Information Transmission Model of High Definition Map for Autonomous Driving
Objectives:The"non-visual"and machine-oriented characteristics of high definition map dis-tinguish 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 relation-ships,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 infor-mation 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 transmis-sion 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 ex-tended the subject of map information cognition from humans to machines,adapting to the full artificial in-telligence characteristics of high definition map in the transmission process of perception,localization,plan-ning,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 impor-tant guiding role in driving services,including content selection,expression methods,system functional framework design,and improving transmission efficiency.
high definition mapmap information transmission modelautonomous drivingmap spatial cognition
万方数据
维普
