Map quality evaluation method and apparatus, and device and storage medium

Abstract

Provided in the embodiments of the present application are a map quality evaluation method and apparatus, and a device and a storage medium. The method comprises: acquiring a map on the basis of perception information collected by means of a perception device; and on the basis of the occurrence of a score deduction scenario in the map, determining a score of the map, wherein the score of the map is used for representing the quality of the map, and the occurrence of the score deduction scenario is inversely proportional to the quality of the map. In the embodiments of the present application, after studying and obtaining a map, a memory navigation system performs quality evaluation on the map, obtains the score of the map by means of the quality evaluation, and displays the score of the map to a driver, such that the driver can determine, by means of the score of the map, whether to use the map, thereby ensuring that each map used by the memory navigation system is a map of a relatively high quality, ensuring the use effect of a memory navigation function, and improving the user experience.

Description

Map quality evaluation method, device, equipment and storage medium

[0001] The present application claims priority to the Chinese patent application No. 202411050385.2, filed on August 01, 2024, and entitled "Map quality evaluation method, device, equipment and storage medium", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of intelligent driving, in particular to a map quality evaluation method, device, equipment and storage medium. BACKGROUND

[0003] With the development of technology, intelligent driving technology is gradually perfected, and users' demand for automatic driving is also gradually increasing. In order to meet users' demand for automatic driving, more and more cars are equipped with memory navigation function. The memory navigation function is an enhanced driving assistance function based on driver learning. After the driver selects a route that has completed learning and confirms the use of the memory navigation function, the memory navigation system will control the vehicle to travel from the starting point of the route to the end point of the route under the monitoring of the driver. During this period, the car may need to realize various auxiliary driving functions such as following driving, passing through intersections, responding to queuing and active lane changing.

[0004] As can be seen from the above, the memory navigation function needs to be learned before use, that is, to obtain a route for the driver to select. Specifically, in the process of driving the vehicle by the driver, the memory navigation system models the surrounding environment in real time through online perception, thereby completing map learning.

[0005] However, in the related art, when the memory navigation function is learning the map, there are situations such as occlusion and poor lighting, which may cause fluctuations in the quality of the generated map, thereby affecting the use effect of the memory navigation function and the user experience.

[0006] It should be pointed out that the information disclosed in the background section of the present application is only intended to deepen the understanding of the general background of the present application, and should not be regarded as acknowledging or implying in any form that the information constitutes prior art known to those skilled in the art. SUMMARY

[0007] Therefore, the present application provides a map quality evaluation method, device, equipment and storage medium to solve the problem that when the memory navigation function is learning the map, there are situations such as occlusion and poor lighting, which may cause fluctuations in the quality of the generated map, thereby affecting the use effect of the memory navigation function and the user experience.

[0008] In a first aspect, the embodiments of the present application provide a map quality evaluation method, comprising:

[0009] obtaining a map according to the perception information collected by the perception device;

[0010] determining a score of the map according to the occurrence of the deduction scene in the map, wherein the score of the map is used to represent the quality of the map, and the occurrence of the deduction scene is inversely proportional to the quality of the map.

[0011] In the embodiments of the present application, when the memory navigation system learns and obtains a map, the quality of the map is evaluated, the score of the map is obtained through the quality evaluation, and the score of the map is displayed to the driver. The driver can determine whether to use the map through the score of the map, so as to ensure that each map used by the memory navigation system is a map with high quality, ensure the use effect of the memory navigation function, and improve the user experience.

[0012] In a possible implementation, the determining of the score of the map according to the occurrence of the deduction scene in the map comprises:

[0013] determining the score of the map according to the blocking condition of the traffic marking in the map.

[0014] In the embodiments of the present application, when the blocking condition of the traffic marking in the map occurs, the map quality evaluation device adjusts the score of the map according to the preset map quality evaluation rule. It can be understood that if the blocking condition of the traffic marking in the map exists, it indicates that the quality of the map is not high enough. At this time, the score of the map is adjusted according to the preset map quality evaluation rule, so that the score of the map is more real and reliable, and the driver can fully understand the quality of the map.

[0015] In a possible implementation, the determining of the score of the map according to the blocking condition of the traffic marking in the map comprises:

[0016] if any traffic marking in the map is blocked, the score of the first influencing factor is increased by a corresponding value, and the score of the first influencing factor is used to represent the blocking condition of the plurality of traffic markings in the map;

[0017] the total score is reduced by the score of the first influencing factor to obtain the score of the map.

[0018] In the embodiments of the present application, when the blocking condition of the traffic marking in the map exists, the score of the first influencing factor is increased by a corresponding value, and the score of the map is obtained by reducing the score of the first influencing factor from the total score. It can be understood that the more the traffic marking is blocked, the higher the score of the first influencing factor is, and the lower the score of the map is, so as to determine an accurate map score.

[0019] In a possible implementation, the score of the map is determined according to the situation of the score deduction scenario in the map.

[0020] The score of the map is determined according to the situation of the landmark sparsity in the map.

[0021] In the embodiment of the present application, when the situation of landmark sparsity occurs in the map, the map quality evaluation device adjusts the score of the map according to the preset map quality evaluation rule. It can be understood that if the situation of landmark sparsity exists in the map, it indicates that the quality of the map is not high enough. At this time, the score of the map is adjusted according to the preset map quality evaluation rule, which can make the score of the map more real and reliable, and enable the driver to fully understand the quality of the map.

[0022] In a possible implementation, the score of the map is determined according to the situation of the landmark sparsity in the map.

[0023] If the landmarks are sparse in any road segment in the map, the score of the second influence factor is increased by a corresponding value, the score of the second influence factor being used to represent the distribution of the landmarks in the plurality of road segments in the map, the road segment being a road segment with a length greater than or equal to a preset length threshold.

[0024] The score of the map is obtained by subtracting the score of the second influence factor from the preset total score.

[0025] In the embodiment of the present application, when the situation of landmark sparsity exists in the map, the score of the second influence factor is increased by a corresponding value, and the score of the map is obtained by subtracting the score of the second influence factor from the total score. It can be understood that the more road segments with landmark sparsity, the higher the score of the second influence factor, and the lower the score of the map, so as to determine an accurate map score.

[0026] In a possible implementation, the score of the map is determined according to the situation of the score deduction scenario in the map.

[0027] The score of the map is determined according to the situation of the signal light recognition error in the map.

[0028] In the embodiment of the present application, when the situation of signal light recognition error occurs in the map, the map quality evaluation device adjusts the score of the map according to the preset map quality evaluation rule. It can be understood that if the situation of signal light recognition error occurs in the map, it indicates that the quality of the map is not high enough. At this time, the score of the map is adjusted according to the preset map quality evaluation rule, which can make the score of the map more real and reliable, and enable the driver to fully understand the quality of the map.

[0029] In a possible implementation, the score of the map is determined according to the signal lamp identification error in the map, including:

[0030] If any signal lamp identification error exists in the map, the score of the third influence factor is increased by a corresponding value, and the score of the third influence factor is used to represent the identification of the plurality of signal lamps in the map.

[0031] The total score is reduced by the score of the third influence factor to obtain the score of the map.

[0032] In the embodiments of the present application, when the signal lamp identification error exists in the map, the score of the third influence factor is increased by a corresponding value, and the score of the map is obtained by reducing the total score by the score of the third influence factor. It can be understood that the more the signal lamp identification errors, the higher the score of the third influence factor, and the lower the score of the map, so as to determine an accurate map score.

[0033] In a possible implementation, the score of the map is determined according to the score deduction scene in the map, including:

[0034] The score of the map is determined according to the specific scene in the map, and the specific scene is a scene with a traffic risk.

[0035] In the embodiments of the present application, when the specific scene exists in the map, the map quality evaluation device adjusts the score of the map according to the preset map quality evaluation rule. It can be understood that if the specific scene exists in the map, the situation that the driver needs to take over is more, that is, the quality of the map is not high enough, and at this time, the score of the map is adjusted according to the preset map quality evaluation rule, so that the score of the map is more real and reliable, and the driver can fully understand the quality of the map.

[0036] In a possible implementation, the score of the map is determined according to the specific scene in the map, including:

[0037] If any specific scene exists in the map, the score of the fourth influence factor is increased by a corresponding value, and the score of the fourth influence factor is used to represent the specific scene in the map.

[0038] The total score is reduced by the score of the fourth influence factor to obtain the score of the map.

[0039] In the embodiments of the present application, when the specific scene exists in the map, the score of the fourth influence factor is increased by a corresponding value, and the score of the map is obtained by reducing the total score by the score of the fourth influence factor. It can be understood that the more the specific scenes, the higher the score of the fourth influence factor, and the lower the score of the map, so as to determine an accurate map score.

[0040] In a second aspect, an embodiment of the present application provides a map quality evaluation device, comprising:

[0041] a map obtaining module, configured to obtain a map according to perception information collected by a perception device;

[0042] a score determining module, configured to determine a score of the map according to a situation of a deduction scene in the map, the score of the map being used to represent a quality of the map, and the situation of the deduction scene being inversely proportional to the quality of the map.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, characterized in that comprising:

[0044] a processor;

[0045] a memory;

[0046] and a computer program, wherein the computer program is stored in the memory, and the computer program comprises instructions, which, when executed by the processor, cause the electronic device to perform the method in any one of the first aspect.

[0047] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, characterized in that comprising a stored program, wherein the program, when executed, controls a device where the computer readable storage medium is located to perform the method in any one of the first aspect.

[0048] It can be understood that the map quality evaluation device provided in the second aspect, the electronic device provided in the third aspect, and the computer readable storage medium provided in the fourth aspect are used to perform the map quality evaluation method provided in the present application. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be repeated here.

[0049] In the embodiments of the present application, when a memory navigation system learns and obtains a map, the quality of the map is evaluated, the score of the map is obtained through the quality evaluation, and the score of the map is displayed to a driver. The driver can determine whether to use the map through the score of the map, so that it is ensured that each map used by the memory navigation system is a map with high quality, the use effect of the memory navigation function is ensured, and the user experience is improved. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative labor.

[0051] FIG. 1 is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0052] FIG. 2 is a schematic diagram of a flow of a map quality evaluation method provided by an embodiment of the present application;

[0053] FIG. 3 is a schematic diagram of a structure of a map quality evaluation device provided by an embodiment of the present application;

[0054] FIG. 4 is a schematic diagram of a structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to better understand the technical solutions of the present application, the embodiments of the present application will be described in detail below with reference to the drawings.

[0056] It should be clear that the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0057] The terms used in the embodiments of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0058] It should be understood that the term "and / or" used herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0059] In order to facilitate understanding, the specific application scenario will be described first by way of example.

[0060] Referring to FIG. 1, an application scenario provided by an embodiment of the present application is shown. As shown in FIG. 1, the application scenario shows a vehicle 101, a traffic marking 102, and a landmark 103, wherein the traffic marking 102 includes a lane line 1021 and a guide arrow 1022. Because the vehicle 101 configured with the memory piloting function needs to be driven by the driver to learn the map before using the memory piloting function, and the obtained map route is displayed to the driver for selection. As shown in FIG. 1, when the driver drives the vehicle 101 on the lane, the vehicle 101 perceives the surrounding environment information to obtain the perception information, wherein the perception information includes but is not limited to the traffic marking 102 and the landmark 103, and the memory piloting system can obtain the map of the route according to the perception information.

[0061] However, because the vehicle 101 obtains the perception information through the perception device, in the related art, when the memory piloting function learns the map, there are situations such as occlusion and poor lighting, and the quality of the generated map fluctuates, thereby affecting the use effect and user experience of the memory piloting function.

[0062] To solve the above problems, an embodiment of the present application provides a map quality evaluation method. When the traffic marking is occluded in the map, the map quality evaluation device adjusts the score of the map according to the preset map quality evaluation rule. It can be understood that if the traffic marking is occluded in the map, the quality of the map is not high. At this time, according to the preset map quality evaluation rule, the score of the map is adjusted, which can make the score of the map more real and reliable, and make the driver fully understand the quality of the map. When the driver uses the memory piloting function, when the vehicle drives to the position with low score in the map, the memory piloting system will prompt the driver to drive carefully in advance, so as to facilitate the driver to take over the vehicle, thereby improving the safety of the memory piloting function. In the following, the embodiment is described in detail in combination with the drawings.

[0063] Referring to FIG. 2, a flowchart of a map quality evaluation method provided by an embodiment of the present application is shown. The method can be applied to the application scenario shown in FIG. 1. As shown in FIG. 2, the method mainly includes the following steps.

[0064] Step S201: Obtain a map according to perception information collected by a perception device.

[0065] Specifically, the map quality evaluation device obtains a map according to the perception information collected by the perception device of the vehicle, and the map is the map used by the memory piloting function. In a possible implementation manner, the perception device can be a camera, a radar, a laser radar, and the like, which can collect surrounding environment information. The map quality evaluation device models the surrounding environment in real time according to the perception device, thereby completing the learning of the map.

[0066] Step S202: determining the score of the map according to the occurrence of the minus-scene in the map.

[0067] Specifically, the map quality evaluation device determines the score of the map according to the occurrence of the minus-scene in the map, wherein the score of the map is used to represent the quality of the map, and the occurrence of the minus-scene is inversely proportional to the quality of the map.

[0068] In a possible implementation, the minus-scene is that the traffic marking in the map is blocked, so the map quality evaluation device can determine the score of the map according to the blocking situation of the traffic marking in the map. The traffic marking includes but is not limited to lane line, stop line, pedestrian crossing and ground arrow.

[0069] Specifically, if any traffic marking in the map is blocked, the score of the first influencing factor is increased by a corresponding value, wherein the score of the first influencing factor is used to represent the blocking situation of the plurality of traffic markings in the map; the preset total score is reduced by the score of the first influencing factor to obtain the score of the map. It can be understood that if there is 1 traffic marking blocked in the map, the score of the first influencing factor is increased by x; similarly, if there are 2 traffic markings blocked in the map, the score of the first influencing factor is increased by 2x; and so on, if there are n traffic markings blocked in the map, the score of the first influencing factor is increased by nx. In the embodiment of the present application, the preset total score is 10 points, and the score of the first influencing factor is [number of traffic markings blocked / length of route (km)]x5, wherein the length of the route is the length of the route in the map learned by the map quality evaluation device this time, and the length of the route is in units of km, and 5 is a calibration quantity. By setting the calibration quantity, the score of the first influencing factor reaches the expected score faster, that is, the difference between the score of the first influencing factor and the total score is reduced, avoiding the situation that the score of the map is too high, so that the score difference between the maps is too small.

[0070] In a possible implementation, the minus-scene is that the markers in the map are sparse, so the map quality evaluation device can determine the score of the map according to the sparsity of the markers in the map. The markers include but are not limited to buildings, bridges, trees and road signs.

[0071] Specifically, if the markers in any road segment in the map are sparse, the score of the second influence factor is increased by a corresponding value, wherein the score of the second influence factor is used to represent the distribution of the markers in the plurality of road segments in the map, and the road segment is a road segment with a length greater than or equal to a preset length threshold; the preset total score is reduced by the score of the second influence factor to obtain the score of the map. It can be understood that if the markers in 1 km in the map are sparse, the score of the second influence factor is increased by y; similarly, if the markers in 2 km in the map are sparse, the score of the second influence factor is increased by 2y; and so on, if the markers in n km in the map are sparse, the score of the second influence factor is increased by ny. Of course, the distance of the sparse markers in the embodiments of the present application and the increase amount of the score of the second influence factor are only an exemplary description, and a person skilled in the art can make changes according to actual needs, and the embodiments of the present application do not make specific limitations. In the embodiments of the present application, the preset total score is 10 points, and the score of the second influence factor is [distance of sparse markers / length of route (km)]x5, wherein the length of the route is the length of the route in the map learned by the map quality evaluation device this time, and the length of the route is in km, and 5 is a calibration amount, so that the score of the second influence factor reaches the expected score faster, that is, the difference between the score of the second influence factor and the total score is reduced, avoiding the case that the score of the map is too high, so that the score difference between the maps is too small.

[0072] In a possible implementation, the score reduction scenario is a signal light recognition error, so the map quality evaluation device can determine the score of the map according to the signal light recognition error in the map. Wherein, the signal light is usually a traffic light, of course, a person skilled in the art can set other signal lights according to actual needs, and the embodiments of the present application do not make specific limitations. For example, if the map quality evaluation device identifies the front traffic light as “left turn green light, straight red light”, but the driver drives the vehicle straight, the map quality evaluation device can determine that the traffic light recognition is wrong.

[0073] Specifically, if any traffic light in the map is misrecognized, the score of the third influencing factor is increased by a corresponding value, the score of the third influencing factor is used to represent the recognition of a plurality of traffic lights in the map, and the preset total score is reduced by the score of the third influencing factor to obtain the score of the map. It can be understood that if there is 1 traffic light misrecognized in the map, the score of the third influencing factor is added by z; similarly, if there are 2 traffic light misrecognized in the map, the score of the third influencing factor is added by 2z; and so on, if there are n traffic light misrecognized in the map, the score of the third influencing factor is added by nz. In the embodiment of the present application, the preset total score is 10 points, and the score of the third influencing factor is [the number of misrecognized traffic lights / the length of the route (km)]x5, wherein the length of the route is the length of the route in the map learned by the map quality evaluation device this time, the length of the route is in units of km, and 5 is a calibration quantity. By setting the calibration quantity, the score of the third influencing factor reaches the expected score faster, that is, the difference between the score of the third influencing factor and the total score is reduced, and the situation that the score of the map is too high to make the score difference between maps too small is avoided.

[0074] In a possible implementation, the score deduction scene is a specific scene, so the map quality evaluation device can determine the score of the map according to the specific scene in the map. The specific scene is a scene with traffic risk, that is, a scene that may need the driver to take over the vehicle to ensure smooth passing. In the embodiment of the present application, the specific scene includes a U-turn intersection, a toll station, a gate, a continuous lane change, a large-curvature curve, and a merging and splitting, etc.

[0075] Specifically, if any specific scene appears in the map, the score of the fourth influencing factor is increased by a corresponding value, and the score of the fourth influencing factor is used to represent the occurrence of a plurality of specific scenes in the map. The preset total score is reduced by the score of the fourth influencing factor to obtain the score of the map. It can be understood that if there is 1 specific scene in the map, the score of the fourth influencing factor is added by q; similarly, if there are 2 specific scenes in the map, the score of the fourth influencing factor is added by 2q; and so on, if there are n specific scenes in the map, the score of the fourth influencing factor is added by nq. In the embodiment of the present application, the preset total score is 10 points, and the score of the fourth influencing factor is [the number of specific scenes appearing / the length of the route (km)]x5, wherein the length of the route is the length of the route in the map learned by the map quality evaluation device this time, the length of the route is in units of km, and 5 is a calibration quantity. By setting the calibration quantity, the score of the fourth influencing factor reaches the expected score faster, that is, the difference between the score of the fourth influencing factor and the total score is reduced, and the situation that the score of the map is too high to make the score difference between maps too small is avoided.

[0076] In addition, in a possible implementation, the demerit scenarios in the map include traffic marking being blocked, sparse markers, signal lamp recognition error, and specific scenarios, that is, when the traffic marking being blocked appears in the map, the map quality evaluation device adaptively increases the score of the first influence factor; when the sparse marker road appears in the map, the map quality evaluation device adaptively increases the score of the second influence factor; when the signal lamp recognition error appears in the map, the map quality evaluation device adaptively increases the score of the third influence factor; and when the specific scenario appears in the map, the map quality evaluation device adaptively increases the score of the fourth influence factor.

[0077] For example, the map quality evaluation device obtains a route length of 20 km in the map, wherein the traffic marking being blocked exists at 11.1 km and 15.5 km, the sparse marker exists within a distance of 2 km, the signal lamp recognition error exists at 19.0 km, the U-turn intersection exists at 19.5 km, and the gate exists at 19.9 km. Then, the map score is calculated according to the following rules: map score = 10 - [number of times of traffic marking being blocked / route length (km)] × 5 - [distance of sparse marker / route length (km)] × 5 - [number of times of signal lamp recognition error / route length (km)] × 5 - [number of times of specific scenario / route length (km)] × 5 = 10 - (2 / 20) × 5 - (2 / 20) × 5 - (1 / 20) × 5 - (2 / 20) × 5 = 8.25. The score 8.25 is output for the driver to determine whether to use the map, and if the driver uses the map, the driver is prompted to drive carefully or take over the vehicle at 11.1 km, 15.5 km, 19.0 km, 19.5 km, and the sparse marker road when the driver uses the memory navigation function, thereby improving the use safety of the memory navigation function.

[0078] In conclusion, in the embodiment of the present application, after the memory navigation system learns and obtains a map, the quality of the map is evaluated, the score of the map is obtained through the quality evaluation, and the score of the map is displayed to the driver, so that the driver can determine whether to use the map according to the score of the map, thereby ensuring that each map used by the memory navigation system is a high-quality map, ensuring the use effect of the memory navigation function, and improving the user experience.

[0079] Corresponding to the above embodiment, the present application further provides a map quality evaluation device.

[0080] Referring to FIG. 3, a structural schematic diagram of a map quality evaluation device provided by an embodiment of the present application is shown. As shown in FIG. 3, the map quality evaluation device can include a map obtaining module 301 and a score determining module 302. These components communicate through one or more buses, and those skilled in the art can understand that the structure of the control device shown in the figure does not constitute a limitation on the embodiments of the present application. It can be a bus structure, or a star structure, and can include more or fewer components than shown in the figure, or combine some components, or different component arrangements.

[0081] The map obtaining module 301 is configured to obtain a map according to perception information collected by a perception device.

[0082] The score determining module 301 is configured to determine a score of the map according to a deduction scene occurrence in the map, wherein the score of the map is used to represent the quality of the map, and the deduction scene occurrence is inversely proportional to the quality of the map.

[0083] Corresponding to the above-mentioned embodiments, the present application further provides an electronic device.

[0084] Referring to FIG. 4, a structural schematic diagram of an electronic device provided by an embodiment of the present application is shown. As shown in FIG. 4, the electronic device 400 can include a processor 401, a memory 402, and a communication unit 403. These components communicate through one or more buses, and those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiments of the present application. It can be a bus structure, or a star structure, and can include more or fewer components than shown in the figure, or combine some components, or different component arrangements.

[0085] The communication unit 403 is configured to establish a communication channel, so that the electronic device can communicate with other devices. It receives user data sent by other devices or sends user data to other devices.

[0086] The processor 401 is the control center of the electronic device, and connects various parts of the electronic device through various interfaces and lines, and executes various functions of the electronic device and / or processes data by running or executing software programs, instructions and / or modules stored in the memory 402 and calling data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, can be composed of a single packaged IC, or can be composed of multiple packaged ICs connected together. For example, the processor 401 can only include a central processing unit (CPU). In the embodiments of the present application, the CPU can be a single operation core or can include multiple operation cores.

[0087] The memory 402 is used to store the execution instructions of the processor 401, and the memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0088] When the execution instructions in the memory 402 are executed by the processor 401, the electronic device 400 can execute part or all of the steps in the embodiment shown in FIG. 1.

[0089] In a specific implementation, the embodiments of the present application also provide a computer storage medium, wherein the computer storage medium can store a program, and the program can include part or all of the steps in the embodiments of the simulation scene generation method provided by the embodiments of the present application when executed. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0090] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0091] Those of ordinary skill in the art can appreciate that the units and algorithm steps described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, and a combination of electronic hardware and computer software. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0093] In several embodiments provided in the present application, any function realized in the form of a software function unit and sold or used as an independent product can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0094] The same and similar parts among the various embodiments in the specification can be referred to each other. In particular, for the device embodiments and the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the description in the method embodiments.

Claims

1. A map quality assessment method characterized by comprising: The method comprises: obtaining a map according to perception information collected by a perception device; determining a score of the map according to a minus-scene occurrence in the map, the score of the map being used to represent a quality of the map, and the minus-scene occurrence being inversely proportional to the quality of the map.

2. The method of claim 1, wherein, The determining of the score of the map according to the minus-scene occurrence in the map comprises: determining the score of the map according to a traffic marking occlusion in the map.

3. The method of claim 2, wherein, The determining of the score of the map according to the traffic marking occlusion in the map comprises: if any traffic marking in the map is occluded, increasing a score of a first influencing factor by a corresponding value, the score of the first influencing factor being used to represent the traffic marking occlusion in the map; obtaining the score of the map by subtracting the score of the first influencing factor from a preset total score.

4. The method of claim 1, wherein, The determining of the score of the map according to the minus-scene occurrence in the map comprises: determining the score of the map according to a landmark sparsity in the map.

5. The method of claim 4, wherein, The determining of the score of the map according to the landmark sparsity in the map comprises: if any landmark in a road segment in the map is sparse, increasing a score of a second influencing factor by a corresponding value, the score of the second influencing factor being used to represent a distribution of landmarks in the map, the road segment being a road segment with a length greater than or equal to a preset length threshold; obtaining the score of the map by subtracting the score of the second influencing factor from a preset total score.

6. The method of claim 1, wherein, The determining of the score of the map according to the minus-scene occurrence in the map comprises: determining the score of the map according to a traffic signal recognition error in the map.

7. The method of claim 6, wherein, The determining of the score of the map according to the traffic signal recognition error in the map comprises: if any traffic signal in the map is recognized incorrectly, increasing a score of a third influencing factor by a corresponding value, the score of the third influencing factor being used to represent a recognition of traffic signals in the map; obtaining the score of the map by subtracting the score of the third influencing factor from a preset total score.

8. The method of claim 1, wherein, The determining of the score of the map according to the minus-scene occurrence in the map comprises: determining the score of the map according to a specific scene occurrence in the map, the specific scene being a scene with a traffic risk.

9. The method of claim 8, wherein, The determining of the score of the map according to the specific scene occurrence in the map comprises: if any specific scene in the map occurs, increasing a score of a fourth influencing factor by a corresponding value, the score of the fourth influencing factor being used to represent the specific scene occurrence in the map; obtaining the score of the map by subtracting the score of the fourth influencing factor from a preset total score.

10. A map quality evaluation device characterized by comprising: The method comprises: obtaining a map according to perception information collected by a perception device; determining a score of the map according to a minus-scene occurrence in the map, the score of the map being used to represent a quality of the map, and the minus-scene occurrence being inversely proportional to the quality of the map.

11. An electronic device, comprising: The method comprises: a processor; a memory; and a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, which, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored program, wherein the program controls the device where the computer readable storage medium is located to perform the method of any one of claims 1 to 9 when the program is running.

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