自动驾驶高精地图的信息传输模型.

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]