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High definition (HD) map is an indispensable part of autonomous driving, and its development will greatly affect the level of autonomous driving. The existing HD map models are diverse, lacking the systems thinking of “human-vehicle-road-map”, and the vehicle-oriented consideration is not thoughtful enough. Based on the consideration of more explicit layers, more comprehensive elements and more prominent user characteristics, we attempt to refine and enrich the data logic structure of intelligent high precision map, and elaborate the contents and functions of static data layer, road real-time information layer, vehicle dynamic information layer and user model layer in detail. We point out three key technologies, the collaborative updating mechanism of “cloud-edge-vehicle”, the intersection safety information framework, and the navigation decision-making driven by traffic event. It describes how these three key technologies support the global and local path navigation decisions and the control of autonomous vehicle. Decision supports of HD map for autonomous driving are explained from four aspects, perception, positioning, decision-making, and control. [ABSTRACT FROM AUTHOR]

Objectives: The “non-visual” and machine-oriented characteristics of high definition map distinguish them from traditional human-oriented spatiotemporal products. Correspondingly, the transmission model describing the relationships between map subjects, objects, and their products also faces significant changes. Existing high definition map information transmission models have reconstructed these relationships, including the addition of user-specific information and its transmission. However, there are still shortcomings in the use of human-oriented map language instead of machine-oriented language as the information transmission tool. To address this, we combine the transmission characteristics of map information in autonomous driving and construct a machine-oriented cognitive high definition map information transmission model. Methods: We propose three extensions to the existing map information transmission model: Substituting GIS language for map language, integrating user-specific information into the user layer of high definition map, and expanding action guidance to action practice. Results: The research results show that the constructed machine-oriented cognitive high definition map information transmission model has extended the subject of map information cognition from humans to machines, adapting to the full artificial intelligence characteristics of high definition map in the transmission process of perception, localization, planning, and control. Conclusions: The proposed model contributes to accurately grasping the essence and content structure of high definition map, enhancing cognitive performance. Additionally, it plays an important guiding role in driving services, including content selection, expression methods, system functional framework design, and improving transmission efficiency. [ABSTRACT FROM AUTHOR]