CN117804473A - Map data acquisition method, device, equipment and computer-readable storage medium - Google Patents

Abstract

A map data acquisition method, device, equipment and computer-readable storage medium. The method includes: determining the vehicle position according to a preset time interval; determining a target road segment based on the vehicle position for each determined vehicle position; obtaining first high-precision map data corresponding to the target road segment; and mapping the storage area based on the first high-precision map data. The second high-precision map data stored in the high-precision map data is updated, wherein the high-precision map data stored in the storage area is used to be called by the intelligent driving module. Through this application, the high-precision map data stored in the storage area is only related to the target road section corresponding to the vehicle position, which greatly reduces the amount of high-precision map data stored in the storage area. On this basis, the intelligent driving module performs When calling, the data processing pressure is less, thus reducing the consumption of computing resources.

Description

Map data acquisition method, device, equipment and computer-readable storage medium

Technical field

This application relates to the field of intelligent driving technology, and specifically to a map data acquisition method, device, equipment and computer-readable storage medium.

Background technique

High-precision map data is an important data basis that intelligent driving algorithms rely on when running. In the existing technology, high-precision map data covering all areas is generally stored in the vehicle in advance for the intelligent driving module to call it.

Since the high-precision map data contains very rich information on various road elements, the data volume of the high-precision map data is very large. Therefore, storing the high-precision map data of all regions requires a large amount of storage space, and the intelligent driving module is facing a large number of problems. When the amount of high-precision map data is large, the computing resources required are relatively high.

Contents of the invention

The present application provides a map data acquisition method, device, equipment and computer-readable storage medium, which can solve the technical problems existing in the prior art that high-precision map data in intelligent driving scenarios requires a large amount of storage space and consumes more computing power resources of the intelligent driving module.

In a first aspect, embodiments of the present application provide a method for obtaining map data. The method for obtaining map data includes:

determining the vehicle position at preset time intervals;

For each determined vehicle position, the target road segment is determined based on the vehicle position;

Obtaining first high-precision map data corresponding to the target road section;

The second high-precision map data stored in the storage area is updated based on the first high-precision map data, where the high-precision map data stored in the storage area is used to be called by the intelligent driving module.

With reference to the first aspect, in one implementation, the target road section is a road section where the vehicle position is located.

With reference to the first aspect, in one implementation, the step of determining the target road segment based on the vehicle position includes:

Determine the first road segment where the vehicle position is located;

Detect whether the distance between the vehicle position and the end point of the first road segment is less than a preset distance;

If the distance between the vehicle position and the end point of the first road segment is less than the preset distance, then the first road segment and the road segments adjacent to the first road segment are used as the target road segment;

If the distance between the vehicle position and the end point of the first road segment is not less than the preset distance, detect whether there is a second road segment whose starting point is less than the preset distance from the vehicle position;

If there is a second road segment, the first road segment and the second road segment are used as the target road segments.

In conjunction with the first aspect, in one implementation, the step of updating the second high-precision map data stored in the storage area based on the first high-precision map data includes:

Delete the second high-precision map data stored in the storage area;

Write the first high-precision map data into the storage area.

In combination with the first aspect, in one implementation, the step of updating the second high-precision map data stored in the storage area based on the first high-precision map data includes:

Determine the data to be added that exists in the first high-precision map data but does not exist in the second high-precision map data stored in the storage area, and determine the data to be deleted that exists in the second high-precision map data stored in the storage area but does not exist in the first high-precision map data;

The data to be deleted is deleted from the storage area, and the data to be added is written into the storage area.

In conjunction with the first aspect, in one implementation, it is determined that the data to be added that exists in the first high-precision map data but does not exist in the second high-precision map data that has been stored in the storage area is determined in the storage area. The steps for deleting data that exists in the second high-precision map data stored in the area but does not exist in the first high-precision map data include:

Compare the road section identification information contained in the first high-precision map data with the road section identification information contained in the second high-precision map data stored in the storage area;

Use the road segment information corresponding to the road segment identification information that exists in the first high-precision map data but does not exist in the second high-precision map data that has been stored in the storage area as the data to be added;

The road section information corresponding to the road section identification information that exists in the second high-precision map data stored in the storage area but does not exist in the first high-precision map data is used as the data to be deleted.

In combination with the first aspect, in one implementation, after the step of obtaining the first high-precision map data corresponding to the target road section, the method further includes:

Perform validity verification on the first high-precision map data;

If the validity check passes, the second high-precision map data stored in the storage area is updated based on the first high-precision map data.

In a second aspect, an embodiment of the present application provides a map data acquisition device, the map data acquisition device comprising:

A position determination module used to determine the vehicle position according to a preset time interval;

The road segment determination module is used to determine the target road segment based on the vehicle position for each determined vehicle position;

The acquisition module is used to obtain the first high-precision map data corresponding to the target road section;

An update module, configured to update the second high-precision map data stored in the storage area based on the first high-precision map data, wherein the high-precision map data stored in the storage area is used to be called by the intelligent driving module.

In a third aspect, embodiments of the present application provide a map data acquisition device. The map data acquisition device includes a processor, a memory, and a map data acquisition program stored on the memory and executable by the processor. When the map data acquisition program is executed by the processor, the steps of the map data acquisition method described in the first aspect are implemented.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium. A map data acquisition program is stored on the computer-readable storage medium. When the map data acquisition program is executed by a processor, the first step is implemented. The steps of the map data acquisition method described in this aspect.

The beneficial effects brought by the technical solutions provided by the embodiments of this application include:

In the embodiment of the present application, the vehicle position is determined according to a preset time interval; for each vehicle position determined, the target road section is determined based on the vehicle position; the first high-precision map data corresponding to the target road section is obtained; based on the first high-precision map data The second high-precision map data stored in the storage area is updated, wherein the high-precision map data stored in the storage area is used to be called by the intelligent driving module. Through this embodiment of the present application, the high-precision map data stored in the storage area is only related to the target road section corresponding to the vehicle position, which greatly reduces the amount of high-precision map data stored in the storage area. On this basis, the intelligent driving module When it is called, the data processing pressure is less, thereby reducing the consumption of computing resources.

Description of drawings

Figure 1 is a schematic flow chart of an embodiment of the map data acquisition method of the present application;

FIG2 is a schematic diagram of a driving scene in an embodiment of a method for acquiring map data of the present application;

Figure 3 is a schematic diagram of a driving scene in another embodiment of the map data acquisition method of the present application;

Figure 4 is a functional module schematic diagram of an embodiment of the map data acquisition device of the present application;

Figure 5 is a schematic diagram of the hardware structure of the map data acquisition device involved in the embodiment of the present application.

Detailed ways

In order to enable those in the technical field to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only These are part of the embodiments of this application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

In order to make the objectives, technical solutions and advantages of the present application more clear, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

In the first aspect, embodiments of the present application provide a method for obtaining map data.

In one embodiment, referring to FIG. 1 , FIG. 1 is a flow chart of an embodiment of a method for obtaining map data of the present application. As shown in FIG. 1 , the method for obtaining map data includes:

Step S10, determine the vehicle position according to a preset time interval;

In this embodiment, the preset time interval is set according to actual needs, for example, set to 3 minutes, 5 minutes, or 10 minutes.

Taking the preset time interval of 5 minutes as an example, considering that the high-precision map data is used to support the intelligent driving function, the vehicle position is obtained when the intelligent driving function is started, and subsequently, the vehicle position is obtained every 5 minutes.

Each time the vehicle position is determined, the vehicle position may be determined by any one of fusion positioning technology, combined navigation positioning technology or RTK positioning technology.

Furthermore, it is also possible to give priority to determining the vehicle position through the fused positioning technology. When the positioning cannot be performed through the fused positioning technology, the vehicle position is determined through the integrated navigation and positioning technology. When the positioning cannot be performed through the integrated navigation and positioning technology, the vehicle position is determined through the RTK positioning technology. vehicle location.

It should be noted that the solution used to determine the vehicle location is not limited to the above method.

Step S20, for each determined vehicle position, determine the target road segment based on the vehicle position;

In this embodiment, referring to the above description, as time goes by, multiple vehicle locations will be acquired sequentially, for example, respectively recorded as vehicle location 1, vehicle location 2, vehicle location 3, and so on.

It is easy to understand that as the position of the vehicle changes, the road segment where the vehicle is located and the road segments surrounding the vehicle may change, and only these road segments are or may be passed by the vehicle. Therefore, it can be determined based on the vehicle position that the vehicle is passing and /or possible road segments, and use these road segments as target road segments.

Furthermore, in one embodiment, the target road section is the road section where the vehicle is located.

In this embodiment, it is easy to understand that the high-precision map data is used for vehicle navigation, and vehicle navigation needs to be based on the road section where the vehicle is traveling. Therefore, the road section where the vehicle position is located can be directly used as the target road section to obtain the target. The first high-precision map data corresponding to road segments.

Further, in one embodiment, the step of determining the target road segment based on the vehicle position includes:

Determine the first road segment where the vehicle position is located;

Detecting whether the distance between the vehicle position and the end point of the first road section is less than a preset distance;

If the distance between the vehicle position and the end point of the first road segment is less than the preset distance, then the first road segment and the road segments adjacent to the first road segment are used as the target road segment;

If the distance between the vehicle position and the end point of the first road section is not less than the preset distance, detecting whether there is a second road section whose starting point and the vehicle position have a distance less than the preset distance;

If there is a second road segment, the first road segment and the second road segment are used as the target road segments.

In this embodiment, refer to FIG. 2 , which is a schematic diagram of a driving scene in an embodiment of the map data acquisition method of the present application. As shown in Figure 2, the first road section where the vehicle position (point A in Figure 2) is located is road section 1, which is shown as a solid line in Figure 2; if the vehicle position at this time is the same as the end point of road section 1 (point A in Figure 2 If the distance from point B) is less than the preset distance, then road segment 1 and the road segment adjacent to road segment 1 (i.e., road segment 2, shown with a dotted line in Figure 2) are the target road segments.

It is easy to understand that there may be multiple road segments adjacent to road segment 1, so road segment 1 and all road segments adjacent to road segment 1 are the target road segments.

The preset distance is set according to actual needs, for example, 200m, 500m, etc., and is not limited here.

Through this embodiment, the first road section where the vehicle is located and the road sections adjacent to the first road section are used as the target road section, thereby obtaining the first high-precision map data corresponding to the target road section, thereby obtaining the information when the vehicle is merging and merging into the scene. The required high-precision map data.

Referring to Figure 3, Figure 3 is a schematic diagram of a driving scene in another embodiment of the map data acquisition method of the present application. As shown in Figure 3, the first road section where the vehicle position (point A in Figure 3) is located is road section 1, which is shown as a solid line in Figure 3; if the vehicle position at this time is different from the end point of road section 1 (point A in Figure 3 The distance between point B) is not less than the preset distance, and the distance between the starting point of road segment 2 (shown as a dotted line in Figure 3) (point C in Figure 3) and the vehicle position is less than the preset distance, then road segment 1 and road segment 2 for the target road section.

It is easy to understand that there may be multiple road segments whose distances between the starting point and the vehicle position are less than the preset distance, so road segment 1 and all road segments whose distances between the starting point and the vehicle position are less than the preset distance are the target road segments.

The preset distance is set according to actual needs, for example, 200m, 500m, etc., and is not limited here.

Through this embodiment, the first road section and the second road section are used as target road sections to obtain the first high-precision map data corresponding to the target road section, thereby obtaining the high-precision map data required for vehicles to divert and exit scenes.

Step S30, obtaining first high-precision map data corresponding to the target road section;

In the existing technology, the high-precision map data used by the intelligent driving module is often the high-precision map data corresponding to the entire area, including relevant information of hundreds or thousands of road sections in the entire area. In this embodiment, only the first high-precision map data corresponding to the target road section determined based on the vehicle position is obtained. Compared with the high-precision map data corresponding to the entire area, the amount of data is greatly reduced.

Step S40: Update the second high-precision map data stored in the storage area based on the first high-precision map data, where the high-precision map data stored in the storage area is used to be called by the intelligent driving module.

In this embodiment, step S10 and its subsequent steps are executed as the smart driving function is turned on. When the smart driving function is turned on, the storage area can be cleared. Then, for the first highest value obtained based on the vehicle position determined for the first time, Precision map data 1, when step S40 is executed, since the second high-precision map data stored in the storage area is empty at this time, the first high-precision map data 1 is directly written into the storage area; for the second determined The first high-precision map data 2 obtained by the vehicle position, when step S40 is executed, the second high-precision map data stored in the storage area at this time, that is, the first high-precision map data 1, is based on the first high-precision map data 2 Update the first high-precision map data 1, and so on.

The update method may be to add the first high-precision map data 2 to the storage area, or to replace the first high-precision map data 1 with the first high-precision map data 2 . Select the update method based on actual needs.

Further, in one embodiment, step S40 includes:

Step S401, delete the second high-precision map data stored in the storage area;

Step S402: Write the first high-precision map data into the storage area.

In this embodiment, the second high-precision map data stored in the storage area is updated in a replacement manner. Specifically, the second high-precision map data stored in the storage area is first deleted, and then the first high-precision map data is deleted. Map data is written to the storage area.

Furthermore, in one embodiment, step S40 includes:

Step S403, determine the data to be added that exists in the first high-precision map data but does not exist in the second high-precision map data stored in the storage area, and determine the second high-precision map data stored in the storage area. Data to be deleted that exists in but does not exist in the first high-precision map data;

Step S404: Delete the data to be deleted from the storage area, and write the data to be added into the storage area.

In this embodiment, assuming that the second high-precision map data is the high-precision map data corresponding to road section 1, road section 2, and road section 3, and the first high-precision map data is the high-precision map data corresponding to road section 2, road section 3, and road section 4, then the data to be added is the high-precision map data corresponding to road section 4, and the data to be deleted is the high-precision map data corresponding to road section 1. On this basis, it is only necessary to delete the high-precision map data corresponding to road section 1 from the storage area, and write the high-precision map data corresponding to road section 4 into the storage area.

Further, in one embodiment, step S403 includes:

Compare the road section identification information contained in the first high-precision map data with the road section identification information contained in the second high-precision map data stored in the storage area;

Use the road segment information corresponding to the road segment identification information that exists in the first high-precision map data but does not exist in the second high-precision map data that has been stored in the storage area as the data to be added;

The road segment information corresponding to the road segment identification information that exists in the second high-precision map data stored in the storage area but does not exist in the first high-precision map data is used as the data to be deleted.

In this embodiment, the high-precision map data includes road section information and road section identification information corresponding to the road section, and different road sections correspond to different road section identification information.

By comparing the road section identification information contained in the first high-precision map data with the road section identification information contained in the second high-precision map data, it can be determined which road section information exists in the first high-precision map data but is not present in the second high-precision map data. The data does not exist in the high-precision map data, and which road sections have road segment information that exists in the second high-precision map data but does not exist in the first high-precision map data, thereby determining the data to be added and the data to be deleted.

Specifically, array 1 and array 2 may be set, wherein array 1 and array 2 are both initially empty. When the first high-precision map data 1 is obtained based on the vehicle position determined for the first time, the road section identification information contained in the first high-precision map data 1 is placed in array 2, and the elements in array 1 and array 2 are compared at this time to determine the data to be added and the data to be deleted, and then the elements in array 1 are replaced with the elements in array 2, and array 2 is cleared; and when the first high-precision map data 2 is obtained based on the vehicle position determined for the second time, the road section identification information contained in the first high-precision map data 2 is placed in array 2, and the elements in array 1 and array 2 are compared at this time to determine the data to be added and the data to be deleted, and then the elements in array 1 are replaced with the elements in array 2, and array 2 is cleared, and so on.

In the embodiment of the present application, the vehicle position is determined at a preset time interval; for each determined vehicle position, a target road section is determined based on the vehicle position; first high-precision map data corresponding to the target road section is obtained; and second high-precision map data stored in the storage area is updated based on the first high-precision map data, wherein the high-precision map data stored in the storage area is used to be called by the intelligent driving module. Through this embodiment of the present application, the high-precision map data stored in the storage area is only related to the target road section corresponding to the vehicle position, which greatly reduces the amount of high-precision map data stored in the storage area. On this basis, when the intelligent driving module calls it, the data processing pressure is relatively small, thereby reducing the consumption of computing resources.

Further, in one embodiment, after step S30, it also includes:

Perform validity verification on the first high-precision map data;

If the validity check passes, the second high-precision map data stored in the storage area is updated based on the first high-precision map data.

In this embodiment, after the first high-precision map data is acquired based on any determined vehicle position, the first high-precision map data is firstly verified for validity. The validity verification includes detecting whether there is complete road section information in the first high-precision map data and whether the timestamp of the road section information is valid; if there is complete road section information and the timestamp of the road section information is valid, it is determined that the validity verification of the first high-precision map data has passed, and on this basis, the subsequent step of updating the second high-precision map data stored in the storage area based on the first high-precision map data is performed.

It should be noted that the validity verification of the first high-precision map data can also be performed through other methods, which are not limited here.

In a second aspect, an embodiment of the present application also provides a map data acquisition device.

In one embodiment, refer to FIG. 4 , which is a functional module schematic diagram of an embodiment of the map data acquisition device of the present application. As shown in Figure 4, the map data acquisition device includes:

The position determination module 10 is used to determine the vehicle position according to a preset time interval;

The road segment determination module 20 is used to determine the target road segment based on the vehicle position for each determined vehicle position;

The acquisition module 30 is used to acquire the first high-precision map data corresponding to the target road section;

The updating module 40 is used to update the second high-precision map data stored in the storage area based on the first high-precision map data, wherein the high-precision map data stored in the storage area is used to be called by the intelligent driving module.

Further, in one embodiment, the target road segment is the road segment where the vehicle position is located.

Furthermore, in one embodiment, the road section determination module 20 is specifically configured to:

Determine a first road section where the vehicle is located;

Detecting whether the distance between the vehicle position and the end point of the first road section is less than a preset distance;

If the distance between the vehicle position and the end point of the first road segment is less than the preset distance, then the first road segment and the road segments adjacent to the first road segment are used as the target road segment;

If the distance between the vehicle position and the end point of the first road section is not less than the preset distance, detecting whether there is a second road section whose starting point and the vehicle position have a distance less than the preset distance;

If there is a second road segment, the first road segment and the second road segment are used as the target road segments.

Further, in one embodiment, the update module 40 is specifically used for:

Delete the second high-precision map data stored in the storage area;

Write the first high-precision map data into the storage area.

Further, in one embodiment, the update module 40 is specifically used for:

Determine the data to be added that exists in the first high-precision map data but does not exist in the second high-precision map data stored in the storage area, and determine that it exists but does not exist in the second high-precision map data stored in the storage area. Data to be deleted that does not exist in the first high-precision map data;

Delete the data to be deleted from the storage area and write the data to be added to the storage area.

Further, in one embodiment, the update module 40 is specifically configured to:

Compare the road section identification information contained in the first high-precision map data with the road section identification information contained in the second high-precision map data stored in the storage area;

Use the road segment information corresponding to the road segment identification information that exists in the first high-precision map data but does not exist in the second high-precision map data that has been stored in the storage area as the data to be added;

The road segment information corresponding to the road segment identification information that exists in the second high-precision map data stored in the storage area but does not exist in the first high-precision map data is used as the data to be deleted.

Further, in one embodiment, the map data acquisition device further includes a verification module for:

Perform validity verification on the first high-precision map data;

The update module 40 is also configured to update the second high-precision map data stored in the storage area based on the first high-precision map data if the validity check is passed.

The functional implementation of each module in the above-mentioned map data acquisition device corresponds to each step in the above-mentioned map data acquisition method embodiment, and its functions and implementation processes are not described one by one here.

In a third aspect, embodiments of the present application provide a map data acquisition device. The map data acquisition device may be a personal computer (PC), laptop, server, or other device with data processing functions.

Referring to Figure 5, Figure 5 is a schematic diagram of the hardware structure of the map data acquisition device involved in the embodiment of the present application. In this embodiment of the present application, the map data acquisition device may include a processor, a memory, a communication interface, and a communication bus.

The communication bus can be of any type and is used to interconnect processors, memories and communication interfaces.

The communication interface includes input/output (I/O) interface, physical interface and logical interface, etc., which are used to realize the interconnection of devices inside the map data acquisition device, and the interface used to realize the interconnection between the map data acquisition device and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory , hard disk, programmable ROM (PROM), erasable PROM (erasable PROM, EPROM), electrically erasable PROM (electrically erasable PROM, EEPROM), etc.

The processor may be a general-purpose processor, and the general-purpose processor may call the map data acquisition program stored in the memory and execute the map data acquisition method provided by the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). For the method executed when the map data acquisition program is called, reference may be made to various embodiments of the map data acquisition method of the present application, and will not be described again here.

Those skilled in the art can understand that the hardware structure shown in Figure 5 does not constitute a limitation of the present application, and may include more or fewer components than shown, or combine certain components, or arrange different components.

In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium.

A map data acquisition program is stored on the computer-readable storage medium of the present application. When the map data acquisition program is executed by a processor, the steps of the above map data acquisition method are implemented.

The method implemented when the map data acquisition program is executed can refer to the various embodiments of the map data acquisition method of the present application, and will not be repeated here.

It should be noted that the above serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

The terms "including" and "having" and any variations thereof in the description and claims of this application and the above-described drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes Other steps or units inherent to such processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc. They do not represent a sequence, nor do they limit "first", "second" and "third" are different types.

In the description of the embodiments of this application, "exemplary", "such as" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design described as "exemplary," "such as," or "for example" in the embodiments of the present application is not to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the words "exemplary," "such as," or "for example" is intended to present the concepts in a concrete manner.

In the description of the embodiments of this application, unless otherwise stated, "/" means or, for example, A/B can mean A or B; "and/or" in the text is just a way to describe the association of associated objects. Relationship means that there can be three relationships. For example, A and/or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiment of this application, " "Plural" means two or more than two.

Some of the processes described in the embodiments of this application include multiple operations or steps that appear in a specific order. However, it should be understood that these operations or steps may be performed out of the order in which they appear in the embodiments of this application or in parallel. , the sequence number of the operation is only used to distinguish different operations, the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be performed sequentially or in parallel, and these operations or steps may be combined.

Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product that is essentially or contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM) as mentioned above. , magnetic disk, optical disk), including several instructions to cause a terminal device to execute the methods described in various embodiments of the present application.

The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of the present application may be directly or indirectly used in other related technical fields. , are all equally included in the patent protection scope of this application.

Claims (10)

1. A map data acquisition method, characterized in that the map data acquisition method comprises:

determining the position of the vehicle according to a preset time interval;

determining a target road segment based on the vehicle position for each determined vehicle position;

acquiring first high-precision map data corresponding to a target road section;

and updating the second high-precision map data stored in the storage area based on the first high-precision map data, wherein the high-precision map data stored in the storage area is used for being called by the intelligent driving module.

2. The map data acquisition method according to claim 1, wherein the target link is a link in which a vehicle position is located.

3. The map data acquisition method according to claim 1, wherein the step of determining the target link based on the vehicle position includes:

determining a first road section where the vehicle position is located;

detecting whether the distance between the vehicle position and the end point of the first road segment is smaller than a preset distance;

if the distance between the vehicle position and the end point of the first road section is smaller than the preset distance, the first road section and the road section adjacent to the first road section are taken as target road sections;

if the distance between the vehicle position and the end point of the first road section is not smaller than the preset distance, detecting whether a second road section with the distance between the starting point and the vehicle position smaller than the preset distance exists or not;

and if the second road section exists, taking the first road section and the second road section as target road sections.

4. The map data obtaining method according to claim 1, wherein the step of updating the second high-definition map data stored in the storage area based on the first high-definition map data includes:

deleting the second high-precision map data stored in the storage area;

the first high-precision map data is written into the storage area.

5. The map data obtaining method according to claim 1, wherein the step of updating the second high-definition map data stored in the storage area based on the first high-definition map data includes:

determining data to be newly added that exists in the first high-precision map data but does not exist in the second high-precision map data stored in the storage area, and determining data to be deleted that exists in the second high-precision map data stored in the storage area but does not exist in the first high-precision map data;

deleting the data to be deleted from the storage area, and writing the data to be newly added into the storage area.

6. The map data acquisition method according to claim 5, wherein the step of determining newly added data that exists in the first high-precision map data but does not exist in the second high-precision map data stored in the storage area, and determining to delete data that exists in the second high-precision map data stored in the storage area but does not exist in the first high-precision map data, includes:

comparing the road section identification information contained in the first high-precision map data with the road section identification information contained in the second high-precision map data stored in the storage area;

taking road section information corresponding to road section identification information which exists in the first high-precision map data but does not exist in the second high-precision map data stored in the storage area as data to be newly added;

and taking the road section information corresponding to the road section identification information which exists in the second high-precision map data stored in the storage area but does not exist in the first high-precision map data as the data to be deleted.

7. The map data acquisition method according to any one of claims 1 to 6, characterized by further comprising, after the step of acquiring the first high-precision map data corresponding to the target link:

performing validity check on the first high-precision map data;

and if the validity check is passed, updating the second high-precision map data stored in the storage area based on the first high-precision map data.

8. A map data acquisition apparatus, characterized in that the map data acquisition apparatus comprises:

the position determining module is used for determining the position of the vehicle according to a preset time interval;

a road segment determining module for determining a target road segment based on the vehicle position for each determined vehicle position;

the acquisition module is used for acquiring first high-precision map data corresponding to the target road section;

and the updating module is used for updating the second high-precision map data stored in the storage area based on the first high-precision map data, wherein the high-precision map data stored in the storage area are used for being called by the intelligent driving module.

9. A map data acquisition apparatus comprising a processor, a memory, and a map data acquisition program stored on the memory and executable by the processor, wherein the map data acquisition program, when executed by the processor, implements the steps of the map data acquisition method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a map data acquisition program is stored, wherein the map data acquisition program, when executed by a processor, implements the steps of the map data acquisition method according to any one of claims 1 to 7.