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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: In the intelligent driving scene, the collaborative calls of real-time dynamic information and static data of high definition maps can support the intelligent driving system to accurately reconstruct the road driving scenes, and make safe and efficient decisions for complex road environment and emergencies. Therefore, the correlation between dynamic data and static data is a key technique for achieving vehicle's intelligent path planning and decision-making. Methods: To solve the problem of weak real-time coupling of dynamic and static data in current high definition map model, we propose an association method of dynamic and static data within high definition map based on the association principles of dynamic and static data, and this association method depends and can be triggered by update mechanism upon custom attributes and specific geographic locations. Results: We propose a high definition map dynamic and static data correlation method and analyze the impacts on intelligent driving through different macro-, meso- and microscales driving levels to verify the method's validity. Conclusions: The establishment of the dynamic and static data association relationship of the high definition map not only supports the planning, decision-making and control of the auto-driving system, but also lays the foundation for the realization of safe, efficient and comfortable intelligent driving of vehicles. [ABSTRACT FROM AUTHOR]