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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]

Objectives: Enhancing people's cognition of the planetary topography becomes an important task of planetary exploration. People living on the Earth have formed visual experiences and spatial cognition based on the Earth environment. It is a challenge to recognize and identify the landforms and phenomena of other planets. With the analysis of the different cognition of the topography between the Moon and the Earth, the enhanced visualization of the moon topography based on the earth cognitive transfer can make us more intuitively understand lunar cartography. Methods: We compare the similarities and the differences in topography and color system between the Moon and the Earth to build the cognition transferring from the Earth view to the Moon view, constructs visualization requirements with its similarities and differences be‐ tween the Moon and the Earth. Results: We formulate a scheme to enhance the visualization of the moon's topography according to people's existing cognition habits of the Earth's geomorphology, and take the lunar typical landforms, including the crater, the mares, the highlands and the riles, and lunar regional topography as examples to shade with different shading parameters (altitudinal angle and azimuth angle) and get corresponding shading effects. Conclusions: We discover the visualization similarities and the differences be‐ tween the Earth and the Moon with cognition transferring, and discover the enhanced moon visualization should utilize the similarities to meet our rapid visual thinking and break certain particular rules of the earth topographic visualization (like snow line) to illustrate lunar terrain characteristics. [ABSTRACT FROM AUTHOR]

Digital information technology has made human beings move into a mixed space where virtuality and reality are integrated. Maps, that have always been used as the abstraction and interpretation of the real world and assist us to cognize the real world, are facing new challenges. Under the new digital and communication situations, maps need to incorporated digital virtual spaces and analyze the impacts brought by virtuality and reality, nihility and existence and the interpromotion of void and solid. Game maps are suitable carrier in this paper to analyze and discover the relations between the virtuality and the reality. Game map has seven elements including the time, the place, the personage, the object, the event, the phenomenon and the scenario to describe the world. This paper analyzes their virtual and real characteristics, explores the relationships and the interactions between virtual world construction and the reality, and discusses their feedback to the real world. This research can enrich the theoretical system of cartography and facilitate planning a smarter and more pleasant society for us. [ABSTRACT FROM AUTHOR]

Objectives: The spatial delimitation of urban area is the foundation for monitoring the urbanization level and determining city scale from the perspective of geographical space.Methods: Based on the requirements of urban area defined in Document No. 51 of the State Council, this paper clarifies the connotations of urban area with further understandings and proposes a process for delimiting urban area in geographical space by connecting map polygons of governmental residents and neighborhood committee, which can reduce the subjectivity and fuzziness in the spatialization of process of urban area. Results: This paper develops the main idea and spatial delimitation about urban area and takes Wuhan as an example to illustrate the details about spatial delimitation of urban areas.Conclusions: This paper mainly discusses how to unify urban delineation under the current technical conditions, and does not mention how to use other auxiliary data, such as the application of multi⁃source big data in urban delineation, because of the unbalanced development of big data infrastructure in different cities. But it does not mean that the application of population big data and urban big data is not important.On the contrary, the application of big data in the work of urban area⁃ delimitation is the next research focus and the future development trend. [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]

Objectives: High definition map (HDM) is the “sensor” for automated driving (AD), which integrates the real-time data collected by various sensors and the prior data collected in the early stage, and serves the application of AD, including providing the necessary prior data and the corresponding over-the-horizon perception, calibration positioning, dynamic planning and decision-making and control. However, there still exists a gap between the supply of HDM data and the demand for AD knowledge, including difficulties in data retrieval due to the large volume of data and challenges in semantic understanding and intelligent decision-making due to weak data correlation. Therefore, how to balance the data supply of HDM and the knowledge demand of AD is the main goal of this paper. Methods: Knowledge graph (KG) is a representation of knowledge in a graph structure to describe entities and their relationships, involving entity extraction and relationship extraction, so that it can make the AD with interpretable, understandable and inferential. This means that KG can serve as an alternative and explicit simulation of the human mind and map cognition that driverless vehicles are missing. Therefore, we introduce KG on the basis of HDM data, and propose a framework of HDM-KG-driving task, so as to support the transformation of HDM data to AD knowledge. The construction of HDM-KG adopts the top-down method, that is, the pattern layer is first followed by the data layer. As the conceptual hierarchy of KG, pattern layer defines the concept, attribute and relation of map ontology, and it can explicitly describe the indirect information and implicit correlation information of map domain from the perspective of traffic. The data layer is an instantiation of the pattern layer, a way to populate the pattern layer through instance matching. Results: To verify the validity of the proposed method, a virtual simulation dataset based on OpenStreetMap data is constructed and converted into two formats. The first is the OpenDRIVE format, which is used to build the KG of static data, including the static knowledge of roads, lanes, intersections, road signs and road markings. The other is the data format of CARLA AD-simulator, which is used for AD simulation and the construction of dynamic real-time KG, including the dynamic knowledge of self-driving cars and vehicles in front of them, pedestrians and other traffic participants. Through the application of the resulting HDM-KG in semantic query, knowledge reasoning and local decision planning, the results show that the HDM-KG explicitly expresses the semantic relationship, enhances the application of HDM in AD, and can further improve AD by connecting additional knowledge such as safety requirements, traffic rules or scene background. Conclusions: HDM-KG can structurally and explicitly express the traffic semantics contained in map objects and the semantic relations between objects, traffic rule constraints, traffic scenes and other knowledge, and realize the three-layer evolution of data-information-knowledge of data sources-HDM-KG, providing support for semantic relation query, logical reasoning and motion planning required by driving tasks. In addition, the introduction of KG eliminates the need for driving tasks to perceive and predict objects and semantic relationships from data collected by sensors, which are explicitly defined by HDM-KG, reducing computational latency and improving safety and reliability. The top-down method of constructing KG will cost more labor and time, and the subsequent research will carry out the bottom-up method to automatically fill the KG with multi-source data. [ABSTRACT FROM AUTHOR]

Objectives: High definition(HD)map plays an important role in autonomous driving. Most existing research focuses on high-resolution mapping in terms of road geometries as well as the updating of static road information in HD map. However, there is still a lack of research on dynamic information involved in road traffic networks. We focus on enriching the content of HD map in terms of the dynamic information including the dynamic information, it's meta data structure and inforamation interaction. Methods: We propose a comprehensive set of content that HD map need to cover for the dynamic information based on existing work on HD map data organization and data standards. Based on this, combined with cloud ends of HD map systems and vehicle ends of autonomous driving vehicles as two information interaction terminals, a method for information interaction between cloud ends and vehicle ends under different combination modes (vehicle-cloud, vehicle-vehicle, and cloud-vehicle modes) used in HD map information system is proposed to facilitate a timely capture of dynamic information in road environments. Results: (1) The vehicle-cloud information interaction mode is suitable for self-driving cars. The vehicle collects vehicle dynamic information in real time and uploads it to the cloud of the HD map information system after preprocessing, so as to realize the sharing of perception information of different vehicles in the road environment on the cloud. (2)The vehicle-to-vehicle information interaction mode is used between different self-driving cars. This mode is mainly aimed at obtaining partially vehicle dynamic information directly through vehicleto-vehicle interaction during driving. (3)The cloud-vehicle information interaction mode is suitable for vehicles connected to the HD map information system platform. The cloud stores dynamic information, and the vehicle requests the demanding information according to its own needs. Therefore, The HD map information system continuously performs information interaction in three interaction modes, and updates information simultaneously, maintaining the freshness of dynamic information and enhancing the robustness of HD map. Conclusions: However, due to the complexity and variability of the road environment, the content of dynamic information interaction we propose can only cover what happens during vehicle driving to a certain extent. For the road information that appears with a small probability, we do not conduct in-depth research, which is also the direction we need to discuss and study in the future. [ABSTRACT FROM AUTHOR]

Cartography is a subject whose development was partially restricted due to historical conditions. Compared with the traditional map, driven by new technology and new demands, the mapping objects, representation form, perception channel and user role of maps have all undergone significant changes in the in‐ formation and communication technology (ICT) era. Therefore, the theoretical framework of cartography needs to be further adapted to the ICT era. This paper firstly analyzes the main characteristics of maps in the ICT era, then discusses the concepts and the scope of representation of modern maps in the new era, and finally tries to build the framework of modern maps including the representation object, information abstraction, map representation and map outcomes. This study is expected to clarify the boundary of extended connotation of cartography research and provide corresponding support for the future development of cartography. [ABSTRACT FROM AUTHOR]

Object: The influence of human factors on wayfinding performance is essential to improve the user experience of intelligent navigation service. Method: Based on four cognitive abilities, i.e. mental rotation, abstract reasoning, spatial short-term memory and spatial perception, this paper conducts several spatial cognitive experiments to analysis the correlation between spatial cognitive abilities and wayfinding performance. Results: The results indicate that: (1) These four spatial cognitive abilities are weakly correlated with each other, and can be separated partly with reference to the scale of recognition space. Specifically, cognitive group with mental rotation ability and abstract reasoning ability, and cognitive group with short ⁃ term memory ability and spatial perception ability, have stronger correlation respectively concerning the same space scale. While those groups where cognitive abilities cross different scale space have week correlation. (2) Mental rotation ability and abstract reasoning ability have higher significance and goodness of regression fitting on wayfinding performance. Considering the combined effect of multiple cognitive factors, in virtual scene, the mental rotation ability shows the highest influence while the spatial perception ability has the lowest influence; in real scene, the spatial perception ability shows the highest influence and the short-term memory ability has the lowest influence. (3) The wayfinding performance has significant scene effect with respect to the real and virtual scenes, i.e. the determinant coefficients of cognitive factors for way⁃ finding performance fitting varies with the scene type. Conclusions: These results provide an empirical reference for the effectiveness of pedestrian navigation research based on virtual scene. It can also serve as a basis for the design of navigation service considering human factors. [ABSTRACT FROM AUTHOR]