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Title	Hidam: A Unified Data Model For High-Definition (Hd) Map Data
ID_Doc	28982
Authors	Kang Y.; Magdy A.
Year	2020
Published	Proceedings - 2020 IEEE 36th International Conference on Data Engineering Workshops, ICDEW 2020
DOI	http://dx.doi.org/10.1109/ICDEW49219.2020.00-11
Abstract	Smart cities are emerging and under development worldwide. As one of the core components for achieving the goal of smart transportation, autonomous vehicles are undoubtedly on the blueprint for smart cities. The development of autonomous driving cars leads to the demand for highly accurate and detailed machine-readable maps. To meet this demand, the high-definition (HD) maps are being developed. The HD maps are extended three-dimensional maps that tend to contain enough detailed information in the driving environment to be consumed by machines. Both commercial companies and research communities got a recent interest in building and are exploring research problems on this kind of mapping data. Several companies have already defined their commercial HD map formats, but there are arguments that the existing HD map formats are not research-friendly enough. In this paper, we propose to define a research-friendly HD map data model by extending the popular node-edge model that is widely used by researchers. Our model considers both on-road and off-road data to be extensible for future use cases and information that will be available on various three-dimensional objects in the outdoor space. This is partially motivated by several use cases that will benefit from HD mapping data other than the self-driving technology. We give a brief discussion for such potential use cases and how our model will fit their requirements. We also discuss that our model can be still used for human-machine interactions for compatibility with existing techniques and applications. © 2020 IEEE.
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