Data expression method and device of self-explaining data model of high-precision map

Abstract

The application relates to a data expression method and device of a self-explaining data model of a high-precision map, which comprises the following steps: acquiring public information, data attribute set definition and element classification of high-precision map data to be expressed, wherein the public information comprises data manufacturing process standards, data version, data partition mode and a coordinate system; defining and storing a unique number, an element classification number, a shape type, a shape point set and a confidence degree of data expression of each element object, and constructing one or more element objects according to the same; storing the association between each element object and attributes, grouping the attributes, recording the unique number of the associated element object for each group of attributes, and expressing all attribute information of each element object. In the application, the data content is classified and abstracted, the general object and the attribute are decoupled, the adaptability and the rapid iteration of the element are realized through the definable attribute and the attribute expression mode.

Description

Technical Field

[0001] This invention belongs to the field of high-precision map production technology, specifically relating to a data expression method and apparatus for a self-interpretive data model of a high-precision map. Background Technology

[0002] High-precision electronic maps primarily serve autonomous vehicles, providing them with lane-level planning, guidance, and vehicle positioning assistance within road segments. As the industry is rapidly developing, the data content requirements of autonomous driving are constantly changing. This necessitates that high-precision map data be able to support these rapidly evolving needs, providing more suitable data content to meet the data usage requirements of autonomous vehicles, thereby accelerating the implementation of autonomous driving solutions and the development of the autonomous driving industry. Summary of the Invention

[0003] To enable rapid iteration and improve the adaptability of high-precision map data representation, a data representation method for a self-interpretive data model of a high-precision map is provided in a first aspect of this invention. The method includes: acquiring common information, data attribute set definitions, and feature classifications of the high-precision map data to be represented; defining and storing the unique identifier, feature classification number, shape type, shape point set, and confidence level of each feature object, and constructing one or more feature objects based on these definitions; storing the association between each feature object and its attributes, grouping attributes, with each group recording the unique identifier of the associated feature object and representing all attribute information of each feature object.

[0004] In some embodiments of the present invention, the data attribute set definition includes the data attribute number, name, data type, attribute value, and attribute meaning; the element classification includes the element classification number and element name.

[0005] In some embodiments of the present invention, defining and storing the unique number, feature classification number, shape type, shape point set, and confidence level of the data representation for each feature object, and constructing one or more feature objects based thereon, includes: expressing the content to be expressed other than the unique number, feature classification number, shape type, shape point set, and confidence level of the data representation through attributes or associations between attributes.

[0006] In some embodiments of the present invention, storing the association between each element object and its attributes, by grouping the attributes, with each group of attributes recording a unique number of the associated element object, and expressing all attribute information of each element object, includes: expressing newly added data to be expressed based on changes in the quantity of data to be expressed, changes in the type of element object, and changes in attributes.

[0007] Furthermore, the process of expressing newly added data based on changes in the quantity, type, and attributes of the data to be expressed includes: if the attribute classification remains unchanged but the type of the element object changes, then: update the definition of the data attribute set and the element classification; if the element attributes change, then update the classification number, name, data type, attribute value, and meaning of the newly added attributes.

[0008] In the above embodiments, the elements include: road center line, lane edge line, lane center line, lane node, ground marking line, pedestrian crossing, speed bump and parking space.

[0009] A second aspect of the present invention provides a data representation device for a self-interpreting data model of a high-precision map, comprising: a data management unit for acquiring common information, data attribute set definitions, and feature classifications of the high-precision map data to be represented, wherein the common information includes data production process standards, data version, data partitioning method, and coordinate system; an entity object unit for defining and storing a unique number, feature classification number, shape type, shape point set, and confidence level of the data representation for each feature object, and constructing one or more feature objects based thereon; and an attribute management unit for storing the association between each feature object and its attributes, wherein by grouping attributes, each group of attributes records the unique number of the associated feature object and represents all attribute information of each feature object.

[0010] Furthermore, the entity object section also includes: an expression unit, used to express the content to be expressed, excluding the unique number, feature classification number, shape type, shape point set, and confidence level of the data expression, through attributes or associations between attributes.

[0011] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data representation method for the self-interpretive data model of the high-precision map provided in the first aspect of the present invention.

[0012] In a fourth aspect, the present invention provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the data representation method for the self-interpretive data model of the high-precision map provided in the first aspect of the present invention.

[0013] The beneficial effects of this invention are:

[0014] This invention constructs an adaptive data representation model, classifies and abstracts data content, decouples general objects and attributes, defines attributes through a data management module, and expresses specific attribute methods through a data entity module. This enables adaptive changes in data element content, allowing the data production platform to output new data through a unified logic, maximizing the preservation of the value of existing data, without being affected by the limitations of existing data in updating map provider data production standards. This greatly promotes the development and iteration of high-precision data required for autonomous driving. Attached Figure Description

[0015] Figure 1 This is a basic flowchart illustrating the data representation method of the self-interpretive data model of a high-precision map in some embodiments of the present invention.

[0016] Figure 2 This is a schematic diagram of the attribute definition results in some embodiments of the present invention;

[0017] Figure 3 This is a schematic diagram illustrating the attribute definition content in some embodiments of the present invention;

[0018] Figure 4 This is a basic schematic diagram of the element classification definition structure in some embodiments of the present invention;

[0019] Figure 5 This is a schematic diagram of the element classification definition structure in some embodiments of the present invention.

[0020] Figure 6 This is a schematic diagram of the element object table structure in some embodiments of the present invention;

[0021] Figure 7 This is a schematic diagram of the element object and attribute association table structure in some embodiments of the present invention;

[0022] Figure 8 This is a schematic diagram of the data representation device for a self-interpreting data model of a high-precision map in some embodiments of the present invention;

[0023] Figure 9 This is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. Detailed Implementation

[0024] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0025] refer to Figure 1In a first aspect of the present invention, a data representation method for a self-interpretive data model of a high-precision map is provided, comprising: S100. acquiring common information, data attribute set definition, and feature classification of the high-precision map data to be represented, wherein the common information includes data production process standards, data version, data partitioning method, and coordinate system; S200. defining and storing a unique number, feature classification number, shape type, shape point set, and confidence level of the data representation for each feature object, and constructing one or more feature objects based thereon; S300. storing the association between each feature object and its attributes, wherein by grouping the attributes, each group of attributes records the unique number of the associated feature object and represents all attribute information of each feature object.

[0026] refer to Figures 2 to 5 In step S100 of some embodiments of the present invention, common information, data attribute set definitions, and feature classifications of the high-precision map data to be represented are obtained. The common information includes data production process standards, data version, data partitioning method, and coordinate system. Specifically:

[0027] ① Record common data information, including data production process standards, data version, data partitioning method, coordinate system and other general information.

[0028] ② Record the definition of the data attribute set, which attributes are used in the stored data, the data type of the attributes, and the meaning of the attribute values.

[0029] In step S200 of some embodiments of the present invention, the data attribute set definition includes the attribute number, name, data type, attribute value and attribute meaning of the data; the element classification includes the element classification number and element name.

[0030] refer to Figure 6 In some embodiments of the present invention, defining and storing the unique number, feature classification number, shape type, shape point set, and confidence level of the data representation for each feature object, and constructing one or more feature objects based thereon, includes: expressing the content to be expressed other than the unique number, feature classification number, shape type, shape point set, and confidence level of the data representation through attributes or associations between attributes.

[0031] refer to Figure 7 In step S300 of some embodiments of the present invention, storing the association between each element object and the attribute, by grouping the attributes, each group of attributes records the unique number of the associated element object, and expressing all the attribute information of each element object includes: expressing the newly added data to be expressed according to the changes in the quantity of data to be expressed, the changes in the type of element object, and the changes in the attributes.

[0032] It is understandable that by specifying the data types and value ranges of different attributes in the storage definition, expressing the unique number, shape and confidence information that the object should have in the data object entity, and storing the relationship between the attributes and the object in the data attributes, the real world is abstractly expressed in a hierarchical manner, thereby supporting the expression of all changes in the world data.

[0033] Furthermore, the process of expressing newly added data based on changes in the quantity, type, and attributes of the data to be expressed includes: if the attribute classification remains unchanged but the type of the element object changes, then: update the definition of the data attribute set and the element classification; if the element attributes change, then update the classification number, name, data type, attribute value, and meaning of the newly added attributes.

[0034] Specifically, the possible changes in the data and the methods supporting its representation:

[0035] ①Adding object record data

[0036] The attribute categories and values ​​remain unchanged; only the number of recorded data increases. This is a normal increase in the number of data records and requires no special processing.

[0037] ② Attribute classification remains unchanged, but the number of object types has increased or decreased.

[0038] The classification code and attribute classification table structure description in the Data Management Department need to be updated.

[0039] ③ Changes in Element Attribute Types

[0040] During the data compilation phase, the records in the attribute management structure are updated, and the number, name, data type, attribute value, and meaning of the newly added attribute category are supplemented.

[0041] ④ Changes in element attribute values

[0042] During the data creation phase, the records in the attribute management structure are updated, and the value range and meaning of newly added attribute types are supplemented. In the above embodiment, the elements include: road centerline, lane edge line, lane centerline, lane node, ground marking line, pedestrian crossing, speed bump, and parking space.

[0043] This layered representation approach ensures that regardless of data changes, the data expression logic structure remains unchanged, allowing for compatibility with both new and old data representations. The data production platform, through its fixed logic, can accommodate changes in data production standards due to external demands, maximizing the reuse of existing data while supplementing new data, thus minimizing the limitations imposed by existing data on data product standard changes. By eliminating concerns about the impact of existing data, map providers can continuously optimize data standards while supporting multiple standards, reducing the complexity of meeting customer needs and improving the timeliness of responding to autonomous driving data requirements.

[0044] Example 2

[0045] refer to Figure 8 In a second aspect, the present invention provides a data representation device 1 for a self-interpreting data model of a high-precision map, comprising: a data management unit 11, used to acquire common information, data attribute set definitions, and feature classifications of the high-precision map data to be represented, wherein the common information includes data production process standards, data version, data partitioning method, and coordinate system; an entity object unit 12, used to define and store the unique number, feature classification number, shape type, shape point set, and confidence level of each feature object, and construct one or more feature objects based thereon; and an attribute management unit 13, used to store the association between each feature object and its attributes, wherein by grouping the attributes, each group of attributes records the unique number of the associated feature object and expresses all attribute information of each feature object.

[0046] Furthermore, the entity object unit 12 also includes an expression unit, used to express the content to be expressed, excluding the unique number, feature classification number, shape type, shape point set and confidence level of data expression, through attributes or associations between attributes.

[0047] Example 3

[0048] refer to Figure 9 A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of the first aspect of the present invention.

[0049] Electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0050] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, hard disks; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 9 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 9 Each box shown can represent a device or multiple devices as needed.

[0051] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, it performs the functions defined in the methods of embodiments of this disclosure. It should be noted that the computer-readable medium described in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0052] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more computer programs, which, when executed by the electronic device, cause the electronic device to:

[0053] Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, C++, and Python—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0054] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0055] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data representation method for a self-interpretive data model of a high-precision map, characterized in that, include: Obtain common information, data attribute set definition, and feature classification of the high-precision map data to be expressed. The common information includes data production process standards, data version, data partitioning method, and coordinate system. Define and store the unique ID, feature classification number, shape type, shape point set, and confidence level of the data representation for each feature object, and construct one or more feature objects based on them; The association between each feature object and its attributes is stored. By grouping attributes, each group of attributes records a unique number of the associated feature object and expresses all attribute information of each feature object. New data to be expressed is expressed based on changes in the quantity of data to be expressed, changes in the type of feature object, and changes in attributes. Specifically, expressing new data to be expressed based on changes in the quantity of data to be expressed, changes in the type of feature object, and changes in attributes includes: if the attribute classification remains unchanged but the type of feature object changes, then: update the data attribute set definition and feature classification; if the feature attribute changes, then update the classification number, name, data type, attribute value, and meaning of the new attribute.

2. The data representation method for the self-interpretive data model of high-precision maps according to claim 1, characterized in that, The data attribute set definition includes the data's attribute number, name, data type, attribute value, and attribute meaning; The element classification includes element classification number and element name.

3. The data representation method for the self-interpretive data model of high-precision maps according to claim 1, characterized in that, The definition and storage of a unique identifier, feature classification number, shape type, shape point set, and confidence level of each feature object, and the construction of one or more feature objects based thereon, include: For content to be expressed other than unique ID, feature classification number, shape type, shape point set, and confidence level of data representation, it is expressed through attributes or the relationship between attributes.

4. The data representation method for a self-interpretive data model of a high-precision map according to any one of claims 1 to 3, characterized in that, The elements include: road centerline, lane edge line, lane centerline, lane node, ground markings, pedestrian crossing, speed bumps, and parking spaces.

5. A data representation device for a self-interpreting data model of a high-precision map, characterized in that, include: The data management department is responsible for acquiring common information, data attribute set definitions, and feature classifications of the high-precision map data to be expressed. The common information includes data production process standards, data version, data partitioning method, and coordinate system. The Entity Objects section is used to define and store the unique number, feature classification number, shape type, shape point set, and confidence level of each feature object, and to construct one or more feature objects based on these parameters. The attribute management department stores the association between each feature object and its attributes. By grouping attributes, each group records a unique number of the associated feature object and expresses all attribute information of each feature object. It expresses newly added data to be expressed based on changes in the quantity of data to be expressed, changes in the type of feature object, and changes in attributes. Specifically, expressing newly added data to be expressed based on changes in the quantity of data to be expressed, changes in the type of feature object, and changes in attributes includes: if the attribute classification remains unchanged but the type of feature object changes, then: update the data attribute set definition and feature classification; if the feature attribute changes, then update the classification number, name, data type, attribute value, and meaning of the newly added attribute.

6. The data representation device for the self-interpretive data model of a high-precision map according to claim 5, characterized in that, The entity object section also includes: The expression unit is used to express the content to be expressed, excluding unique ID, feature classification number, shape type, shape point set, and confidence level of data expression, through attributes or the relationship between attributes.

7. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the data representation method of the self-interpretive data model of the high-precision map as described in any one of claims 1 to 4.

8. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the data representation method of the self-interpretive data model of the high-precision map as described in any one of claims 1 to 4.

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