CN112966059A

CN112966059A - Data processing method and device for positioning data, electronic equipment and medium

Info

Publication number: CN112966059A
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: 2021-06-15
Anticipated expiration: 2041-03-02
Also published as: CN112966059B

Abstract

The disclosure discloses a data processing method, a data processing device, data processing equipment, data processing media and data processing products 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, the positioning data comprising a first set of data and a second set of data; 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 and 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 technologies, 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 the driving of a vehicle, the vehicle is usually required to be positioned, and particularly for an automatic driving vehicle, the driving of the automatic driving vehicle is required to be controlled through the positioning data. The accuracy of the positioning data has a large impact on the safe driving of the autonomous vehicle. In the related art, when the accuracy of the positioning data is evaluated, the evaluation is usually performed 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, the positioning data comprising a first set of data and a second set of data; determining first relative position information between the vehicle and a target road based on the first set of data 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 and 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 display module and a display 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 to determine first relative position information between the vehicle and a target road based on the first set of data 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; a comparison module, 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.

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 above-described data processing method for positioning data.

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 execute the above-described data processing method for positioning data.

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

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

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

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

fig. 2 schematically shows a flow chart of a data processing method for positioning data according to an embodiment of the present disclosure;

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

fig. 4 schematically shows 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 view of the relative positions of a vehicle and a lane line, in accordance with an embodiment of the present disclosure;

fig. 6 schematically shows 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 used to implement an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those 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 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 is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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.).

An embodiment of the present disclosure provides a data processing method for positioning data, including: positioning data for a vehicle is acquired, the positioning data including a first set of data and a second set of data. 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 shows 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 the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to 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, to name a few.

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

Vehicle 101 may be an autonomous vehicle. The server 103 may be a server that provides various services. For example, the vehicle 101 may transmit the positioning data collected by the sensor to the server 103, and the server 103 may analyze and process the positioning data. The server 103 may be a cloud server, i.e., the server 103 has a cloud computing function. The server 103 may be integrated in the vehicle 101 as part of the 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 embodiment of the present disclosure may be generally executed by the server 103. Accordingly, the data processing device for positioning data provided by the embodiment of the present disclosure may be generally disposed in the server 103. The data processing method for positioning data provided by the embodiments of the present disclosure may also be executed 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 device for positioning data provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 103 and 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 is 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 following describes the data processing method for positioning data according to an exemplary embodiment of the present disclosure with reference to fig. 2 to 5 in conjunction with the system architecture of fig. 1. The data processing for the positioning data of the embodiment of the present disclosure is performed by, for example, the server 103 shown in fig. 1.

Fig. 2 schematically shows 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 of 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 set of data and a second set of data.

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, a vehicle is equipped with various types of sensors, and positioning data is collected by the sensors. The first set of data comprises, for example, positioning data acquired by a first type of sensor and the second set of data comprises, for example, 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 set of data, position data and attitude data of the vehicle indicated by the first set of data 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. First relative position information between the vehicle and the target road is then determined based on the position data and attitude data of the vehicle and road data of the target road indicated by the first data set.

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. Second relative position information between the vehicle and the target road is then determined based on the position data and attitude data of the vehicle and road data of the target road indicated by the second data set.

The data accuracy of the second map data is higher than that of the first map data, for example. The accuracy of the second relative position information is generally higher than that of the first relative position information, and therefore the accuracy evaluation of the first relative position information can be performed 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, and a smaller difference indicates a higher accuracy of the first relative position information.

According to an embodiment of the present disclosure, in order to evaluate the accuracy of obtaining 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 by taking the second relative position information as a reference, so that the evaluation cost is reduced, and the evaluation efficiency is improved.

Fig. 3 schematically shows 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 of 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 a vehicle is acquired

In operation S312, a second set of data 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 velocity data of the vehicle, and the attitude data may be derived based on the position data and the velocity data.

The position data and the posture data of the vehicle are input into the first electronic map, the position and the posture of the vehicle in the first electronic map are obtained, then the target road around the vehicle is determined from the first electronic map, and the road data of the target road around the vehicle is obtained 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 road data acquired from the first map data and the first data set.

In operation S350, an integral calculation is performed based on the at least one position data and the at least one velocity data position to obtain at least one attitude data.

In an embodiment of the present disclosure, the second set of data 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 velocity data includes, for example, velocity, acceleration, angular velocity, angular acceleration.

Wherein, integral calculation is performed based on the position data and the speed data, and the attitude data of the vehicle can be obtained. The at least one attitude data includes, for example, heading angle data, roll angle data, pitch angle data. The at least one position data and the at least one pose data are then considered as a second data set.

In operation S331, a plurality of sets of variation amounts are acquired.

For example, each variation set includes at least one variation, the plurality of data in the second data set includes at least one piece of specifying data, and the at least one piece of specifying data corresponds to the at least one variation one by one.

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

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

Take the example of a set of 2 variables. The first variation set includes, for example, dx _1, dy _1, dz _1, and dyaw _1, the variation dx _1 corresponds to the x-coordinate data, the variation dy _1 corresponds to the y-coordinate data, the variation dz _1 corresponds to the z-coordinate data, and the variation dyaw _1 corresponds to the course angle data.

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 course angle data in the second data set, thereby obtaining a processed second data set corresponding to the first variable set.

Similarly, the second variation set includes, for example, dx _2, dy _2, dz _2, and dyaw _2, and a second processed data set corresponding to the second variation set is obtained through similar calculation.

Because the acquired second data set is influenced by the external environment or the accuracy of the sensor, certain errors exist in the data in the second data set inevitably. And processing the second data set through the plurality of variable sets to obtain a plurality of second data sets so as to determine one with smaller error from the plurality of second data sets as a target second data set, so that the data accuracy is improved, and the positioning evaluation effect is improved.

How to determine the target second data set 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 the plurality of processed second data sets, road data for a specified road, which is located within a first preset area range in which the vehicle is currently located, is acquired from the second map data based on the second data set.

For example, the second map data corresponds to a second electronic map, each second data set indicating position data and posture 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 specified road around the vehicle from the second electronic map so as to obtain the road data of the specified road around the vehicle from the second map data. The specified road may be a road on which the vehicle is currently located indicated by the second electronic map or a road adjacent to the road on which the vehicle is located.

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

Next, the 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 disclosure, the road data in the acquired image data is compared with the road data in each second data set to determine a target second data set from the plurality of 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 of higher accuracy.

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 range in which 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 second target 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 target roads around the vehicle from the second electronic map so as to obtain road data of the target roads 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 vehicle center to the lane line and an angle between the x-axis (or y-axis) and the lane line of the vehicle body coordinate system. Similarly, the second relative position information includes, for example, at least a distance from the vehicle center to the lane line and an angle between the x-axis (or y-axis) and the lane line of the body coordinate system. By comparing the distances in the first relative position and the second relative position and comparing the angles in the first relative position and the second relative position, a comparison result is obtained so as to evaluate the accuracy of the first relative position based on the comparison result.

Fig. 4 schematically shows 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 sensors mounted to the vehicle include, for example, a first type sensor 401 and a second type sensor 402, the first type sensor 401 being, for example, a sensor of a level L3, and the second type sensor 402 being, for example, a sensor of a level L4. 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 of sensor 402 is for example higher than the acquisition accuracy of the first type of 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 velocity 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 a first type of sensor 401 and a 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 data set 403 is acquired by a first type of sensor 401, a second data set 405 is acquired by a second type of sensor 402, and image data 404 is acquired by the first type of sensor 401 and the second type of sensor 402.

The first data set 403 includes, for example, position data and attitude data for the vehicle. Alternatively, the first data set 403 may include position data and velocity data for the vehicle, and the position data and the velocity data are integrated to obtain attitude data, so that the first data set 403 after the integration calculation includes the position data and the attitude data.

Next, the first data set 403 is input into the first electronic map 406, resulting in first relative position information 407a of the vehicle and the target road. 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, the first data 407b may also be obtained from the first data set 403, the first data 407b for example includes position data P _1 and heading angle data yaw _1 of the vehicle, P _1 for example being 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 includes, for example, position data and speed data for the vehicle. The second data set 405 is input into an RTK (Real-time Kinematic) solver 408 for calculation, so as to obtain position data in a UTM (Universal transform Mercator 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 resolver 408 converts into a UTM coordinate system. And the RTK resolver performs integral calculation on the position data and the speed data to obtain attitude data of the vehicle in a vehicle body coordinate system. After calculation by the RTK resolver, a calculated second data set 409 is obtained, and the calculated second data set 409 includes, for example, position data in the UTM coordinate system and attitude data in the vehicle body coordinate system.

The second data set 409 is then processed by the particle filter algorithm 410, for example, by adding a variance to the related 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 second set of target data 412 is input into the second electronic map 413, resulting in second relative position information 414a for 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, where the second data 414b includes, for example, position data P _2 and heading angle data yaw _2 of the vehicle, and P _2 is, 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 into the comparator 415 to be compared, and a comparison result 416 is obtained. The process of comparing the data to be evaluated 407 and the reference data 414 includes comparing the lane line identification ID _1 and the lane line identification ID _2, comparing the distance L _1 and the distance L _2, comparing the included angle α _1 and the included angle α _2, comparing the position data P _1 and the position data P _2, and comparing the course angle data yaw _1 and the course angle data yaw _ 2.

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

Fig. 5 schematically shows a schematic view of the relative positions of a vehicle and a lane line 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 the x-axis and the y-axis. The distance of the vehicle 501 from the lane line includes, for example, a distance L' from the center (origin o) to the first lane line 504, and a distance L ″ from the center (origin o) to the second lane line 505. The angle between the vehicle 501 and the lane line includes, for example, an angle α' between the y-axis and the first lane line 504, and an angle α ″ between the y-axis and 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 shows a block diagram of a data processing apparatus for positioning data according to an embodiment of the present disclosure.

As shown in fig. 6, the data processing apparatus 600 for positioning data of the embodiment of the present disclosure includes, for example, an obtaining module 610, a first determining module 620, a second determining module 630, and a comparing module 640.

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

The first determination module 620 may be configured to determine first relative position information between the vehicle and the target road based on the first set of data and the first map data. According to the 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 again.

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

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

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

According to an embodiment of the present disclosure, each variation set includes at least one variation, the second data set includes a plurality of data, the plurality of data includes at least one piece of specifying data, and the at least one piece of specifying data corresponds to the at least one variation one to one. The processing submodule is also used for adding the specified data to the variable quantity corresponding to the specified data for each 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 attitude data. The third determination module is to treat the at least one position data and the at least one attitude data as a second data set. The at least one position data includes x-coordinate data, y-coordinate data, and z-coordinate data. The at least one attitude data includes heading angle data, roll angle data, pitch angle data.

According to an embodiment of the present disclosure, the at least one piece of specifying 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 determination module for determining a target second data set from the plurality of processed second data sets. The fourth determining module includes: a second obtaining submodule, a second determining submodule and a third determining submodule. The second obtaining sub-module is used for obtaining road data aiming at a specified road from the second map data based on the second data set aiming at each second data set in the plurality of processed second data sets, wherein the specified road is positioned in the first preset area range where the vehicle is positioned currently. The second determining sub-module is configured to determine a matching degree 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, so as to obtain a plurality of matching degrees in one-to-one correspondence with the plurality of processed second data sets. The third determining submodule is used for taking the second data set corresponding to the maximum matching degree in the 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 determining module 620 includes a third obtaining sub-module and a fourth determining sub-module. The third obtaining submodule is used for obtaining road data aiming at a target road from the first map data based on the first data set, and the target road is located in a second preset area range where the vehicle is located currently. A fourth determination submodule configured to determine 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, and the target road is located in a third preset area range where the vehicle is located currently. The determination unit is configured to determine second relative position information based on the road data acquired from the second map data and the target second data set.

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

FIG. 7 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure. The electronic device 700 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable 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 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, 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.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the 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, and so forth. The calculation unit 701 executes the respective methods and processes described above, such as 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 in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the 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 by any other suitable means (e.g. by means of firmware) to perform the data processing method for the positioning data.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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 clients and servers. A client and server are generally 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 understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

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

acquiring positioning data for a vehicle, the positioning data comprising a first set of data and a second set of data;

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

comparing the first relative position information and 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 the second set of data comprises a plurality of data; the determining second relative position information between the vehicle and a target road based on the second set of data and second map data comprises:

acquiring a plurality of variable quantity sets;

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

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.

3. The method of claim 2, wherein each set of variables includes at least one variable, the second set of data includes a plurality of data including at least one piece of specifying data, and the at least one piece of specifying data corresponds to the at least one variable one by one;

the processing the second set of data comprises: for each piece of the specification data, the amount of change corresponding to the specification data is added to the specification data.

4. The method of claim 3, 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 set of data:

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

using the at least one position data and the at least one pose data 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 attitude data comprises course angle data, roll angle data and pitch angle data.

5. The method of claim 4, wherein the at least one designation data includes:

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

6. The method of claim 2, wherein the positioning data further comprises image data comprising road data for a reference road;

the method further comprises the following steps: determining the target second data set from the plurality of processed second data sets;

wherein the determining the target second data set from the plurality of processed second data sets comprises:

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

determining a matching degree 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 matching degrees in one-to-one correspondence with the plurality of processed second data sets; and

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

7. The method of claim 6, wherein the road data comprises lane line data.

8. The method of any of claims 1-7, wherein the determining first relative position information between the vehicle and a target road based on the first set of data and first map data comprises:

acquiring road data aiming at a target road from the first map data based on the first data set, wherein the target road is located in a second preset area range where the vehicle is located currently; and

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

9. The method of any of claims 2-7, wherein the 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 comprises:

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 located in a third preset area range where the vehicle is located currently; and

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

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

an acquisition module to acquire positioning data for a vehicle, the positioning data comprising a first set of data and a second set of data;

a first determination module to determine first relative position information between the vehicle and a target road based on the first set of data 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

a comparison module, 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.

11. The apparatus of claim 10, wherein the second set of data comprises a plurality of data; the second determining module includes:

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

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

a first determining 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.

12. The apparatus of claim 11, wherein each set of variables comprises at least one variable, the second set of data comprises a plurality of data, the plurality of data comprises at least one piece of specifying data, and the at least one piece of specifying data corresponds to the at least one variable one by one;

the processing submodule is also used for adding the specified data to the variable quantity corresponding to the specified data aiming at each specified data.

13. The apparatus of claim 12, wherein the plurality of data comprises at least one position data and at least one speed data for a vehicle;

the device further comprises:

the calculation module is used for performing integral calculation on the basis of the at least one position data and the at least one speed data to obtain at least one attitude data; and

a third determination module for taking the at least one position data and the at least one attitude data as the second data set,

14. The apparatus of claim 13, wherein the at least one designation data comprises:

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

15. The apparatus of claim 11, wherein the positioning data further comprises image data comprising road data for a reference road;

the device further comprises: a fourth determination module to determine the target second data set from the plurality of processed second data sets;

wherein the fourth determining module comprises:

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

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

and the third determining submodule is used for taking the second data set corresponding to the maximum matching degree in the matching degrees as the target second data set.

16. The apparatus of claim 15, wherein the road data comprises lane line data.

17. The apparatus of any of claims 10-16, wherein the first determining means comprises:

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

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

18. The apparatus of any one of claims 11-16, wherein the first determination submodule comprises:

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

a determination 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.

19. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

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 1-9.

20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.

21. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-9.