Cloud driving assistance map construction method and system, readable storage medium and vehicle

Abstract

The invention discloses a cloud driving assistance map construction method and system, a readable storage medium and a vehicle, and the method comprises the steps: setting a vehicle navigation path, and obtaining the anonymization data of a vehicle end advanced driving assistance function; identifying and classifying the anonymized data, aggregating and aligning the anonymized data of the same category, and verifying validity; and comparing the navigation map information, screening invalid values based on the path change elements, and drawing a cloud driving assistance map. The intelligent driving sensor of mass-produced vehicles is used as a map acquisition data source to generate the vehicle-end intelligent driving map, so that the cost is saved, normal use of the vehicles is not influenced, the vehicle-end intelligent driving map can be used as a normal passenger vehicle at ordinary times, the speed of generating the driving auxiliary map is high, and the driving auxiliary map can be used within 15 minutes after acquisition is completed; the vehicle speed requirement during acquisition is lower than that of the existing scheme, and the efficiency is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of map construction, and in particular to a cloud-based driving assistance map construction method, system, readable storage medium and vehicle. Background Art

[0002] Cloud-based driving assistance maps are a comprehensive map system that integrates high-precision maps, real-time traffic information, cloud computing power and data storage, and is designed to improve the navigation, positioning and decision-making capabilities of autonomous driving systems. This map system uses cloud technology to achieve real-time updates and rapid transmission of map data, thereby improving the adaptability and accuracy of autonomous driving systems.

[0003] The current mainstream method in the industry is to use a dedicated high-precision driving assistance map collection vehicle, which drives to the area where the driving assistance map needs to be collected, drives at the specified speed, and repeats the same road 5 times. The video information and laser point cloud information collected by the sensor are uploaded to the cloud, where the cloud camera collects two-dimensional information and the laser radar collects three-dimensional information. The two are superimposed to form a three-dimensional model. The three-dimensional model is then used to generate a driving assistance map that can be used for advanced driving assistance functions.

[0004] However, maps can only be drawn using expensive dedicated map collection vehicles, and the driving speed is slow (30km / h) when collecting maps. A single road needs to be collected repeatedly 5 to 10 times, resulting in low collection efficiency. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a cloud-based driving assistance map construction method, system, readable storage medium and vehicle, aiming to solve the problem that current maps can only be drawn by special map collection vehicles with high costs, and when collecting maps, the driving speed is slow, a single road needs to be repeatedly collected 5 to 10 times, and the collection efficiency is low.

[0006] To achieve the above object, the present invention proposes a cloud-based driving assistance map construction method.

[0007] Set the vehicle navigation path and obtain anonymized data of the vehicle-side advanced driver assistance function;

[0008] Identify and classify the anonymized data, aggregate and align the anonymized data of the same category, and verify the validity;

[0009] Compare navigation map information, filter invalid values ​​based on path change elements, and draw cloud-based driving assistance maps.

[0010] In summary, according to a cloud-based driving assistance map construction method proposed by the present invention, by importing the cloud-based driving assistance function into a mass-produced car, setting the navigation path through the mass-produced car, and obtaining the anonymized data of the vehicle-side advanced driving assistance function; the system background aggregates and aligns the anonymized data of the same category by identifying and classifying the anonymized data, and verifies the validity; the information in the navigation map is compared with the acquired anonymous information, and invalid values ​​are filtered based on the path change elements, and a cloud-based driving assistance map is drawn. The present invention uses the intelligent driving sensor of the mass-produced car as the source of map acquisition data to generate a car-side intelligent driving map to save costs, while not affecting the normal use of the vehicle. It can be used as a normal passenger car at ordinary times. At the same time, the generation speed of the driving assistance map is fast. After the acquisition is completed, it can be used within 15 minutes. The vehicle speed requirement (80km / h) during acquisition is lower than the existing solution (30km / h), and the efficiency is higher.

[0011] According to one aspect of the above technical solution, the steps of identifying and classifying the anonymized data, aggregating and aligning the anonymized data of the same category, and verifying the effectiveness specifically include:

[0012] After controlling the cloud big data module to obtain the anonymized data in the advanced driver assistance function of the mass-produced vehicle, the anonymized data is identified and classified, clustered based on the identified content, and type labels are established according to the clustering categories, and the clustered anonymized data is matched with the type labels;

[0013] The classification categories include at least lane line information, roadside building information, traffic sign information, traffic signal device information, and street light device information.

[0014] According to one aspect of the above technical solution, after the anonymized data is classified, the clustered anonymized data obtained multiple times on the same navigation path are aligned, and the data validity of the anonymized data on the same path is determined;

[0015] Compare the anonymous data collected multiple times on the same road of the same vehicle, and obtain the element sum comparison information of the road path and the element geographic location comparison information; if the element sum in the anonymous data collected once is different from the alignment standard, the anonymous data collected once is invalid data;

[0016] If the error between the element's geographic location information and the corresponding element's location information in the alignment standard exceeds a threshold, the single-collection anonymized data is invalid data.

[0017] According to one aspect of the above technical solution, the steps of comparing navigation map information, filtering invalid values ​​based on path change elements, and drawing a cloud-based driving assistance map specifically include:

[0018] The original navigation map information is set as a true value, valid anonymized data is obtained, and a cloud-based driving assistance map is constructed based on the valid anonymized data, and the navigation map and the cloud-based driving assistance map are compared.

[0019] According to one aspect of the above technical solution, the process of filtering invalid values ​​based on path change elements is:

[0020] The path change elements at least include a left turn intersection, a right turn intersection, a ramp change point, an intersection, and a zebra crossing, and the path change elements are used as a comparison reference benchmark;

[0021] Obtain the number of comparison reference benchmarks of the navigation map in the preset road and the number of path change elements in the cloud-based driving assistance map, and compare the two numbers. If the numbers are equal, it is determined that the currently used anonymized data is a valid value; if the numbers are not equal, it is determined that the currently used anonymized data is an invalid value.

[0022] According to one aspect of the above technical solution, after filtering out invalid values ​​of the number of path change elements, the deviation of the geographical location of the path change elements in the preset road is determined:

[0023] Obtain the three-dimensional coordinates of the comparison reference benchmark in the original navigation map and the three-dimensional coordinates of the corresponding path change elements in the cloud-based driving assistance map, compare the three-dimensional coordinates of the two, and based on the scale standard, if the deviation difference between the three-dimensional coordinates of the corresponding path change elements in the cloud-based driving assistance map and the three-dimensional coordinates of the comparison reference benchmark in the original navigation map is not greater than the preset deviation value, then determine that the currently used anonymized data is a valid value; if the deviation difference is greater than the preset deviation value, then determine that the currently used anonymized data is an invalid value.

[0024] According to one aspect of the above technical solution, when the deviation point of the corresponding path change element in the cloud-based driving assistance map of the preset road is greater than the deviation number, it is determined that there is an error in the cloud-based driving assistance map of the current preset road, and it is treated as an invalid value;

[0025] Invalid values ​​are filtered out and longitude and latitude coordinate systems are coupled based on valid values ​​until a cloud-based driving assistance map of the vehicle navigation path is generated.

[0026] The present invention further proposes a cloud-based driving assistance map construction system, which is used to implement the above-mentioned cloud-based driving assistance map construction method. The system includes:

[0027] The data acquisition module is used to set the vehicle navigation path and obtain anonymized data of the vehicle-side advanced driver assistance function;

[0028] A valid verification module, used to identify and classify the anonymized data, aggregate and align the anonymized data of the same category, and verify the validity;

[0029] The map drawing module is used to compare navigation map information, filter invalid values ​​based on path change elements, and draw cloud-based driving assistance maps.

[0030] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-mentioned cloud-based driving assistance map construction method.

[0031] The present invention further provides a vehicle, characterized in that the vehicle includes a memory and a processor, wherein:

[0032] The memory is used to store computer programs;

[0033] When the processor is used to execute the computer program stored in the memory, the above-mentioned cloud-based driving assistance map construction method is implemented.

[0034] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a method for constructing a cloud-based driving assistance map in a first embodiment of the present invention;

[0036] Figure 2 A schematic diagram of the structure of a cloud-based driving assistance map construction system in a second embodiment of the present invention;

[0037] Figure 3 This is a schematic structural diagram of a vehicle using a cloud-based driving assistance map construction method in a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0039] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0041] Embodiment 1

[0042] like Figure 1 The flowchart of a cloud-based driving assistance map construction method in the present invention is shown. The cloud-based driving assistance map construction method includes the following steps S01 to S03, wherein:

[0043] S01. Set the vehicle navigation path and obtain anonymized data of the vehicle-side advanced driver assistance function.

[0044] The vehicle-side advanced driver assistance function is introduced into mass-produced vehicles. When the navigation route is set during normal use in the mass-produced vehicles, the cloud big data module obtains the anonymized data of the vehicle-side advanced driver assistance function for building a cloud-based driving assistance map.

[0045] S02. Identify and classify the anonymized data, aggregate and align the anonymized data of the same category, and verify the validity.

[0046] After controlling the cloud big data module to obtain the anonymized data in the advanced driver assistance function of the mass-produced vehicle, the anonymized data is identified and classified, clustered based on the identified content, and type labels are established according to the clustering categories, and the clustered anonymized data is matched with the type labels;

[0047] The classification categories include at least lane line information, roadside building information, traffic sign information, traffic signal device information, and street light device information.

[0048] After the anonymized data is classified, the clustered anonymized data obtained multiple times on the same navigation path are aligned, and the data validity of the anonymized data on the same path is determined;

[0049] Compare the anonymous data collected multiple times on the same road of the same vehicle, and obtain the element sum comparison information of the road path and the element geographic location comparison information; if the element sum in the anonymous data collected once is different from the alignment standard, the anonymous data collected once is invalid data;

[0050] If the error between the element's geographic location information and the corresponding element's location information in the alignment standard exceeds a threshold, the single-collection anonymized data is invalid data. The threshold can be set to 0.5 meters for horizontal distance deviation and 1 meter for vertical distance deviation.

[0051] S03. Compare navigation map information, filter invalid values ​​based on path change elements, and draw a cloud-based driving assistance map.

[0052] The original navigation map information is set as a true value, valid anonymized data is obtained, and a cloud-based driving assistance map is constructed based on the valid anonymized data, and the navigation map and the cloud-based driving assistance map are compared.

[0053] The process of filtering invalid values ​​based on path change elements is as follows:

[0054] The path change elements at least include a left turn intersection, a right turn intersection, a ramp change point, an intersection, and a zebra crossing, and the path change elements are used as a comparison reference benchmark;

[0055] Obtain the number of comparison reference benchmarks of the navigation map in the preset road and the number of path change elements in the cloud-based driving assistance map, and compare the two numbers. If the numbers are equal, it is determined that the currently used anonymized data is a valid value; if the numbers are not equal, it is determined that the currently used anonymized data is an invalid value.

[0056] After filtering out invalid values ​​of the number of path change elements, determine the deviation of the geographical location of the path change elements in the preset road:

[0057] Obtain the three-dimensional coordinates of the comparison reference benchmark in the original navigation map and the three-dimensional coordinates of the corresponding path change elements in the cloud-based driving assistance map, and compare the three-dimensional coordinates of the two. Based on the scale standard, if the deviation difference between the three-dimensional coordinates of the corresponding path change elements in the cloud-based driving assistance map and the three-dimensional coordinates of the comparison reference benchmark in the original navigation map is not greater than the preset deviation value, which can be set to a lateral distance deviation of 0.5 meters and a longitudinal distance deviation of 1 meter, then it is determined that the currently used anonymized data is a valid value; if the deviation difference is greater than the preset deviation value, then it is determined that the currently used anonymized data is an invalid value.

[0058] When the deviation point of the corresponding path change element in the cloud-based driving assistance map of the preset road is greater than the deviation number, which can be set to 3, it is determined that there is an error in the cloud-based driving assistance map of the current preset road and it is treated as an invalid value;

[0059] Invalid values ​​are filtered out and longitude and latitude coordinate systems are coupled based on valid values ​​until a cloud-based driving assistance map of the vehicle navigation path is generated.

[0060] In summary, according to a cloud-based driving assistance map construction method proposed by the present invention, by importing the cloud-based driving assistance function into a mass-produced car, setting the navigation path through the mass-produced car, and obtaining the anonymized data of the vehicle-side advanced driving assistance function; the system background aggregates and aligns the anonymized data of the same category by identifying and classifying the anonymized data, and verifies the validity; the information in the navigation map is compared with the acquired anonymous information, and invalid values ​​are filtered based on the path change elements, and a cloud-based driving assistance map is drawn. The present invention uses the intelligent driving sensor of the mass-produced car as the source of map acquisition data to generate a car-side intelligent driving map to save costs, while not affecting the normal use of the vehicle. It can be used as a normal passenger car at ordinary times. At the same time, the generation speed of the driving assistance map is fast. After the acquisition is completed, it can be used within 15 minutes. The vehicle speed requirement (80km / h) during acquisition is lower than the existing solution (30km / h), and the efficiency is higher.

[0061] Embodiment 2

[0062] Another aspect of the present invention is to provide a cloud-based driving assistance map construction system, please refer to Figure 2 , which is a schematic diagram of the structure of a cloud-based driving assistance map construction system in a second embodiment of the present invention, wherein the cloud-based driving assistance map construction system comprises:

[0063] The data acquisition module 11 is used to set the vehicle navigation path and obtain anonymized data of the vehicle-side advanced driver assistance function;

[0064] A validity verification module 12, used to identify and classify the anonymized data, aggregate and align the anonymized data of the same category, and verify the validity;

[0065] The map drawing module 13 is used to compare navigation map information, filter invalid values ​​based on path change elements, and draw a cloud-based driving assistance map.

[0066] Furthermore, in some optional embodiments, the validity verification module 12 further includes:

[0067] An effective verification unit is used to control the cloud big data module to obtain anonymized data in the advanced driver assistance function of the mass-produced vehicle, identify and classify the anonymized data, cluster the data based on the identified content, establish type labels according to the clustering categories, and correspond the clustered anonymized data to the type labels;

[0068] The classification categories include at least lane line information, roadside building information, traffic sign information, traffic signal device information, and street light device information.

[0069] After the anonymized data is classified, the clustered anonymized data obtained multiple times on the same navigation path are aligned, and the data validity of the anonymized data on the same path is determined;

[0070] Compare the anonymous data collected multiple times on the same road of the same vehicle, and obtain the element sum comparison information of the road path and the element geographic location comparison information; if the element sum in the anonymous data collected once is different from the alignment standard, the anonymous data collected once is invalid data;

[0071] If the error between the element's geographic location information and the corresponding element's location information in the alignment standard exceeds a threshold, the single-collection anonymized data is invalid data.

[0072] Furthermore, in some optional embodiments, the map drawing module 13 further includes:

[0073] The map drawing unit is used to set the original navigation map information as a true value, obtain valid anonymized data, and construct a cloud-based driving assistance map based on the valid anonymized data, and compare the navigation map with the cloud-based driving assistance map.

[0074] The process of filtering invalid values ​​based on path change elements is as follows:

[0075] The path change elements at least include a left turn intersection, a right turn intersection, a ramp change point, an intersection, and a zebra crossing, and the path change elements are used as a comparison reference benchmark;

[0076] Obtain the number of comparison reference benchmarks of the navigation map in the preset road and the number of path change elements in the cloud-based driving assistance map, and compare the two numbers. If the numbers are equal, it is determined that the currently used anonymized data is a valid value; if the numbers are not equal, it is determined that the currently used anonymized data is an invalid value.

[0077] After filtering out invalid values ​​of the number of path change elements, determine the deviation of the geographical location of the path change elements in the preset road:

[0078] Obtain the three-dimensional coordinates of the comparison reference benchmark in the original navigation map and the three-dimensional coordinates of the corresponding path change elements in the cloud-based driving assistance map, compare the three-dimensional coordinates of the two, and based on the scale standard, if the deviation difference between the three-dimensional coordinates of the corresponding path change elements in the cloud-based driving assistance map and the three-dimensional coordinates of the comparison reference benchmark in the original navigation map is not greater than the preset deviation value, then determine that the currently used anonymized data is a valid value; if the deviation difference is greater than the preset deviation value, then determine that the currently used anonymized data is an invalid value.

[0079] When the deviation point of the corresponding path change element in the cloud-based driving assistance map of the preset road is greater than the deviation number, it is determined that there is an error in the cloud-based driving assistance map of the current preset road and it is treated as an invalid value;

[0080] Invalid values ​​are filtered out and longitude and latitude coordinate systems are coupled based on valid values ​​until a cloud-based driving assistance map of the vehicle navigation path is generated.

[0081] In summary, according to a cloud-based driving assistance map construction system proposed by the present invention, by importing cloud-based driving assistance functions into mass-produced vehicles, setting navigation paths through mass-produced vehicles, and obtaining anonymized data of vehicle-side advanced driving assistance functions; the system background aggregates and aligns the anonymized data of the same category by identifying and classifying the anonymized data, and verifies the validity; the information in the navigation map is compared with the acquired anonymous information, and invalid values ​​are filtered based on path change elements, and a cloud-based driving assistance map is drawn. The present invention uses the intelligent driving sensor of mass-produced vehicles as a source of map acquisition data to generate a map for intelligent driving on the vehicle side to save costs, while not affecting the normal use of the vehicle. It can be used as a normal passenger car at ordinary times, and the generation speed of driving assistance maps is fast. After the acquisition is completed, it can be used within 15 minutes. The vehicle speed requirement (80km / h) during acquisition is lower than the existing solution (30km / h), and the efficiency is higher.

[0082] Embodiment 3

[0083] On the other hand, the present invention further proposes a computer-readable storage medium, on which one or more computer programs are stored, and when the program is executed by a processor, the above-mentioned cloud-based driving assistance map construction method is implemented.

[0084] Those skilled in the art will appreciate that the logic or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable storage medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable storage medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0085] More specific examples of computer-readable storage media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in another suitable manner as necessary, and then stored in a computer memory.

[0086] Embodiment 4

[0087] Another aspect of the present invention provides a vehicle, see Figure 3 , shown is a vehicle in the fourth embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the cloud-based driving assistance map construction method as described above is implemented.

[0088] Specifically, the vehicle may be a computer, a vehicle testing device, etc. The processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip in some embodiments, and is used to run the program code or process data stored in the memory 20, such as executing an access restriction program.

[0089] The memory 20 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 20 may be an internal storage unit of the vehicle, such as a hard disk of the vehicle. In other embodiments, the memory 20 may also be an external storage device of the vehicle, such as a plug-in hard disk equipped on the vehicle, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 20 may also include both an internal storage unit of the vehicle and an external storage device. The memory 20 may be used not only to store application software and various types of data installed in the vehicle, but also to temporarily store data that has been output or is to be output.

[0090] It should be pointed out that Figure 3The structure shown does not constitute a limitation on the vehicle. In other embodiments, the vehicle may include Figure 3 Fewer or more components may be shown, or certain components may be combined, or arranged differently.

[0091] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0092] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0093] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A cloud-based driving assistance map construction method, characterized in that: Set the vehicle navigation path and obtain anonymized data of the vehicle-side advanced driver assistance function; Identify and classify the anonymized data, aggregate and align the anonymized data of the same category, and verify the validity; Compare navigation map information, filter invalid values ​​based on path change elements, and draw cloud-based driving assistance maps.

2. The cloud-based driving assistance map construction method according to claim 1, characterized in that: The steps of identifying and classifying the anonymized data, aggregating and aligning the anonymized data of the same category, and verifying the effectiveness specifically include: After controlling the cloud big data module to obtain the anonymized data in the advanced driver assistance function of the mass-produced vehicle, the anonymized data is identified and classified, clustered based on the identified content, and type labels are established according to the clustering categories, and the clustered anonymized data is matched with the type labels; The classification categories include at least lane line information, roadside building information, traffic sign information, traffic signal device information, and street light device information.

3. The cloud-based driving assistance map construction method according to claim 2, characterized in that: After the anonymized data is classified, the clustered anonymized data obtained multiple times on the same navigation path are aligned, and the data validity of the anonymized data on the same path is determined; Compare the anonymous data collected multiple times on the same road of the same vehicle, and obtain the element sum comparison information of the road path and the element geographic location comparison information; if the element sum in the anonymous data collected once is different from the alignment standard, the anonymous data collected once is invalid data; If the error between the element's geographic location information and the corresponding element's location information in the alignment standard exceeds a threshold, the single-collection anonymized data is invalid data.

4. The cloud-based driving assistance map construction method according to claim 1, characterized in that: The steps of comparing navigation map information, filtering invalid values ​​based on path change elements, and drawing a cloud-based driving assistance map specifically include: The original navigation map information is set as a true value, valid anonymized data is obtained, and a cloud-based driving assistance map is constructed based on the valid anonymized data, and the navigation map and the cloud-based driving assistance map are compared.

5. The cloud-based driving assistance map construction method according to claim 4, characterized in that: The process of filtering invalid values ​​based on path change elements is as follows: The path change elements at least include a left turn intersection, a right turn intersection, a ramp change point, an intersection, and a zebra crossing, and the path change elements are used as a comparison reference benchmark; Obtain the number of reference benchmarks of the navigation map in the preset road and the number of path change elements in the cloud-based driving assistance map, and compare the two numbers. If the numbers are equal, it is determined that the currently used anonymized data is a valid value; If the numbers are not equal, the currently used anonymized data is determined to be an invalid value.

6. The cloud-based driving assistance map construction method according to claim 5, characterized in that: After filtering out invalid values ​​of the number of path change elements, determine the deviation of the geographical location of the path change elements in the preset road: Obtain the three-dimensional coordinates of the comparison reference benchmark in the original navigation map and the three-dimensional coordinates of the corresponding path change element in the cloud-based driving assistance map, compare the three-dimensional coordinates of the two, and based on the scale standard, if the deviation difference between the three-dimensional coordinates of the corresponding path change element in the cloud-based driving assistance map and the three-dimensional coordinates of the comparison reference benchmark in the original navigation map is not greater than a preset deviation value, then determine that the currently used anonymized data is a valid value; If the deviation difference is greater than the preset deviation value, the currently used anonymized data is determined to be an invalid value.

7. The cloud-based driving assistance map construction method according to claim 6, characterized in that: When the deviation point of the corresponding path change element in the cloud-based driving assistance map of the preset road is greater than the deviation number, it is determined that there is an error in the cloud-based driving assistance map of the current preset road and it is treated as an invalid value; Invalid values ​​are filtered out and longitude and latitude coordinate systems are coupled based on valid values ​​until a cloud-based driving assistance map of the vehicle navigation path is generated.

8. A cloud-based driving assistance map construction system, the cloud-based driving assistance map construction system is used to implement the cloud-based driving assistance map construction method according to any one of claims 1 to 7, the system comprising: The data acquisition module is used to set the vehicle navigation path and obtain anonymized data of the vehicle-side advanced driver assistance function; A valid verification module, used to identify and classify the anonymized data, aggregate and align the anonymized data of the same category, and verify the validity; The map drawing module is used to compare navigation map information, filter invalid values ​​based on path change elements, and draw cloud-based driving assistance maps.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the cloud-based driving assistance map construction method as described in any one of claims 1 to 6 is implemented.

10. A vehicle, characterized in that: The vehicle comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, the cloud-based driving assistance map construction method described in any one of claims 1-7 is implemented.

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