CN115355919A - Precision detection method and device of vehicle positioning algorithm, computing equipment and medium - Google Patents

Abstract

The application provides a precision detection method, a precision detection device, computing equipment and a medium for a vehicle positioning algorithm. According to the method and the device, the test data packets including the driving data acquired in the driving process of the vehicle are acquired, so that a first positioning information of the vehicle is determined based on the driving data included in each test data packet and a map established in advance, a second positioning information of the vehicle is determined based on each test data packet through a positioning algorithm to be detected, a plurality of first positioning information and a plurality of second positioning information are obtained, and a positioning error information is determined based on each first positioning information and the corresponding second positioning information, so that the positioning accuracy of the positioning algorithm to be detected can be determined to meet the positioning requirement under the condition that the positioning error information corresponding to the plurality of test data packets meets the set condition, the accuracy of the positioning algorithm of the automatic driving vehicle is evaluated, and the accuracy of the positioning position of the automatic driving vehicle is guaranteed.

Description

Accuracy detection method, device, computing equipment and medium of vehicle positioning algorithm

technical field

This specification relates to the technical field of automatic driving, and in particular to a method, device, computing device and medium for detecting the accuracy of a vehicle positioning algorithm.

Background technique

Today, with the rapid development of technologies such as mobile communications, the Internet of Things, and computer vision, new technologies such as autonomous driving have also ushered in rapid development, and autonomous vehicles are more and more widely used in real life.

Among them, the first step for an autonomous driving vehicle is to know its own position. In a complex and changeable urban environment, the positioning accuracy of an autonomous driving vehicle requires an error of no more than 10 cm. If the deviation of the positioning position is too large, it will make the The deviation of the driving video determined by the software algorithm based on the positioning position is too large, which makes it easy for the self-driving vehicle to scratch and collide with the surrounding road facilities during driving. Therefore, in autonomous driving technology, the accuracy of positioning is of great significance regardless of the hardware level or software level considerations. Therefore, there is an urgent need for an accuracy detection method of a vehicle positioning algorithm to evaluate the accuracy of the positioning algorithm of an autonomous vehicle, so as to ensure the accuracy of the positioning position of the autonomous vehicle.

Contents of the invention

In order to overcome the problems existing in the related technologies, the present application provides a method, device, computing device and medium for detecting the accuracy of a vehicle positioning algorithm.

According to the first aspect of the embodiments of the present application, a method for detecting the accuracy of a vehicle positioning algorithm is provided, the method comprising:

Obtain multiple test data packets, the test data packets include driving data collected during vehicle driving;

Based on the driving data included in each test data packet and the pre-established map, determine a first location information of the vehicle, and determine a second location information of the vehicle based on each test data packet through the location algorithm to be detected;

determining a piece of positioning error information based on each first positioning information and corresponding second positioning information;

When the positioning error information corresponding to the multiple test data packets meets the set condition, it is determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement.

In some embodiments of the present application, a plurality of test data packets are prepared in advance, and the test data packets are marked with a positioning problem label and an environment label, the positioning problem label is used to indicate the cause of the vehicle positioning error, and the environment label is used to indicate The environmental conditions during the driving process of the vehicle, the preparation process of multiple test data packages includes:

Acquiring driving data collected by at least one vehicle during driving;

Based on the time stamp of the driving data, the collected driving data is segmented to obtain multiple candidate data packets;

Based on the visual display results of each candidate data packet, obtain the positioning problem label and environment label of each candidate data packet;

Candidate data packets whose positioning problem labels and environment labels meet the target conditions are used as test data packets.

In some embodiments of the present application, the driving data includes at least initial positioning information, point cloud data and wheel speed data;

Based on the driving data included in each test data package and the pre-established map, a first positioning information of the vehicle is determined, including:

For any test data packet, based on the initial positioning information and wheel speed data in the driving data included in the test data packet, determine the positioning area of the vehicle in the map;

From the candidate point cloud data corresponding to each candidate positioning information in the positioning area, the candidate point cloud data matching the point cloud data is determined, and the candidate positioning information corresponding to the candidate point cloud data is determined as the first positioning information.

In some embodiments of the present application, the positioning information includes position information and attitude information, the position information includes the position coordinates of the vehicle in the set coordinate system, and the attitude information is used to indicate the distance between the position of the vehicle and the coordinate axes of the set coordinate system The angular relationship of , the positioning error information includes relative pose error and absolute trajectory error;

Based on each first positioning information and corresponding second positioning information, determine a positioning error information, including:

For any first positioning information and the second positioning information corresponding to the first positioning information, the absolute trajectory error is determined based on the first position information included in the first positioning information and the second position information included in the second positioning information, based on The first pose information included in the first positioning information and the second pose information included in the second positioning information determine a relative pose error.

In some embodiments of the present application, when the positioning error information corresponding to multiple test data packets meets the set conditions, determining that the positioning accuracy of the positioning algorithm to be detected meets the positioning requirements, including:

When the positioning error information corresponding to multiple test data packets is less than the set error threshold, it is determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement.

In some embodiments of the present application, after determining a positioning error information based on each first positioning information and corresponding second positioning information, the method further includes:

When the positioning error information corresponding to multiple test data packets does not meet the set condition, it is determined that the positioning accuracy of the positioning algorithm to be tested does not meet the positioning requirement.

In some embodiments of the present application, the method also includes:

Outputting test result information, the test result information is used to indicate the test passing status of the plurality of test data packets, and the test comparison status between the positioning algorithm to be tested and at least one positioning algorithm that has been tested.

According to the second aspect of the embodiments of this specification, a device for detecting the accuracy of a vehicle positioning algorithm is provided, the device comprising:

An acquisition module, configured to acquire a plurality of test data packets, the test data packets including driving data collected during vehicle driving;

A determining module, configured to determine a first location information of the vehicle based on the driving data included in each test data packet and a pre-established map;

The determining module is also used to determine a second positioning information of the vehicle based on each test data packet through the positioning algorithm to be detected;

The determining module is further configured to determine a piece of positioning error information based on each first positioning information and corresponding second positioning information;

The determining module is further configured to determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement when the positioning error information corresponding to the multiple test data packets meets the set condition.

In some embodiments of the present application, a plurality of test data packets are prepared in advance, and the test data packets are marked with a positioning problem label and an environment label, the positioning problem label is used to indicate the cause of the vehicle positioning error, and the environment label is used to indicate The environmental conditions during the driving process of the vehicle, the preparation process of multiple test data packages includes:

Acquiring driving data collected by at least one vehicle during driving;

Based on the time stamp of the driving data, the collected driving data is segmented to obtain multiple candidate data packets;

Based on the visual display results of each candidate data packet, obtain the positioning problem label and environment label of each candidate data packet;

Candidate data packets whose positioning problem labels and environment labels meet the target conditions are used as test data packets.

In some embodiments of the present application, the driving data includes at least initial positioning information, point cloud data and wheel speed data;

The determining module, when used for determining a first positioning information of the vehicle based on the driving data included in each test data packet and the pre-established map, is used for:

For any test data packet, based on the initial positioning information and wheel speed data in the driving data included in the test data packet, determine the positioning area of the vehicle in the map;

From the candidate point cloud data corresponding to each candidate positioning information in the positioning area, the candidate point cloud data matching the point cloud data is determined, and the candidate positioning information corresponding to the candidate point cloud data is determined as the first positioning information.

In some embodiments of the present application, the positioning information includes position information and attitude information, the position information includes the position coordinates of the vehicle in the set coordinate system, and the attitude information is used to indicate the distance between the position of the vehicle and the coordinate axes of the set coordinate system The angular relationship of , the positioning error information includes relative pose error and absolute trajectory error;

The determining module, when used to determine a positioning error information based on each first positioning information and corresponding second positioning information, is used to:

For any first positioning information and the second positioning information corresponding to the first positioning information, the absolute trajectory error is determined based on the first position information included in the first positioning information and the second position information included in the second positioning information, based on The first pose information included in the first positioning information and the second pose information included in the second positioning information determine a relative pose error.

In some embodiments of the present application, the determination module is used to determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirements when the positioning error information corresponding to the multiple test data packets meets the set conditions. :

When the positioning error information corresponding to multiple test data packets is less than the set error threshold, it is determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement.

In some embodiments of the present application, the determining module is further configured to determine that the positioning accuracy of the positioning algorithm to be tested does not meet the positioning requirements when the positioning error information corresponding to the multiple test data packets does not meet the set conditions.

In some embodiments of the present application, the device also includes:

The output module is used to output test result information, and the test result information is used to indicate the test passing status of multiple test data packets, and the test comparison between the positioning algorithm to be tested and at least one positioning algorithm that has been tested.

According to a third aspect of the embodiments of the present application, a computing device is provided, including a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein the above-mentioned vehicle positioning algorithm is realized when the processor executes the computer program The operations performed by the accuracy detection method.

According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, and a program is stored on the computer-readable storage medium, and the program is executed by a processor to execute the operations performed by the accuracy detection method of the vehicle positioning algorithm described above.

According to a fifth aspect of the embodiments of the present application, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the operations performed by the above-mentioned method for detecting the accuracy of the vehicle positioning algorithm are implemented.

The technical solutions provided by the embodiments of the present application may include the following beneficial effects:

The present application obtains the test data packets including the driving data collected during the driving process of the vehicle, thereby determining a first positioning information of the vehicle based on the driving data included in each test data packet and the pre-established map, and by waiting The detection positioning algorithm, based on each test data packet, determines a second positioning information of the vehicle to obtain a plurality of first positioning information and a plurality of second positioning information, and then based on each first positioning information and the corresponding second positioning information Positioning information, determine a positioning error information, so that when the positioning error information corresponding to multiple test data packets meets the set conditions, determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirements, and realize the positioning of the self-driving vehicle Evaluation of the accuracy of the algorithm to ensure the accuracy of the location of the autonomous vehicle.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the specification.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and together with the description serve to explain the principles of the specification.

Fig. 1 is a flowchart of a method for detecting the accuracy of a vehicle positioning algorithm according to an exemplary embodiment of the present application.

Fig. 2 is a schematic diagram showing a display form of test result information according to an exemplary embodiment of the present application.

Fig. 3 is a frame diagram of an accuracy detection process of a vehicle positioning algorithm according to an exemplary embodiment of the present application.

Fig. 4 is a block diagram of an accuracy detection device for a vehicle positioning algorithm according to an exemplary embodiment of the present application.

Fig. 5 is a schematic structural diagram of a computing device according to an exemplary embodiment of the present application.

Detailed ways

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with this specification. Rather, they are merely examples of devices and methods consistent with aspects of the present specification, as detailed herein.

The terms used in this specification are for the purpose of describing particular embodiments only, and are not intended to limit the specification. As used in this application, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be called second information, and similarly, second information may also be called first information. Depending on the context, the word "if" as used herein may be interpreted as "at" or "when" or "in response to a determination."

The present application provides a method for detecting the accuracy of a vehicle positioning algorithm, which is used for detecting the accuracy of the positioning algorithm to be detected. Wherein, the positioning algorithm to be detected can be the positioning algorithm currently used by the vehicle, and the positioning algorithm to be detected can be any type of positioning algorithm, for example, the positioning algorithm to be detected can be a fusion positioning algorithm based on Bayesian filtering, Based on Ultra-wideband (UWB) least square method (Least Square Method, LSM) Taylor (Taylor) cascade vehicle positioning algorithm, etc., the application does not limit the specific type of positioning algorithm to be detected.

The above is only an exemplary description of the application scenario of the application, and does not constitute a limitation to the application scenario of the application. In more possible implementations, the application can be applied to various other scenarios involving the accuracy detection process of the vehicle positioning algorithm middle.

The accuracy detection method of the above-mentioned vehicle positioning algorithm can be performed by a computing device, which can be a server, such as a server, multiple servers, server clusters, cloud computing platforms, etc. Optionally, the computing device can also be a terminal device, For example, desktop computers, portable computers, advertising machines, all-in-one machines, etc., the present application does not limit the specific types of computing devices.

The above is the relevant introduction about the application scenarios of the present application. Next, the accuracy detection method of the vehicle positioning algorithm provided by the present application will be described in detail in combination with the embodiments of the present application.

As shown in Figure 1, Figure 1 is a flowchart of a method for detecting the accuracy of a vehicle positioning algorithm according to an exemplary embodiment of the present application, the method includes the following steps:

Step 101. Obtain a plurality of test data packets, the test data packets include driving data collected during the driving of the vehicle.

Step 102, based on the driving data included in each test data packet and the pre-established map, determine a first location information of the vehicle, and determine a second location information of the vehicle based on each test data packet through the positioning algorithm to be detected. positioning information.

Step 103: Determine a piece of positioning error information based on each piece of first positioning information and corresponding second positioning information.

Step 104 , in the case that the positioning error information corresponding to the multiple test data packets meets the set condition, determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement.

The present application obtains the test data packets including the driving data collected during the driving process of the vehicle, thereby determining a first positioning information of the vehicle based on the driving data included in each test data packet and the pre-established map, and by waiting The detection positioning algorithm, based on each test data packet, determines a second positioning information of the vehicle to obtain a plurality of first positioning information and a plurality of second positioning information, and then based on each first positioning information and the corresponding second positioning information Positioning information, determine a positioning error information, so that when the positioning error information corresponding to multiple test data packets meets the set conditions, determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirements, and realize the positioning of the self-driving vehicle Evaluation of the accuracy of the algorithm to ensure the accuracy of the location of the autonomous vehicle.

After introducing the basic implementation process of the present application, various optional implementation manners of the present application are introduced below.

In some embodiments, for step 101, the test data packets obtained in this step may be prepared in advance. In a possible implementation manner, the preparation of multiple test data packets may be implemented through the following steps:

Step 1. Obtain driving data collected by at least one vehicle during driving.

Wherein, the model, driving scene, and driving speed of the at least one vehicle may be the same or different, which is not limited in the present application.

For each vehicle, various types of data are collected during driving. For example, for any of the vehicles, multiple types of data collection devices can be provided on the vehicle, and the data collection devices can include vehicle cameras (such as driving recorders), laser radars, wheel speed sensors (such as wheel speedometers), etc. This application does not limit the specific type of the data acquisition device.

Taking the data acquisition device including the vehicle camera, lidar and wheel speed sensor as an example, the driving data collected by the vehicle camera can be image data, and the driving data collected by the laser radar can be point cloud data. The collected traveling data may be the traveling speed of the vehicle.

Step 2: Segment the collected driving data based on the time stamp of the driving data to obtain multiple candidate data packets.

It should be noted that, for any vehicle, the time stamps of the collected driving data are continuous, and the time-continuous driving data collected by the vehicle can be segmented according to the set duration, so that the driving data Divide into a plurality of candidate data packets whose duration is a set duration.

Taking the driving data collected by the vehicle as an example of continuous 100-second driving data, if the set duration is 10 seconds, the 100-second driving data can be divided into 10 candidate data packets, and each candidate The driving data included in the data package are all driving data collected in a time period of 10 seconds.

Step 3: Based on the visual display result of each candidate data packet, obtain the localization problem label and the environment label of each candidate data packet.

In a possible implementation, the data of each candidate data package can be visualized through a data visualization tool, so that relevant technical personnel can visually see the driving process of the vehicle, and then based on the relevant conditions of the vehicle driving process, for The candidate data packets are set with corresponding location problem tags and environment tags, so that the computing device can obtain the location problem tags and environment tags set by relevant technicians for each candidate data packet.

Among them, the positioning problem label is used to indicate the cause of the vehicle positioning error, and the environment label can be used to indicate the environmental conditions during the driving of the vehicle. Optionally, the positioning problem tag can include initial positioning failure, positioning deviation during operation, positioning error caused by data error, etc. The environment tag can be used to indicate the weather conditions, road conditions, and the number of pedestrians during the driving of the vehicle, for example, The environment label may be an Operational Design Domain (ODD) label, or the environment label may be another type of label, and this application does not limit the specific types of the location problem label and the environment label.

Step 4: Use the candidate data packets whose positioning problem labels and environment labels meet the target conditions as test data packets.

In a possible implementation, the computing device can determine the candidate data packets corresponding to the positioning problem labels and environment labels that meet the target conditions based on the positioning problem labels and environment labels of each candidate data packet, and set the positioning problem labels that meet the target conditions to Candidate data packets corresponding to environment labels are used as test data packets.

Wherein, the target condition may be that the location problem label is the first setting type, and/or the environment label is the second setting type, and this application does not limit the specific content of the first setting type and the second setting type.

It should be noted that the first setting type may include at least one positioning problem label type, and the second setting type may include at least one environment label type. This application includes the first setting type and the second setting type There is no limit to the number of label types. Based on this, in order to make the test data packets cover as many positioning error scenarios as possible, the number of label types included in the first setting type and the second setting type can be made as large as possible, so as to ensure a variety of positioning error scenarios Candidate data packets collected under the scenario can be used as test data packets to ensure that the test data packets can cover as many positioning error scenarios as possible.

In addition, it needs to be emphasized that since there may be multiple vehicles used to collect driving data, and the models and driving speeds of these multiple vehicles may be different, in order to ensure the generalization of the positioning algorithm to be detected, in When selecting the test data packets, the candidate data packets as the test data packets can be collected by different vehicles, so that the detection of the positioning algorithm can be realized based on the test data packets collected by different vehicles, thereby improving the detection rate. Generalization of localization algorithms.

Through the above process, a plurality of test data packets meeting the test requirements can be obtained, so that the accuracy of the positioning algorithm to be detected can be detected subsequently based on the plurality of test data packets.

It should be noted that since multiple candidate data packets are marked with localization problem labels and environment labels, the test data packets determined from the multiple candidate data packets are also marked with localization problem labels and environment labels.

Optionally, after the test data packets are determined from the multiple candidate data packets, the multiple test data packets can be recorded into the data set, so as to construct a test data set including multiple test data packets, so that subsequent tests can be based on The data set is used to test the accuracy of the positioning algorithm to be tested. Among them, the test data set records the positioning problem labels and environment labels of each test data package.

In more possible implementation manners, new driving data may also be obtained in determining multiple test data packets. At this time, a new test data packet may also be determined based on the newly obtained driving data, based on The process of determining a new test data package from the newly acquired driving data can refer to the above-mentioned embodiments, and will not be repeated here.

It should be noted that after the new test data packet is determined, the determined new test data packet and the previously determined test data packet can be used together as the test data packet for detecting the positioning algorithm to be detected , so that the expansion of the test data package can be realized, and then the precision detection degree of the positioning algorithm can be improved.

In addition, in the case of constructing a test data set based on a plurality of test data packets, after determining a new test data packet based on newly acquired driving data, the determined new test data packet may be added to the test data set, So that the computing device can obtain new test data packets and previously determined test data packets by obtaining the test data set.

After the multiple test data packets are determined through the above process, the computing device can obtain multiple test data packets through step 101, so as to detect the accuracy of the positioning algorithm to be tested based on the obtained test data packets.

In some embodiments, the driving data may include point cloud data and wheel speed data. Optionally, the driving data may also include more types of data. This application does not limit the types of data included in the driving data. Taking the driving data including point cloud data and wheel speed data as an example, for step 102, when determining a first positioning information of the vehicle based on the driving data included in each test data packet and the pre-established map, the following steps can be taken: accomplish:

Step 102-1-1. For any test data packet, determine the location area of the vehicle on the map based on the initial positioning information and wheel speed data in the driving data included in the test data packet.

It should be noted that the initial positioning information may be Global Navigation Satellite System (GNSS) information, and optionally, the initial positioning information may also be other types of positioning information. This application does not specify the specific type of initial positioning information. limited.

By setting the initial positioning information in the test data packet, based on the initial positioning information, the determination of the positioning information can be realized through loopback. Since the error will continue to accumulate during the driving process, through the loopback, the vehicle can pass the previous passing position again. When , the error is evenly distributed to each positioning point to ensure that the acquired position and attitude information of each point is more accurate.

In a possible implementation, when determining the positioning area of the vehicle in the map based on the initial positioning information and wheel speed data in the driving data included in the test data package, based on the wheel speed data and the driving time of the vehicle, Determine the driving distance of the vehicle, and then determine the positioning area of the vehicle in the map based on the driving distance of the vehicle and the initial positioning information.

Optionally, when determining the positioning area of the vehicle in the map based on the vehicle's driving distance and initial positioning information, the predicted positioning information of the vehicle in multiple directions can be determined based on the sum of the vehicle's driving distance and initial positioning information , to obtain a plurality of predicted positioning information, and then add and subtract the set distance value on the basis of each predicted positioning information to obtain a positioning area centered on the predicted positioning information. At this time, the vehicle’s position in the map The positioning area may be an annular area.

In more possible implementations, the driving data of the vehicle can also include the driving direction of the vehicle, then based on the initial positioning information and wheel speed data in the driving data included in the test data package, determine the positioning area of the vehicle in the map , the vehicle’s travel distance can be determined based on the wheel speed data and the vehicle’s travel time, and then based on the vehicle’s travel distance, travel direction, and initial positioning information, the location area of the vehicle in the map can be determined.

Wherein, when determining the positioning area of the vehicle in the map based on the vehicle's driving distance, driving direction, and initial positioning information, a predicted positioning information can be determined based on the sum of the vehicle's driving distance in the driving direction and the initial positioning information , so that the set distance value is added and subtracted respectively on the basis of the predicted positioning information to obtain a positioning area centered on the predicted positioning information. At this time, the positioning area of the vehicle in the map can be a line segment.

Step 102-1-2, from the candidate point cloud data corresponding to each candidate positioning information in the positioning area, determine the candidate point cloud data that matches the point cloud data, and determine the candidate positioning information corresponding to the candidate point cloud data as the first positioning information.

It should be noted that the positioning area includes a plurality of candidate positioning information, and each candidate positioning information corresponds to point cloud data (referred to as candidate point cloud data). Therefore, the computing device can combine the point cloud data included in the driving data with The candidate point cloud data are compared to determine the candidate point cloud data that matches the point cloud data, so that the candidate positioning information corresponding to the candidate point cloud data is used as the first positioning information.

It should be noted that the positioning information may include position information and attitude information, the position information may include the position coordinates of the vehicle in the set coordinate system, and the attitude information may be used to indicate the distance between the position of the vehicle and the coordinate axes of the set coordinate system. Angle relationship, optionally, the set coordinate system may be any type of coordinate system, for example, the set coordinate system may be an earth coordinate system.

Wherein, the first positioning information may be recorded as ground truth(x, y, yaw). Among them, x is the position coordinate of the vehicle in the x-axis direction of the set coordinate system, y is the position coordinate of the vehicle in the x-axis direction of the set coordinate system, x and y are position information; yaw is the position of the vehicle The angle between the line connecting the coordinate origin and the y-axis of the set coordinate system is the yaw angle, and yaw is the attitude information. The above is only an exemplary form of positioning information. In more possible implementations, the positioning information can also be in other forms. For example, the position information can also be three-dimensional coordinates, and the attitude information can also include pitch angle and roll angle. roll, this application does not limit the specific form of location information.

For step 102, when a second positioning information of the vehicle is determined based on each test data packet through the positioning algorithm to be detected, each test data packet can be input into the positioning algorithm to be detected respectively, and by running the positioning algorithm to be detected The algorithm outputs the second positioning information corresponding to each test data packet to obtain a plurality of second positioning information of the vehicle, and one second positioning information can be obtained based on one test data packet.

It should be noted that, for an introduction about the second positioning information, reference may be made to the foregoing embodiments, and details are not repeated here. In addition, it should be noted that the form of the second positioning information needs to be consistent with the form of the first positioning information, so as to ensure the smooth progress of the subsequent process.

In addition, it should be noted that there is a one-to-one correspondence between the second location information and the first location information, that is, one piece of first location information and one piece of second location information can be obtained based on one test data packet.

After the first positioning information and the second positioning information are obtained through the above process, a positioning error information can be determined based on each first positioning information and corresponding second positioning information through step 103 .

It should be noted that since the positioning information can include position information and attitude information, the positioning error information can include relative pose error and absolute trajectory error, wherein the absolute trajectory error can include lateral absolute trajectory error (that is, the x-coordinate error) and the longitudinal absolute trajectory error (that is, the error of the y coordinate). In addition, in the case that the positioning information includes other types of information, the positioning error information may also include other types of information, which is not limited in this application.

Taking the positioning error information including relative pose error and absolute trajectory error as an example, for step 103, when determining a positioning error information based on each first positioning information and corresponding second positioning information, it can be implemented in the following manner:

For any first positioning information and the second positioning information corresponding to the first positioning information, determine the absolute trajectory error based on the first position information included in the first positioning information and the second position information included in the second positioning information , based on the first pose information included in the first positioning information and the second pose information included in the second positioning information, determine a relative pose error.

In a possible implementation manner, when determining the absolute track error based on the first position information included in the first positioning information and the second position information included in the second positioning information, the first position information and the second The difference in position information is taken as the absolute trajectory error. When determining the relative pose error based on the first pose information included in the first positioning information and the second pose information included in the second positioning information, the difference between the first pose information and the second pose information can be used as the relative Attitude error.

Through the above process, based on each first positioning information and the corresponding second positioning information, an absolute trajectory error and a relative attitude error can be determined as one positioning error information, so that multiple positioning error information can be obtained, each A test data packet may correspond to a positioning error information.

After the positioning error information corresponding to the multiple test data packets is determined through the above process, it can be determined whether the positioning accuracy of the positioning algorithm to be tested meets the positioning requirements through the positioning error information corresponding to the multiple test data packets.

In some embodiments, when the positioning error information corresponding to the multiple test data packets meets the set condition, it can be determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement. For example, in a case where the positioning error information corresponding to multiple test data packets is less than the set error threshold, it is determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement.

In some other embodiments, when the positioning error information corresponding to the plurality of test data packets does not meet the set condition, it is determined that the positioning accuracy of the positioning algorithm to be tested does not meet the positioning requirement. For example, if there is one positioning error information greater than or equal to a set error threshold in the positioning error information corresponding to multiple test data packets, it can be determined that the positioning accuracy of the positioning algorithm to be tested does not meet the positioning requirements.

Wherein, setting the error threshold may include a first error threshold and a second error threshold, the first error threshold may correspond to an absolute trajectory error, and the second error threshold may correspond to a relative attitude error. Therefore, when determining whether the positioning error information corresponding to a plurality of test data packets meets the set conditions, it can be determined whether the absolute trajectory errors corresponding to the plurality of test data packets are less than the first error threshold, whether the relative attitude errors of the plurality of test data packets is less than the second error threshold, when the absolute trajectory errors corresponding to the multiple test data packets are less than the first error threshold, and the relative attitude errors of the multiple test data packets are less than the second error threshold, multiple test data packets can be determined The positioning error information of satisfies the set conditions. Conversely, there is an absolute trajectory error greater than or equal to the first error threshold among the absolute trajectory errors corresponding to multiple test data packets, or there is a relative attitude error greater than or equal to the second error among the relative attitude errors of multiple test data packets In the threshold case, it can be determined that the positioning error information of multiple test data packets does not meet the set condition.

Through the above-mentioned embodiments, the accuracy detection method of the vehicle positioning algorithm provided by the present application can provide a method from analyzing the positioning problem of each candidate data packet, to finding a test data packet suitable for the accuracy detection of the positioning algorithm, and then to using The test data package is a full-link solution to detect the positioning accuracy of the vehicle positioning algorithm, so as to realize the evaluation of the accuracy of the positioning algorithm of the self-driving vehicle, and then ensure the accuracy of the positioning position of the self-driving vehicle.

In more possible implementation manners, after determining whether each test data packet passes the test based on the positioning error information of each test data packet, the test result information may also be output. Wherein, the test result information may be used to indicate the test passing status of multiple test data packets, and the test comparison status between the positioning algorithm to be tested and at least one positioning algorithm that has been tested.

Wherein, the test passing status refers to whether the test data packet passes the test, and the test data packet passes the test, that is, the positioning error information corresponding to the test data packet is less than the set error threshold; otherwise, the positioning error information corresponding to the test data packet If the information is greater than or equal to the set error threshold, it can be determined that the test data packet fails the test.

By outputting the test result information for indicating the test passing status of a plurality of test data packets and the test comparison between the positioning algorithm to be tested and at least one positioning algorithm that has been tested, so that the computing device or others can provide a visualization function The device can visually display the test result information output by the computing device, so that relevant technical personnel can realize the horizontal comparison of multiple positioning algorithms based on the displayed test result information, so as to determine the positioning algorithm with more positioning effects.

Optionally, the test result information can also classify and display the test passing status of multiple test data packets based on the positioning problem tags and environment tags of each test data package. For example, the test passing of the same type of positioning problem tags can be summarized conditions and the test passing status of the same type of environmental labels, so that relevant technical personnel can determine which positioning problems are likely to occur in which environment based on the classification and display results, so that subsequent targeted improvements can be made.

Referring to FIG. 2, FIG. 2 is a schematic diagram of a display form of test result information shown according to an exemplary embodiment of the present application. As shown in FIG. The test result information shown in 2 shows the number of test data packets used to detect the positioning accuracy of Algorithm 3 (that is, the number of test bags), the time spent in the test process (that is, the total duration of the test bag, including obtaining test data packets and The total duration of the test process), the overall test situation and the classification display results of the test situation.

Wherein, the overall test situation is also the test passing situation of a plurality of test data packets as a whole, as shown in Figure 2, the overall test situation includes test time consumption, perfect pass rate (that is, the number of test data packets passing the test is in the total The proportion of the number of test data packets), the average frequency (that is, the test speed of the data packet), the maximum lateral error (that is, the maximum value of the lateral absolute trajectory errors of multiple absolute trajectory errors), the maximum Longitudinal error (that is, the maximum value among the longitudinal absolute trajectory errors of multiple absolute trajectory errors), the maximum angular deviation (that is, the maximum value among multiple relative pose errors), and the minimum lateral error (that is, multiple The minimum value of the absolute trajectory error in the horizontal absolute trajectory error), the minimum longitudinal error (that is, the minimum value of the longitudinal absolute trajectory error of multiple absolute trajectory errors), the minimum angular deviation (that is, the multiple relative pose errors the minimum value in ).

The classification and display results of the test situation may include classification and display results based on positioning problem tags and classification and display results based on environment tags. Among them, the classification and display results based on the positioning problem label are also the display content corresponding to the algorithm test situation, including perfect pass, initial positioning failure, automatic retrieval after positioning deviation during motion, non-recovery after positioning failure during operation, running The data packet test passing status corresponding to the five positioning problem tags of positioning timeout, that is, the number of test data packets that have passed the test and the passing rate of the test data packets corresponding to the five positioning problem tags; the classification and display results based on the environmental tags are also That is, the passing status of the data packet tests corresponding to the five environmental labels of sunny-day, sunny-night, rain-day, rain-night, and snow-day, that is, the test data corresponding to the five environmental labels that have passed the test The number of packets and the pass rate of test packets.

In addition, the test result information shown in FIG. 2 also shows a test comparison between the positioning algorithm to be tested and at least one positioning algorithm that has been tested. The test result information shown in Figure 2 is provided with an algorithm selection control (that is, the selection box corresponding to the text marked "Please select the algorithm to be compared"), and relevant technical personnel can select the algorithm through the algorithm set in Figure 2. The control is used to select the positioning algorithm to be compared with the current positioning algorithm to be detected, and the relevant technical personnel can trigger the algorithm selection control, so that the device can display at least one positioning algorithm that has been detected in response to the trigger operation of the relevant technical personnel, so that relevant Technicians can choose from them.

Among them, the tested positioning algorithm used for comparison with No. 3 algorithm in Fig. 2 is No. 2 algorithm, and the test comparison between No. 3 algorithm and No. 2 algorithm includes the perfect Pass, initial positioning failure, automatic recovery after positioning deviation during motion, non-recovery after running positioning failure, and running positioning timeout. The question labels correspond to the number of test packets that have passed the test and the pass rate of the test packets.

In addition, the test result information shown in Figure 2 is provided with a label selection control (that is, a selection box corresponding to the text labeled "Please select the situation that needs to be included"). The label selection control is used to select the specific situation to be compared when comparing different algorithms (that is, the specific positioning problem label to be compared), and the relevant technical personnel can trigger the label selection control so that the device can respond to the trigger operation of the relevant technical personnel. Display at least one optional Locating Questions tab from which the relevant technician can choose.

The positioning problem label selected by the relevant technicians in Fig. 2 is positioning timeout in operation. Therefore, it is shown in Fig. 2 that the positioning problem label is positioning timeout in operation but the average longitudinal error, average lateral error and Average offset angle.

Optionally, the test result information shown in FIG. 2 also displays the basic information of the data packet and the environment label information of the No. 3 algorithm. Among them, the basic information of the data packet includes the positioning problem label of the test data packet (that is, the problem description), the test start time (that is, the Start Time), the test end time (that is, the End Time), the average vertical error, and the average horizontal Error, longitudinal error offset, lateral error offset, average angle error, and angle error offset; environmental label information includes weather conditions, vehicle position conditions, site conditions, road conditions, obstacle conditions (including whether there are static obstacles, What is the static obstacle, whether there is a dynamic obstacle, what is the dynamic obstacle).

Through the test result information shown in Figure 2, relevant technical personnel can compare the algorithm 3 detected this time with the previously tested algorithm (that is, algorithm 2), so that relevant technical personnel can intuitively understand that after improvement Algorithms for which test packets pass the test successfully and which previously passed test packets fail. In addition, the environment tags can be used to calculate what kind of positioning problems are likely to occur in what kind of scenes.

The accuracy detection method of the vehicle positioning algorithm provided by the above-mentioned embodiments can be referred to in FIG. 3 , which is a frame diagram of the accuracy detection process of a vehicle positioning algorithm shown in the present application according to an exemplary embodiment, as shown in FIG. 3 , the accuracy detection process of the vehicle positioning algorithm provided by each of the above embodiments can be divided into three major stages, which are respectively the data packet marking stage, the data set collection stage and the algorithm evaluation stage. In the data packet marking stage, each data packet is analyzed through the driving data after data visualization processing, so that each data packet can be marked according to the analysis results, and some representative or relatively Special labels to use these data packets as test data packets, and then record the positioning problem labels and environment labels of the test data packets in the data set construction stage, so that the algorithm can be evaluated based on multiple test data packets in the algorithm evaluation stage to generate Algorithm evaluation report.

What is shown in FIG. 3 is only a description of the flow of the present application. For the introduction of the specific implementation manners of each step in each stage, reference may be made to the above-mentioned embodiments, which will not be repeated here.

Corresponding to the foregoing method embodiments, the present application also provides embodiments of an apparatus and a computing device to which it is applied.

As shown in FIG. 4, FIG. 4 is a block diagram of a device for detecting the accuracy of a vehicle positioning algorithm according to an exemplary embodiment of the present application. The device includes:

An acquisition module 401, configured to acquire a plurality of test data packets, the test data packets including driving data collected during vehicle driving;

A determination module 402, configured to determine a first location information of the vehicle based on the driving data included in each test data packet and a pre-established map;

The determining module 402 is further configured to determine a second positioning information of the vehicle based on each test data packet through the positioning algorithm to be detected;

The determining module 402 is further configured to determine a positioning error information based on each first positioning information and corresponding second positioning information;

The determination module 402 is further configured to determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement when the positioning error information corresponding to the multiple test data packets meets the set condition.

In some embodiments of the present application, a plurality of test data packets are prepared in advance, and the test data packets are marked with a positioning problem label and an environment label, the positioning problem label is used to indicate the cause of the vehicle positioning error, and the environment label is used to indicate The environmental conditions during the driving process of the vehicle, the preparation process of multiple test data packages includes:

Acquiring driving data collected by at least one vehicle during driving;

Based on the time stamp of the driving data, the collected driving data is segmented to obtain multiple candidate data packets;

Based on the visual display results of each candidate data packet, obtain the positioning problem label and environment label of each candidate data packet;

Candidate data packets whose positioning problem labels and environment labels meet the target conditions are used as test data packets.

In some embodiments of the present application, the driving data includes at least initial positioning information, point cloud data and wheel speed data;

The determining module 402, when determining a first positioning information of the vehicle based on the driving data included in each test data packet and the pre-established map, is used for:

For any test data packet, based on the initial positioning information and wheel speed data in the driving data included in the test data packet, determine the positioning area of the vehicle in the map;

From the candidate point cloud data corresponding to each candidate positioning information in the positioning area, the candidate point cloud data matching the point cloud data is determined, and the candidate positioning information corresponding to the candidate point cloud data is determined as the first positioning information.

In some embodiments of the present application, the positioning information includes position information and attitude information, the position information includes the position coordinates of the vehicle in the set coordinate system, and the attitude information is used to indicate the distance between the position of the vehicle and the coordinate axes of the set coordinate system The angular relationship of , the positioning error information includes relative pose error and absolute trajectory error;

The determining module 402, when determining a positioning error information based on each first positioning information and corresponding second positioning information, is used to:

For any first positioning information and the second positioning information corresponding to the first positioning information, the absolute trajectory error is determined based on the first position information included in the first positioning information and the second position information included in the second positioning information, based on The first pose information included in the first positioning information and the second pose information included in the second positioning information determine a relative pose error.

In some embodiments of the present application, the determination module 402 is used to determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirements when the positioning error information corresponding to multiple test data packets meets the set conditions. At:

When the positioning error information corresponding to multiple test data packets is less than the set error threshold, it is determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement.

In some embodiments of the present application, the determination module 402 is further configured to determine that the positioning accuracy of the positioning algorithm to be tested does not meet the positioning requirements when the positioning error information corresponding to the multiple test data packets does not meet the set conditions.

In some embodiments of the present application, the device also includes:

The output module is used to output test result information, and the test result information is used to indicate the test passing status of multiple test data packets, and the test comparison between the positioning algorithm to be tested and at least one positioning algorithm that has been tested.

For the implementation process of the functions and effects of each module in the above-mentioned device, please refer to the implementation process of the corresponding steps in the above-mentioned method for details, and details will not be repeated here.

As for the device embodiment, since it basically corresponds to the method embodiment, for related parts, please refer to the part description of the method embodiment. The device embodiments described above are only illustrative, and the modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in One place, or it can be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this specification. It can be understood and implemented by those skilled in the art without creative effort.

The present application also provides a computing device. Referring to FIG. 5 , FIG. 5 is a schematic structural diagram of a computing device according to an exemplary embodiment of the present application. As shown in FIG. 5, the computing device includes a processor 510, a memory 520, and a network interface 530. The memory 520 is used to store computer instructions that can run on the processor 510. The processor 510 is used to implement the present invention when executing the computer instructions. Applying the accuracy detection method of the vehicle positioning algorithm provided by any embodiment, the network interface 530 is used to realize the input and output functions. In more possible implementation manners, the computing device may further include other hardware, which is not limited in this application.

The present application also provides a computer-readable storage medium. The computer-readable storage medium can be in various forms. For example, in different examples, the computer-readable storage medium can be: RAM (Radom Access Memory, Random Access Memory ), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), solid-state drives, storage disks of any type (such as compact discs, DVDs, etc.), or similar storage media, or combinations thereof. In particular, the computer readable medium may also be paper or other suitable medium capable of printing the program. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the accuracy detection method of the vehicle positioning algorithm provided by any embodiment of the present application is implemented.

The present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the accuracy detection method of the vehicle positioning algorithm provided in any embodiment of the present application is implemented.

Those skilled in the art should understand that one or more embodiments of this specification may be provided as a method, apparatus, computing device, computer-readable storage medium, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may employ a computer program embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein. The form of the product.

Each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the embodiment corresponding to the computing device, since it is basically similar to the method embodiment, the description is relatively simple, and for relevant parts, please refer to part of the description of the method embodiment.

The foregoing describes specific embodiments of this specification. Other embodiments are within the scope of the application. In some cases, the actions or steps recited herein can be performed in an order different from that in the examples and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Multitasking and parallel processing are also possible or may be advantageous in certain embodiments.

Embodiments of the subject matter and functional operations described in this specification can be implemented in digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or in A combination of one or more of . Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, that is, one or more of computer program instructions encoded on a tangible, non-transitory program carrier for execution by or to control the operation of data processing apparatus. Multiple modules. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical or electromagnetic signal, which is generated to encode and transmit information to a suitable receiver device for transmission by the vehicle The accuracy detection means of the positioning algorithm is executed. A computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

Computers suitable for the execution of a computer program include, for example, general and/or special purpose microprocessors, or any other type of central processing unit. Generally, a central processing unit will receive instructions and data from a read only memory and/or a random access memory. The essential components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to, one or more mass storage devices for storing data, such as magnetic or magneto-optical disks, or optical disks, to receive data therefrom or to It transmits data, or both. However, a computer is not required to have such a device. In addition, a computer may be embedded in another device such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a device such as a Universal Serial Bus (USB) ) portable storage devices like flash drives, to name a few.

Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices (such as EPROM, EEPROM, and flash memory devices), magnetic disks (such as internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as primarily describing features of particular embodiments of particular inventions. Certain features that are described in this specification in multiple embodiments can also be implemented in combination in a single embodiment. On the other hand, various features that are described in a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, although features may function in certain combinations as described above and even be initially so claimed, one or more features from a claimed combination may in some cases be removed from that combination and the claimed A protected combination can point to a subcombination or a variant of a subcombination.

Similarly, while operations are depicted in the figures in a particular order, this should not be construed as requiring that those operations be performed in the particular order shown, or sequentially, or that all illustrated operations be performed, to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can often be integrated together in a single software product in, or packaged into multiple software products.

Thus, certain embodiments of the subject matter have been described. Other embodiments are within the scope of this application. In some cases, the actions recited herein can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.

Other embodiments of the description will readily occur to those skilled in the art from consideration of the specification and practice of the invention claimed herein. This description is intended to cover any modification, use or adaptation of this description. These modifications, uses or adaptations follow the general principles of this description and include common knowledge or conventional technical means in this technical field for which this description does not apply . That is, the specification is not limited to the precise structures that have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof.

The above descriptions are only optional embodiments of this specification, and are not intended to limit this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this specification shall be included in this specification. within the scope of protection.

Claims (10)

1. A method for detecting accuracy of a vehicle location algorithm, characterized in that the method comprises: Obtaining a plurality of test data packets, the test data packets including driving data collected during vehicle driving; Determine a first location information of the vehicle based on the driving data included in each test data packet and a pre-established map, and determine a first location information of the vehicle based on each test data packet through a positioning algorithm to be detected 2. positioning information; determining a piece of positioning error information based on each first positioning information and corresponding second positioning information; In a case where the positioning error information corresponding to the plurality of test data packets satisfies the set condition, it is determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement. 2. The method according to claim 1, wherein the plurality of test data packets are prepared in advance, the test data packets are marked with a positioning problem label and an environment label, and the positioning problem label is used to indicate The reason for the error in vehicle positioning, the environmental label is used to indicate the environmental conditions during the driving of the vehicle, and the preparation process of the multiple test data packets includes: Acquiring driving data collected by at least one vehicle during driving; Segmenting the collected driving data based on the time stamp of the driving data to obtain a plurality of candidate data packets; Based on the visual display results of each candidate data packet, obtain the positioning problem label and environment label of each candidate data packet; A candidate data packet whose location question label and environment label meet a target condition is used as the test data packet. 3. The method according to claim 1, wherein the driving data includes at least initial positioning information, point cloud data and wheel speed data; The determining a first positioning information of the vehicle based on the driving data included in each test data packet and a pre-established map includes: For any test data packet, based on the initial positioning information and wheel speed data in the driving data included in the test data packet, determine the positioning area of the vehicle in the map; From the candidate point cloud data corresponding to each candidate positioning information in the positioning area, determine the candidate point cloud data that matches the point cloud data, and determine the candidate positioning information corresponding to the candidate point cloud data as the first positioning information. 4. The method according to claim 1, wherein the positioning information includes position information and attitude information, the position information includes the position coordinates of the vehicle in the set coordinate system, and the attitude information is used for Indicating the angular relationship between the position of the vehicle and the coordinate axes of the set coordinate system, the positioning error information includes relative pose error and absolute trajectory error; The determining a positioning error information based on each first positioning information and corresponding second positioning information includes: For any first positioning information and second positioning information corresponding to the first positioning information, based on the first position information included in the first positioning information and the second position information included in the second positioning information, The absolute trajectory error is determined, and the relative pose error is determined based on the first attitude information included in the first positioning information and the second attitude information included in the second positioning information. 5. The method according to claim 1, wherein, in the case where the positioning error information corresponding to the plurality of test data packets satisfies a set condition, it is determined that the positioning accuracy of the positioning algorithm to be detected satisfies Positioning needs, including: In a case where the positioning error information corresponding to the plurality of test data packets is less than a set error threshold, it is determined that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement. 6. The method according to claim 1, wherein after determining a positioning error information based on each first positioning information and corresponding second positioning information, the method further comprises: In a case where the positioning error information corresponding to the plurality of test data packets does not meet the set condition, it is determined that the positioning accuracy of the positioning algorithm to be tested does not meet the positioning requirement. 7. The method according to claim 1, further comprising: Outputting test result information, where the test result information is used to indicate the test passing status of the plurality of test data packets and the test comparison status between the positioning algorithm to be tested and at least one positioning algorithm that has been tested. 8. A precision detection device for a vehicle positioning algorithm, characterized in that the device comprises: An acquisition module, configured to acquire a plurality of test data packets, the test data packets including driving data collected during vehicle driving; A determining module, configured to determine a first positioning information of the vehicle based on the driving data included in each test data packet and a pre-established map; The determining module is further configured to determine a second positioning information of the vehicle based on each test data packet through the positioning algorithm to be detected; The determining module is further configured to determine a positioning error information based on each first positioning information and corresponding second positioning information; The determination module is further configured to determine that the positioning accuracy of the positioning algorithm to be tested meets the positioning requirement when the positioning error information corresponding to the plurality of test data packets meets the set condition. 9. A computing device, characterized in that the computing device includes a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor implements the computer program as claimed in claim 1 when executing the program. Operations performed by the accuracy detection method of the vehicle positioning algorithm described in any one of requirements 1 to 7. 10. A computer-readable storage medium, characterized in that a program is stored on the computer-readable storage medium, and the program is executed by a processor according to the vehicle positioning algorithm according to any one of claims 1 to 7. The operation performed by the accuracy detection method.

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