CN112966059B

CN112966059B - Data processing method and device for positioning data, electronic equipment and medium

Info

Publication number: CN112966059B
Application number: CN202110232682.9A
Authority: CN
Inventors: 李元
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-03-02
Filing date: 2021-03-02
Publication date: 2023-11-24
Anticipated expiration: 2041-03-02
Also published as: CN112966059A

Abstract

The disclosure discloses a data processing method, device, equipment, medium and product for positioning data, and relates to the field of intelligent transportation. The data processing method for the positioning data comprises the following steps: acquiring positioning data for a vehicle, wherein the positioning data comprises a first data set and a second data set; determining first relative position information between the vehicle and the target road based on the first data set and the first map data; determining second relative position information between the vehicle and the target road based on the second data set and the second map data; comparing the first relative position information with the second relative position information to obtain a comparison result, wherein the comparison result indicates the accuracy of the first relative position information.

Description

Data processing method and device for positioning data, electronic equipment and medium

Technical Field

The present disclosure relates to the field of computer technology, and more particularly, to the field of intelligent transportation, and more particularly, to a data processing method for positioning data, a data processing apparatus for positioning data, an electronic device, a medium, and a program product.

Background

During driving of a vehicle, it is often necessary to locate the vehicle, in particular for an autonomous vehicle, and it is necessary to control the travel of the autonomous vehicle by means of the location data. The accuracy of the positioning data has a great influence on the safe running of the autonomous vehicle. In the related art, when the accuracy of positioning data is evaluated, the positioning data is usually evaluated manually, which results in high evaluation cost and low efficiency.

Disclosure of Invention

The present disclosure provides a data processing method, apparatus, electronic device, storage medium and program product for positioning data.

According to an aspect of the present disclosure, there is provided a data processing method for positioning data, including: acquiring positioning data for a vehicle, wherein the positioning data comprises a first data set and a second data set; determining first relative position information between the vehicle and a target road based on the first data set and first map data; determining second relative position information between the vehicle and a target road based on the second data set and second map data; comparing the first relative position information with the second relative position information to obtain a comparison result, wherein the comparison result indicates the accuracy of the first relative position information.

According to another aspect of the present disclosure, there is provided a data processing apparatus for positioning data, including: the device comprises an acquisition module, a first determination module, a second determination module and a comparison module. The system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring positioning data for a vehicle, and the positioning data comprises a first data set and a second data set; a first determination module for determining first relative position information between the vehicle and a target road based on the first data set and first map data; a second determining module for determining second relative position information between the vehicle and a target road based on the second data set and second map data; and the comparison module is used for comparing the first relative position information with the second relative position information to obtain a comparison result, and the comparison result indicates the accuracy of the first relative position information.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the data processing method for positioning data described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the above-described data processing method for positioning data.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the data processing method for positioning data described above.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 schematically illustrates a system architecture of a data processing method and apparatus for positioning data according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a data processing method for positioning data according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of a data processing method for positioning data according to another embodiment of the present disclosure;

FIG. 4 schematically illustrates a schematic diagram of a data processing method for positioning data according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a schematic diagram of relative positions of a vehicle and lane lines according to an embodiment of the present disclosure;

FIG. 6 schematically illustrates a block diagram of a data processing apparatus for positioning data according to an embodiment of the present disclosure; and

fig. 7 is a block diagram of an electronic device for performing data processing to implement an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

The embodiment of the disclosure provides a data processing method for positioning data, which comprises the following steps: positioning data for a vehicle is acquired, the positioning data comprising a first data set and a second data set. Then, first relative position information between the vehicle and the target road is determined based on the first data set and the first map data, and second relative position information between the vehicle and the target road is determined based on the second data set and the second map data. Next, the first relative position information and the second relative position information are compared to obtain a comparison result, and the comparison result indicates the accuracy of the first relative position information.

Fig. 1 schematically illustrates a system architecture of a data processing method and apparatus for positioning data according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.

As shown in fig. 1, a system architecture 100 according to this embodiment may include a vehicle 101, a network 102, and a server 103. Network 102 is the medium used to provide a communication link between vehicle 101 and server 103. Network 102 may include various connection types such as wired, wireless communication links, or fiber optic cables, among others.

The vehicle 101 may interact with the server 103 through the network 102 to receive or transmit data or the like. The vehicle 101 may be mounted with various sensors for positioning, including, for example, an image sensor, a GPS sensor, an inertial sensor, a radar sensor, and the like.

The vehicle 101 may be an autonomous vehicle. The server 103 may be a server providing various services. For example, the vehicle 101 may transmit the positioning data collected by the sensor to the server 103, and the server 103 analyzes the positioning data, or the like. The server 103 may be a cloud server, i.e. the server 103 has cloud computing functionality. The server 103 may be integrated in the vehicle 101 as part of an on-board system or may be independent of the vehicle 101.

It should be noted that, the data processing method for positioning data provided by the embodiments of the present disclosure may be generally performed by the server 103. Accordingly, the data processing apparatus for positioning data provided by the embodiments of the present disclosure may be generally provided in the server 103. The data processing method for positioning data provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 103 and is capable of communicating with the vehicle 101 and/or the server 103. Accordingly, the data processing apparatus for positioning data provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 103 and is capable of communicating with the vehicle 101 and/or the server 103.

It should be understood that the number of vehicles, networks, and servers in fig. 1 are merely illustrative. There may be any number of vehicles, networks, and servers, as desired for implementation.

The embodiment of the present disclosure provides a data processing method for positioning data, and the data processing method for positioning data according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 5 in conjunction with the system architecture of fig. 1. The data processing for positioning data of the embodiments of the present disclosure is performed by, for example, the server 103 shown in fig. 1.

Fig. 2 schematically illustrates a flow chart of a data processing method for positioning data according to an embodiment of the present disclosure.

As shown in fig. 2, the data processing method 200 for positioning data according to the embodiment of the present disclosure may include, for example, operations S210 to S240.

In operation S210, positioning data for a vehicle is acquired, the positioning data including a first data set and a second data set.

In operation S220, first relative position information between the vehicle and the target road is determined based on the first data set and the first map data.

In operation S230, second relative position information between the vehicle and the target road is determined based on the second data set and the second map data.

In operation S240, the first relative position information and the second relative position information are compared to obtain a comparison result.

For example, vehicles are equipped with various types of sensors by which positioning data is collected. The first data set for example comprises positioning data acquired by a first type of sensor and the second data set for example comprises positioning data acquired by a second type of sensor. The acquisition accuracy of the second type of sensor is, for example, higher than the acquisition accuracy of the first type of sensor.

Based on the first data set, position data and attitude data of the vehicle indicated by the first data set may be obtained. Road data of a target road around the vehicle is acquired from the first map data based on the position data and the posture data of the vehicle indicated by the first data set. Then, first relative position information between the vehicle and the target road is determined based on the position data and the posture data of the vehicle indicated by the first data set and the road data of the target road.

Based on the second data set, position data and attitude data of the vehicle indicated by the second data set may be obtained. Road data of a target road around the vehicle is acquired from the second map data based on the position data and the posture data of the vehicle indicated by the second data set. Then, second relative position information between the vehicle and the target road is determined based on the position data and the posture data of the vehicle and the road data of the target road indicated by the second data set.

The data accuracy of the second map data is, for example, higher than the data accuracy of the first map data. The accuracy of the second relative position information is generally higher than that of the first relative position information, so the accuracy of the first relative position information can be evaluated with the second relative position information as a reference. For example, the second relative position information is compared with the first relative position information to obtain a comparison result, and the comparison result indicates the accuracy of the first relative position information. The comparison result includes, for example, a difference between the first relative position information and the second relative position information, the smaller the difference is, the higher the accuracy of the first relative position information is.

According to an embodiment of the present disclosure, in order to evaluate the accuracy of the first relative position information based on the first data set, the second relative position information is obtained based on the second data set. Then, the accuracy of the first relative position information is evaluated with the second relative position information as a reference, thereby reducing the evaluation cost and improving the evaluation efficiency.

Fig. 3 schematically illustrates a flow chart of a data processing method for positioning data according to another embodiment of the present disclosure.

As shown in fig. 3, the data processing method 300 for positioning data according to the embodiment of the present disclosure may include operations S311 to S312, operations S321 to S322, operations S331 to S334, and operations S340 to S360, for example.

In operation S311, a first data set for the vehicle is acquired

In operation S312, a second data set for the vehicle is acquired.

In operation S321, road data for a target road, which is located within a second preset area where the vehicle is currently located, is acquired from the first map data based on the first data set.

For example, the first map data corresponds to a first electronic map, and the first data set indicates position data and posture data of the vehicle, for example, the first data set includes the position data and posture data of the vehicle. Alternatively, the first data set includes position data and speed data of the vehicle, and the posture data may be obtained based on the position data and the speed data.

And inputting the position data and the posture data of the vehicle into a first electronic map to obtain the position and the posture of the vehicle in the first electronic map, determining the target road around the vehicle from the first electronic map, and acquiring the road data of the target road around the vehicle from the first map data. The target road may be a road on which the vehicle is currently located or a road adjacent to the road on which the vehicle is located.

In operation S322, first relative position information is determined based on the road data and the first data set acquired from the first map data.

In operation S350, an integration calculation is performed based on the at least one position data and the at least one velocity data position, resulting in at least one pose data.

In an embodiment of the present disclosure, the second data set includes a plurality of data. The plurality of data includes, for example, at least one position data and at least one speed data for the vehicle. The at least one position data comprises, for example, x-coordinate data, y-coordinate data, z-coordinate data. The at least one speed data comprises, for example, speed, acceleration, angular speed, angular acceleration.

And performing integral calculation based on the position data and the speed data to obtain the attitude data of the vehicle. The at least one gesture data includes, for example, heading angle data, roll angle data, pitch angle data. Then, at least one position data and at least one posture data are set as a second data set.

In operation S331, a plurality of change amount sets are acquired.

For example, each change amount set includes at least one change amount, and the plurality of data in the second data set includes at least one specified data, the at least one specified data being in one-to-one correspondence with the at least one change amount.

In operation S332, the second data set is processed according to each of the plurality of variable sets, to obtain a plurality of processed second data sets corresponding to the plurality of variable sets one to one.

For example, for each piece of specified data, the specified data is added with the amount of change corresponding to the specified data. The specified data includes x-coordinate data, y-coordinate data, z-coordinate data, and heading angle data.

Take 2 sets of variations as an example. The first change amount set includes, for example, dx_1, dy_1, dz_1, dyaw_1, the change amount dx_1 corresponds to x-coordinate data, the change amount dy_1 corresponds to y-coordinate data, the change amount dz_1 corresponds to z-coordinate data, and the change amount dyaw_1 corresponds to heading angle data.

The processed second data set corresponding to the first variable set is obtained by adding the variable dx_1 to the x-coordinate data in the second data set, adding the variable dy_1 to the y-coordinate data in the second data set, adding the variable dz_1 to the z-coordinate data in the second data set, and adding the variable dyaw_1 to the heading angle data in the second data set.

Similarly, the second set of variables includes, for example, dx_2, dy_2, dz_2, dyaw_2, and the processed second set of data corresponding to the second set of variables is obtained by a similar calculation.

Since the acquired second data set is affected by the external environment or the accuracy of the sensor itself, the data in the second data set is inevitably subject to certain errors. And processing the second data set through the plurality of variable quantity sets to obtain a plurality of second data sets, so that one with smaller error is determined from the plurality of second data sets as a target second data set, thereby improving the data accuracy and the positioning evaluation effect.

How the target second data set is determined from the plurality of second data sets will be described below with reference to operation S360.

In operation S360, a target second data set is determined from the plurality of processed second data sets.

In an embodiment of the present disclosure, the positioning data for the vehicle further includes, for example, image data including road data for a reference road. For example, the image data is an image of the surroundings of the vehicle acquired by an image sensor mounted in the vehicle, the image having therein road data of a reference road around the vehicle.

First, for each of a plurality of processed second data sets, road data for a specified road is acquired from second map data based on the second data set, the specified road being located within a first preset area where the vehicle is currently located.

For example, the second map data corresponds to second electronic maps, each second data set indicating position data and attitude data of the vehicle. And inputting the position data and the posture data of the vehicle in the second data set into a second electronic map to obtain the position and the posture of the vehicle in the second electronic map, and then determining the designated road around the vehicle from the second electronic map to obtain the road data of the designated road around the vehicle from the second map data. The specified road may be a road on which the vehicle is currently located or a neighboring road to the road on which the vehicle is located, which is indicated by the second electronic map.

Then, the degree of matching between the specified road and the reference road is determined based on the road data for the specified road indicated by the second electronic map and the road data for the reference road indicated by the image data, to obtain a plurality of degrees of matching in one-to-one correspondence with the plurality of processed second data sets. The degree of matching includes, for example, the degree of coincidence between the specified road and the reference road.

Next, a second data set corresponding to the maximum matching degree among the plurality of matching degrees is set as a target second data set.

In an embodiment of the present disclosure, the target second data set is determined from the plurality of second data sets by comparing the road data in the acquired image data with the road data in each of the second data sets. It will be appreciated that the target second data set is determined with the acquired image data as a reference such that the determined target second data set is highly accurate.

After determining the target second data set, second relative position information is determined based on the target second data set. The process of determining the second relative position information is as shown in operations S333 to S334.

In operation S333, road data for a target road, which is located within a third preset area where the vehicle is currently located, is acquired from the second map data based on the target second data set.

In operation S334, second relative position information is determined based on the road data acquired from the second map data and the target second data set.

For example, the second map data corresponds to a second electronic map, and the target second data set indicates position data and posture data of the vehicle. And inputting the position data and the posture data of the vehicle in the target second data set into a second electronic map to obtain the position and the posture of the vehicle in the second electronic map, and then determining the target road around the vehicle from the second electronic map to obtain the road data of the target road around the vehicle from the second map data. The target road may be a road on which the vehicle is currently located or a road adjacent to the road on which the vehicle is located. Then, second relative position information is determined based on the road data of the target road and the target second data set.

In operation S340, the first relative position information and the second relative position information are compared to obtain a comparison result.

In an embodiment of the present disclosure, the road data includes lane line data, for example. And establishing a vehicle body coordinate system by taking the center of the vehicle as an origin, wherein the vehicle body coordinate system comprises an x axis, a y axis and a z axis. The first relative position information includes, for example, at least a distance from the center of the vehicle to the lane line and an angle between the x-axis (or y-axis) of the vehicle body coordinate system and the lane line. Similarly, the second relative positional information includes, for example, at least a distance from the center of the vehicle to the lane line and an angle between the x-axis (or y-axis) of the vehicle body coordinate system and the lane line. The comparison result is obtained by comparing the distances in the first and second relative positions and comparing the angles in the first and second relative positions, so as to evaluate the accuracy of the first relative position based on the comparison result.

Fig. 4 schematically illustrates a schematic diagram of a data processing method for positioning data according to an embodiment of the present disclosure.

As shown in fig. 4, the vehicle-mounted sensor includes, for example, a first type sensor 401 and a second type sensor 402, the first type sensor 401 being, for example, an L3 level sensor, and the second type sensor 402 being, for example, an L4 level sensor. The first type of sensor 401 includes, but is not limited to, an image sensor, a GPS sensor, an inertial sensor. The second type of sensor 402 includes, but is not limited to, an image sensor, a GPS sensor, an inertial sensor, a radar sensor. The acquisition accuracy of the second type sensor 402 is, for example, higher than that of the first type sensor 401.

The image sensor is used for collecting image data around the vehicle, the GPS sensor is used for collecting GPS position data, the inertial sensor is used for collecting data such as speed, acceleration, angular speed and angular acceleration, and the radar sensor is used for collecting point cloud data.

According to an embodiment of the present disclosure, positioning data is acquired by the first type of sensor 401 and the second type of sensor 402, the positioning data comprising at least a first data set 403, image data 404, a second data set 405. For example, a first set of data 403 is acquired by a first type of sensor 401, a second set of data 405 is acquired by a second type of sensor 402, and image data 404 is acquired by both the first type of sensor 401 and the second type of sensor 402.

The first data set 403 includes, for example, position data and posture data for the vehicle. Alternatively, the first data set 403 may include position data and speed data for the vehicle, and the position data and the speed data are subjected to integral calculation to obtain posture data, so that the first data set 403 obtained after the integral calculation includes the position data and the posture data.

Next, the first data set 403 is input into the first electronic map 406, and first relative position information 407a of the vehicle and the target road is obtained. The first relative position information 407a includes, for example, a lane line identification id_1 of the target road, a distance l_1 between the vehicle and the lane line, and an angle α_1 between the vehicle and the lane line. In addition, first data 407b may also be acquired from the first data set 403, the first data 407b including, for example, position data p_1 and heading angle data yaw_1, p_1 of the vehicle, for example, coordinates (x_1, y_1, z_1) of the vehicle. The first relative position information 407a and the first data 407b are taken as the data to be evaluated 407.

The second data set 405 comprises, for example, position data and speed data for the vehicle. The second data set 405 is input to an RTK (Real-time Kinematic) solver 408 for calculation to obtain position data in a UTM (Universal Transverse Mercator Grid System, universal transverse ink card grid system) coordinate system and attitude data in a vehicle body coordinate system. The position data in the second data set 405 is, for example, data in a vehicle body coordinate system or other coordinate system, which the RTK solver 408 converts into a UTM coordinate system. And the RTK solver performs integral calculation on the position data and the speed data to obtain attitude data of the vehicle under a vehicle body coordinate system. The RTK solver calculates to obtain a calculated second data set 409, where the calculated second data set 409 includes, for example, position data in a UTM coordinate system and attitude data in a vehicle body coordinate system.

The second data set 409 is then processed using the particle filtering algorithm 410, for example by adding variations to the relevant data in the second data set 409, resulting in a plurality of second data sets 411.

Based on the plurality of second data sets 411 and the image data 404, a target second data set 412 is determined from the plurality of second data sets 411.

Next, the target second data set 412 is input into the second electronic map 413, resulting in second relative positional information 414a of the vehicle and the target road. The second relative position information 414a includes, for example, a lane line identification id_2 of the target road, a distance l_2 between the vehicle and the lane line, and an angle α_2 between the vehicle and the lane line. In addition, second data 414b may also be obtained from the target second data set 412, the second data 414b including, for example, position data p_2 and heading angle data yaw_2 of the vehicle, p_2 being, for example, coordinates (x_2, y_2, z_2) of the vehicle. The second relative position information 414a and the second data 414b are taken as reference data 414.

Then, the data to be evaluated 407 and the reference data 414 are input to a comparator 415 to be compared, and a comparison result 416 is obtained. The process of comparing the data to be evaluated 407 with the reference data 414 includes comparing the lane line identification id_1 with the lane line identification id_2, comparing the distance l_1 with the distance l_2, comparing the angle α_1 with the angle α_2, comparing the position data p_1 with the position data p_2, and comparing the heading angle data yaw_1 with the heading angle data yaw_2.

In an embodiment of the present disclosure, the first electronic map 406 is, for example, an L3-level high-precision map, and the precision of the first electronic map 406 may reach the meter level. The second electronic map 413 is, for example, an L4-level high-precision map, and the precision of the second electronic map 413 may reach the centimeter level.

Fig. 5 schematically illustrates a schematic diagram of relative positions of a vehicle and lane lines according to an embodiment of the present disclosure.

As shown in fig. 5, the lane lines include, for example, a first lane line 504 and a second lane line 505. A coordinate system is established with the center of the vehicle 501 as the origin o, the coordinate system including an x-axis and a y-axis. The distance of the vehicle 501 from the lane lines includes, for example, a distance L 'of the center (origin o) to the first lane line 504 and a distance L' of the center (origin o) to the second lane line 505. The angle of the vehicle 501 to the lane line includes, for example, an angle α 'of the y-axis to the first lane line 504 and an angle α' of the y-axis to the second lane line 505. Alternatively, the angle between the vehicle 501 and the lane line may also be the angle between the x-axis and the first lane line 504 and the angle between the x-axis and the second lane line 505.

Fig. 6 schematically illustrates a block diagram of a data processing apparatus for positioning data according to an embodiment of the present disclosure.

As shown in fig. 6, a data processing apparatus 600 for positioning data according to an embodiment of the present disclosure includes, for example, an acquisition module 610, a first determination module 620, a second determination module 630, and a comparison module 640.

The acquisition module 610 may be configured to acquire positioning data for a vehicle, the positioning data including a first data set and a second data set. The obtaining module 610 may, for example, perform operation S210 described above with reference to fig. 2 according to an embodiment of the present disclosure, which is not described herein.

The first determination module 620 may be configured to determine first relative location information between the vehicle and the target link based on the first data set and the first map data. According to an embodiment of the present disclosure, the first determining module 620 may perform, for example, operation S220 described above with reference to fig. 2, which is not described herein.

The second determination module 630 may be configured to determine second relative location information between the vehicle and the target link based on the second data set and the second map data. The second determining module 630 may, for example, perform operation S230 described above with reference to fig. 2 according to an embodiment of the present disclosure, which is not described herein.

The comparison module 640 may be configured to compare the first relative position information with the second relative position information to obtain a comparison result, where the comparison result indicates an accuracy of the first relative position information. The comparison module 640 may, for example, perform operation S240 described above with reference to fig. 2 according to an embodiment of the present disclosure, which is not described herein.

According to an embodiment of the present disclosure, the second data set comprises a plurality of data. The second determining module 630 includes: the system comprises a first acquisition sub-module, a processing sub-module and a first determination sub-module. The first acquisition submodule is used for acquiring a plurality of variable quantity sets. The processing sub-module is used for processing the second data set according to each variable quantity set in the variable quantity sets to obtain a plurality of processed second data sets which are in one-to-one correspondence with the variable quantity sets. The first determination submodule is used for determining second relative position information between the vehicle and the target road based on a target second data set and second map data in the plurality of processed second data sets.

According to an embodiment of the present disclosure, each of the variable sets includes at least one variable, the second data set includes a plurality of data, the plurality of data includes at least one specified data, and the at least one specified data corresponds one-to-one to the at least one variable. The processing sub-module is further configured to add, for each piece of specified data, the specified data to a variation corresponding to the specified data.

According to an embodiment of the present disclosure, the plurality of data includes at least one position data and at least one speed data for the vehicle. The apparatus 600 further comprises: a calculation module and a third determination module. The calculation module is used for carrying out integral calculation based on at least one position data and at least one speed data position to obtain at least one gesture data. The third determination module is configured to use the at least one position data and the at least one pose data as a second data set. The at least one location data includes x-coordinate data, y-coordinate data, z-coordinate data. The at least one gesture data includes heading angle data, roll angle data, pitch angle data.

According to an embodiment of the present disclosure, the at least one specified data includes: x-coordinate data, y-coordinate data, z-coordinate data, heading angle data.

According to an embodiment of the present disclosure, the positioning data further comprises image data comprising road data for the reference road. The apparatus 600 further comprises a fourth determining module for determining a target second data set from the plurality of processed second data sets. The fourth determination module includes: the second acquisition sub-module, the second determination sub-module and the third determination sub-module. The second obtaining sub-module is used for obtaining road data aiming at a specified road from second map data according to each second data set in the plurality of processed second data sets, and the specified road is located in a first preset area range where the vehicle is located currently. The second determination submodule is used for determining the matching degree between the specified road and the reference road based on the road data of the specified road and the road data of the reference road so as to obtain a plurality of matching degrees which are in one-to-one correspondence with the plurality of processed second data sets. The third determination submodule is used for taking a second data set corresponding to the maximum matching degree in the plurality of matching degrees as a target second data set.

According to an embodiment of the present disclosure, the road data includes lane line data.

According to an embodiment of the present disclosure, the first determination module 620 includes a third acquisition sub-module and a fourth determination sub-module. The third acquisition sub-module is used for acquiring road data aiming at a target road from the first map data based on the first data set, wherein the target road is positioned in a second preset area where the vehicle is currently positioned. And a fourth determination sub-module for determining the first relative position information based on the road data and the first data set acquired from the first map data.

According to an embodiment of the present disclosure, the first determination submodule includes an acquisition unit and a determination unit. The acquisition unit is used for acquiring road data aiming at a target road from the second map data based on the target second data set, wherein the target road is positioned in a third preset area range where the vehicle is currently positioned. The determination unit is configured to determine second relative position information based on road data acquired from second map data and a target second data set.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 7 illustrates a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic device 700 is intended to represent various forms of digital computers, such as laptops, desktops, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the respective methods and processes described above, for example, a data processing method for positioning data. For example, in some embodiments, the data processing method for positioning data may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When a computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the data processing method for positioning data described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the data processing method for positioning data by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A data processing method for positioning data, comprising:

acquiring positioning data for a vehicle, wherein the positioning data comprises a first data set and a second data set;

determining first relative position information between the vehicle and a target road based on the first data set and first map data;

acquiring a plurality of variable quantity sets;

processing the second data set according to each variable quantity set in the variable quantity sets to obtain a plurality of processed second data sets which are in one-to-one correspondence with the variable quantity sets;

acquiring road data for a specified road from second map data based on each of the plurality of processed second data sets, wherein the specified road is located in a first preset area range where the vehicle is currently located;

Determining a degree of matching between the specified road and the reference road based on the road data for the specified road and the road data for the reference road to obtain a plurality of degrees of matching in one-to-one correspondence with the plurality of processed second data sets; and

taking a second data set corresponding to the maximum matching degree in the plurality of matching degrees as a target second data set;

determining second relative position information between the vehicle and a target road based on a target second data set of the plurality of processed second data sets and the second map data; and

comparing the first relative position information with the second relative position information to obtain a comparison result, wherein the comparison result indicates the accuracy of the first relative position information.

2. The method of claim 1, wherein each variation set comprises at least one variation, the second data set comprising a plurality of data, the plurality of data comprising at least one specified data, the at least one specified data in one-to-one correspondence with the at least one variation;

the processing the second data set includes: for each piece of specified data, the specified data is added with a variation corresponding to the specified data.

3. The method of claim 2, wherein the plurality of data includes at least one position data and at least one speed data for a vehicle;

the method further comprises, prior to processing the second data set:

performing integral calculation based on the at least one position data and the at least one speed data position to obtain at least one attitude data; and

the at least one position data and the at least one pose data are used as the second data set,

the at least one position data comprises x-coordinate data, y-coordinate data and z-coordinate data, and the at least one posture data comprises course angle data, roll angle data and pitch angle data.

4. A method according to claim 3, wherein the at least one specified data comprises:

x-coordinate data, y-coordinate data, z-coordinate data, heading angle data.

5. The method of claim 1, wherein the road data comprises lane line data.

6. The method of any of claims 1-5, wherein the determining first relative location information between the vehicle and a target road based on the first data set and first map data comprises:

Acquiring road data for a target road from the first map data based on the first data set, wherein the target road is positioned in a second preset area range where the vehicle is currently positioned; and

the first relative position information is determined based on road data acquired from the first map data and the first data set.

7. The method of any of claims 1-5, wherein the determining second relative positional information between the vehicle and a target road based on a target second data set of the plurality of processed second data sets and the second map data comprises:

acquiring road data for a target road from the second map data based on the target second data set, wherein the target road is positioned in a third preset area range where the vehicle is currently positioned; and

the second relative position information is determined based on road data acquired from the second map data and the target second data set.

8. A data processing apparatus for positioning data, comprising:

the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring positioning data for a vehicle, and the positioning data comprises a first data set and a second data set;

A first determination module for determining first relative position information between the vehicle and a target road based on the first data set and first map data;

the first acquisition submodule is used for acquiring a plurality of variable quantity sets;

the processing sub-module is used for processing the second data set according to each variable quantity set in the variable quantity sets to obtain a plurality of processed second data sets which are in one-to-one correspondence with the variable quantity sets;

a second obtaining sub-module, configured to obtain, for each of the plurality of processed second data sets, road data for a specified road from second map data based on the second data set, where the specified road is located in a first preset area range where the vehicle is currently located;

a second determining sub-module for determining a degree of matching between the specified road and the reference road based on the road data for the specified road and the road data for the reference road to obtain a plurality of degrees of matching in one-to-one correspondence with the plurality of processed second data sets; and

a third determining sub-module, configured to use a second data set corresponding to a maximum matching degree of the plurality of matching degrees as a target second data set; and

A first determination sub-module for determining second relative position information between the vehicle and a target road based on a target second data set of the plurality of processed second data sets and the second map data; and

and the comparison module is used for comparing the first relative position information with the second relative position information to obtain a comparison result, and the comparison result indicates the accuracy of the first relative position information.

9. The apparatus of claim 8, wherein each variation set comprises at least one variation, the second data set comprising a plurality of data, the plurality of data comprising at least one specified data, the at least one specified data in one-to-one correspondence with the at least one variation;

the processing sub-module is further configured to add, for each piece of specified data, the specified data to a variation corresponding to the specified data.

10. The apparatus of claim 9, wherein the plurality of data includes at least one position data and at least one speed data for a vehicle;

the apparatus further comprises:

the calculation module is used for carrying out integral calculation based on the at least one position data and the at least one speed data position to obtain at least one gesture data; and

A third determination module for determining the at least one position data and the at least one pose data as the second data set,

11. The apparatus of claim 10, wherein the at least one specified data comprises:

x-coordinate data, y-coordinate data, z-coordinate data, heading angle data.

12. The apparatus of claim 8, wherein the road data comprises lane line data.

13. The apparatus of any of claims 8-12, wherein the first determining module comprises:

a third obtaining sub-module, configured to obtain, based on the first data set, road data for a target road from the first map data, where the target road is located in a second preset area where the vehicle is currently located; and

and a fourth determination sub-module configured to determine the first relative position information based on the first data set and the road data acquired from the first map data.

14. The apparatus of any of claims 8-12, wherein the first determination submodule comprises:

an obtaining unit, configured to obtain, from the second map data, road data for a target road based on the target second data set, where the target road is located in a third preset area range where the vehicle is currently located; and

and a determining unit configured to determine the second relative position information based on the road data acquired from the second map data and the target second data set.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.