpenDRIVE file. The algorithm extracts lane lines resting on intensity value within the range of roads. Next, it models lane lines by cubic spline interpolation with the result of first phase, and build the OpenDRIVE file following the announcement of OpenDRIVE. The final result is compared with the verified HD map from the mapping company to analyze the accuracy. The root mean square (RMSE) obtained were 0.069 and 0.079 m for 2D and 3D maps, respectively. Full article
(This article belongs to the Special Issue High Definition Maps for Intelligent Transportation Applications)
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