High Definition Maps for Intelligent Transportation Applications

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

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