Data processing method and apparatus, electronic device, computer-readable storage medium, and computer program product

Abstract

The present application provides a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The data processing method is executed by an electronic device, and comprises: determining a plurality of candidate roads for a target object on the basis of positioning data of the target object; acquiring a plurality of pieces of road data of the plurality of candidate roads, the candidate roads being in one-to-one correspondence with the plurality of pieces of road data, and the plurality of pieces of road data including at least one piece of road data of which the precision is greater than a precision threshold; on the basis of the positioning data and the road data of each candidate road, determining a matching degree between the target object and each candidate road, the matching degree being used for indicating a probability that the target object is located on a corresponding candidate road; and on the basis of the matching degree between the target object and each candidate road, determining, from among the plurality of candidate roads, a target road where the target object is located.

Description

Data processing method and device, electronic equipment, computer-readable storage medium, and computer program product

[0001] Cross-reference to Related Applications

[0002] This application is based on and claims priority to Chinese Patent Application No. 2024110021493, filed on July 24, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] The present application relates to the technical field of computer, in particular to the technical field of map matching, and specifically to a data processing method, a data processing device, an electronic device, a computer-readable storage medium, and a computer program product. BACKGROUND

[0004] For a map, the road where the object is located can be determined. In related technologies, the positioning data of the object can be obtained, and based on the positioning data and the position of the road, the road where the object is located is determined. However, the positioning data has the problem of inaccurate positioning, which leads to low accuracy of determining the road where the object is located. SUMMARY

[0005] The embodiments of the present application provide a data processing method, a data processing device, an electronic device, a computer-readable storage medium, and a computer program product, which can improve the matching accuracy between the object and the road.

[0006] In some embodiments, the embodiments of the present application provide a data processing method, which is executed by an electronic device, and the data processing method comprises:

[0007] determining a plurality of candidate roads of a target object according to positioning data of the target object;

[0008] obtaining a plurality of pieces of road data of the plurality of candidate roads, the candidate road and the road data correspond one by one, and at least one piece of road data in the plurality of pieces of road data has a precision greater than a precision threshold;

[0009] determining a matching degree between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads, the matching degree being used to indicate a probability that the target object is located on a corresponding candidate road;

[0010] determining a target road where the target object is located from the plurality of candidate roads according to the matching degree between the target object and each of the candidate roads.

[0011] In some embodiments, the data processing apparatus provided by the embodiments of the present application comprises:

[0012] The processing module is configured to determine a plurality of candidate roads of the target object according to the positioning data of the target object.

[0013] The obtaining module is configured to obtain a plurality of pieces of road data of the plurality of candidate roads, the candidate roads and the road data being in one-to-one correspondence, and at least one piece of road data in the plurality of pieces of road data having a precision greater than a precision threshold.

[0014] The processing module is further configured to determine a matching degree between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads, the matching degree being used to indicate a probability that the target object is located on a corresponding candidate road.

[0015] The processing module is further configured to determine a target road where the target object is located from the plurality of candidate roads according to the matching degree between the target object and each of the candidate roads.

[0016] In some embodiments, the electronic device provided by the embodiments of the present application comprises:

[0017] The memory is configured to store computer executable instructions or computer programs.

[0018] The processor is configured to execute the computer executable instructions or computer programs stored in the memory to implement the data processing method provided by the embodiments of the present application.

[0019] In some embodiments, the computer readable storage medium provided by the embodiments of the present application stores a computer program, and the computer program is read and executed by the processor of the electronic device, so that the electronic device executes the above-mentioned data processing method.

[0020] In some embodiments, the computer program product provided by the embodiments of the present application comprises a computer program stored in a computer readable storage medium. The processor of the electronic device reads the computer program from the computer readable storage medium, and the processor executes the computer program, so that the electronic device executes the above-mentioned data processing method.

[0021] In the embodiments of the present application, the positioning data of the target object can be used to determine a plurality of candidate roads of the target object, a plurality of road data of the plurality of candidate roads can be obtained, the candidate roads and the road data are one-to-one corresponding, at least one road data with a precision greater than a precision threshold is included in the plurality of road data, and then the matching degree between the target object and each candidate road can be determined based on the positioning data and the road data of each candidate road; the matching degree is used to indicate the probability that the target object is located on the corresponding candidate road, and the target road where the target object is located is determined from the plurality of candidate roads according to the matching degree between the target object and each candidate road.

[0022] By using the data processing method provided in the embodiments of the present application, the candidate roads can be determined according to the positioning data of the target object, which is equivalent to the first screening of the roads, and then the target road where the target object is located is determined from the candidate roads in combination with the road data with a precision greater than a precision threshold, which is equivalent to the second screening of the roads, that is, the target road where the target object is located can be determined through the screening of the roads, and the target road is determined based on the road data with a precision greater than a precision threshold, thereby improving the accuracy of determining the target road where the target object is located. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0024] FIG. 1 is a schematic diagram of the architecture of the execution subject of a data processing method provided in an embodiment of the present application;

[0025] FIG. 2 is a schematic diagram of the structure of an electronic map platform provided in an embodiment of the present application;

[0026] FIG. 3 is a schematic diagram of the flow of a data processing method provided in an embodiment of the present application;

[0027] FIG. 4 is a schematic diagram of the mapping relationship between the low-precision road data and the high-precision road data provided in an embodiment of the present application;

[0028] FIG. 5 is a schematic diagram of the matching result of the map coarse matching provided in an embodiment of the present application;

[0029] FIG. 6 is a schematic diagram of the flow of another data processing method provided in an embodiment of the present application;

[0030] FIG. 7 is a schematic diagram of the road represented by the high-precision road data and the low-precision road data provided in an embodiment of the present application;

[0031] FIG. 8 is a schematic diagram of a mapping relationship between an angular velocity and a first matching degree according to an embodiment of the present application;

[0032] FIG. 9 is a schematic diagram of a projection of a target object to a road boundary according to an embodiment of the present application;

[0033] FIG. 10 is a schematic diagram of a calculation of a second matching degree between a target object and a candidate road according to an embodiment of the present application;

[0034] FIG. 11 is a schematic diagram of a structure of a data processing apparatus according to an embodiment of the present application;

[0035] FIG. 12 is a schematic diagram of a structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0037] In order to make the technical solutions provided by the embodiments of the present application more clearly understood, the technical terms related to the embodiments of the present application are introduced first.

[0038] I. Map data

[0039] Map data refers to data containing map information of an electronic map (Electronic Map), and is digital mapping data collected through various channels and methods for electronic map making. An electronic map, i.e., a digital map, is a map stored and consulted in a digital way by using computer technology, is a map generated by electronic equipment control, is a screen map based on digital mapping technology, and is a visual field map. Based on data precision, map data can include standard definition (Standard Definition, SD) and high definition (High Definition, HD) map data. The standard definition map data can also be referred to as SD map data, and the high definition map data can also be referred to as HD map data. The data precision of the high definition map data is higher than that of the standard definition map data. Compared with the standard definition map data, the high definition map data contains more abundant and fine map information.

[0040] The high-precision map data is map data with a data precision greater than a precision threshold, and the precision level of the high-precision map data is higher than that of the standard-precision map data. For ease of understanding, it is illustrated below that the precision level of the high-precision map data can reach a centimeter level. The high-precision map data not only includes road-level data such as spatial three-dimensional shape, function level, road level, traffic relationship, lane number, and traffic regulation information of a road, but also covers detailed information of lane-level such as lane edge line and lane boundary line. In addition, the high-precision road data further includes fixed object information around the road, such as traffic signs, traffic signal lights, guardrails, road edge types, and roadside landmarks. The precision of the standard-precision map data can be a meter level. The standard-precision map data mainly includes basic information of a road such as shape, slope, paving, and direction. The error range of the high-precision map data is smaller, and the error range of the standard-precision map data is larger.

[0041] The map data can include road data, which refers to data containing road information. According to data precision, the road data can include SD road data (i.e., standard-precision road data, or can be referred to as ordinary road data) and HD road data (i.e., high-precision road data). The standard-precision road data can be understood as road data in the standard-precision map data, and the high-precision road data can be understood as road data in the high-precision map data. The data precision of the high-precision road data is higher than that of the standard-precision road data. The standard-precision road data can be used to record basic attributes of a road, for example, basic information such as road length, lane number, direction, and topology. Compared with the standard-precision road data, the high-precision road data contains more abundant and fine road information, for example, the road information recorded by the high-precision road data can include road boundary information, lane line equation / line point coordinates, lane type, lane speed limit, lane marking type, power pole coordinates, direction sign position, camera position, and traffic light position.

[0042] Between the standard-precision road data and the high-precision road data, there is also a kind of lane-level data. The lane-level data can be considered as a data specification richer than the road information recorded by the standard-precision map data but not reaching the standard of the high-precision road data. Compared with the standard-precision road data, the lane-level data records road information containing lane-level information of a road, for example, lane line equation / line point coordinates, lane type, lane speed limit, lane marking type, and lane topology information.

[0043] II. Positioning data

[0044] The positioning data refers to data containing positioning information. The positioning information contained in the positioning data can include positioning coordinates and speed information. The positioning coordinates can include absolute position coordinates (for example, latitude and longitude coordinates) or relative position coordinates (for example, three-dimensional coordinates relative to the geocentric coordinate system). The speed information can include a speed value (i.e., speed size) and a speed direction.

[0045] The positioning data can be obtained based on a positioning system. The positioning system refers to a GNSS (Global Navigation Satellite System, or can be referred to as a global satellite navigation system), which is an air-based radio navigation positioning system capable of providing all-weather positioning coordinates, speed information and time information at any location on the earth's surface or near space, and includes one or more satellite constellations and the enhancement system required to support a specific work. The positioning system can include Beidou satellite navigation system (BDS), global positioning system (GPS), GLONASS satellite navigation system (GLONASS), and Galileo satellite navigation system (GALILEO), etc.

[0046] III. Map matching

[0047] Map matching refers to a technology of matching a road for an object in a map, and the road matched for the object refers to a road in which the object is located in the map. In the map matching technology, the matching between the object and the road can be performed based on the positioning data of the object and the road data of each road, and the matching degree between the object and the road can be determined; the matching degree between the object and the road can be used to indicate the probability of the object being located in the road, and the road with the highest matching degree (i.e., the largest probability) can be determined as the road in which the object is located based on the matching degrees between the object and each road.

[0048] The object refers to an object using an electronic map platform, for example, a user, a vehicle, a robot, etc.; the object is abstracted as a positioning point in the electronic map. The electronic map platform refers to a platform providing map services based on an electronic map, and the map services can include, for example, visual display of the electronic map, navigation, location query, and real-time road condition broadcast, etc. The map matching technology can be used in the electronic map platform to determine the road in which the object using the electronic map platform is located in real time, and the electronic map platform can provide map services based on the road in which the object is located.

[0049] Based on the introduction of the above technical terms, map matching can use low-precision positioning data and high-precision road data to perform low-precision map matching between the object and the road, but the accuracy of low-precision map matching is relatively low, for example, low-precision map matching is prone to errors in scenarios with parallel roads (e.g., main and auxiliary roads), or cannot determine the specific location with high probability (i.e., cannot accurately determine the road in which the object is located).

[0050] Based on this, the embodiment of the present application provides a data processing method. The data processing method provided by the embodiment of the present application can determine a plurality of candidate roads of a target object according to positioning data of the target object, can obtain a plurality of road data of the plurality of candidate roads, the candidate roads and the road data are one-to-one corresponding, at least one of the plurality of road data has a precision greater than a precision threshold, and then the matching degree between the target object and each candidate road can be determined based on the positioning data and the road data of each candidate road. The matching degree is used to indicate the probability that the target object is located on the corresponding candidate road. The target road where the target object is located is determined from the plurality of candidate roads according to the matching degree between the target object and each candidate road.

[0051] Through the data processing method provided by the embodiment of the present application, the candidate road can be determined according to the positioning data of the target object first, which is equivalent to the first screening of the road. Then the target road where the target object is located is determined from the candidate road in combination with the road data with a precision greater than the precision threshold, which is equivalent to the second screening of the road. That is, the target road where the target object is located can be determined through the screening of the road, and the target road is determined based on the road data with a precision greater than the precision threshold, thereby improving the accuracy of determining the target road where the target object is located.

[0052] The data processing method can use the positioning data of the object and the high-precision road data of each road around the object to perform low-precision map matching between the object and the road, and quickly determine the candidate road where the target object is located from each road around the object. In the case that the number of candidate roads is greater than one, there is high-precision positioning data, and at least one candidate road has high-precision road data, high-precision map matching can be performed between the object and the candidate road using the high-precision positioning data of the object and the high-precision road data of the candidate road, and the matching degree between the object and the candidate road can be obtained. The road where the object is located can be determined from each candidate road based on the matching degree between the object and each candidate road.

[0053] As can be seen, the embodiment of the present application performs high-precision map matching between the object and the candidate road based on high-precision positioning data and high-precision road data, which can improve the matching accuracy between the object and the road, especially in the parallel road scene. Moreover, the embodiment of the present application only requires that there is one candidate road in the current region where the object is located covered by high-precision road data, and the remaining candidate roads not covered by high-precision road data can be appropriately converted to high-precision road data for map matching, so that the embodiment of the present application can work without requiring the object to be completely in the region covered by high-precision map data, which can increase the effective scene and improve the recall ability.

[0054] It should be noted that in the related data collection and processing (for example, the collection and processing of the positioning data of the object) in the embodiments of the present application, the informed consent or separate consent of the personal information subject (or the legal basis) should be obtained in strict accordance with the requirements of the relevant laws and regulations, and the subsequent data use and processing behavior should be carried out within the scope of authorization of the laws and regulations and the personal information subject.

[0055] Next, the execution subject of the data processing method is introduced.

[0056] The data processing method provided in the embodiments of the present application can be executed by an electronic device. As shown in FIG. 1, FIG. 1 is an architecture schematic diagram of an execution subject of a data processing method provided in the embodiments of the present application, and the electronic device can include a map data module, a positioning module, a map coarse matching module, and a map fine matching module. Wherein: ① the positioning module can be configured to obtain positioning data of a target object. ② the map data module can be configured to obtain local map data of a certain range (i.e., a preset range) around the current position of the target object based on the positioning data of the target object; the local map data can include local label-precise map data and local high-precision map data, the label-precise map data can include label-precise road data of roads within a certain range around the current position of the target object, and the high-precision map data can include high-precision road data of roads within a certain range around the current position of the target object; in addition to the label-precise road data and the high-precision road data, a mapping relationship (a mapping relationship between SD and HD) between the label-precise road data and the high-precision road data can also be included, and the mapping relationship can be used to express whether the label-precise road data of the current road has corresponding high-precision road data and which high-precision road data.

[0057] ③ the map coarse matching module can be configured to perform low-precision map matching between the object and the road based on the positioning data of the target object and the label-precise road data of each road around the target object, and quickly determine a candidate road where the target object can be located. ④ the map fine matching module can be configured to perform high-precision map matching between the target object and each candidate road based on the high-precision positioning data of the object and the high-precision road data of each candidate road of the target object, obtain the matching degree between the target object and each candidate road, and determine the target road where the target object is located from each candidate road based on the matching degree between the target object and each candidate road.

[0058] The electronic device can be a terminal, and the terminal can run an electronic map platform. The electronic map platform running in the terminal can include the map data module, the positioning module, the map coarse matching module, and the map fine matching module. Alternatively, the electronic device can be a server, and the server can run an electronic map platform. The electronic map platform running in the server can include the map data module, the positioning module, the map coarse matching module, and the map fine matching module. Alternatively, the electronic device can be an electronic map platform composed of a terminal and a server. The terminal can include the positioning module, and the server can include the map data module, the map coarse matching module, and the map fine matching module.

[0059] The electronic map platform composed of a terminal and a server (i.e., a data processing system) is described below. As shown in FIG. 2, the electronic map platform can include a terminal 201 and a server 202. The terminal 201 and the server 202 can establish a direct communication connection through wired communication or an indirect communication connection through wireless communication. The connection between the terminal 201 and the server 202 is not limited in the embodiments of the present application.

[0060] For the terminal 201, the terminal 201 can run a front end of an electronic map platform. The front end of the electronic map platform can include, but is not limited to, an electronic map application, an electronic map applet, an electronic map software, and an electronic map web (World Wide Web) application. The electronic map platform shown in FIG. 2 is described by taking the terminal 201 as a vehicle-mounted terminal in a vehicle as an example. The terminal can also be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart voice interaction device, a smart watch, a smart home appliance, and an aircraft, etc. that runs a front end of an electronic map platform, but is not limited thereto.

[0061] For the server 202, the server 202 can be a background of an electronic map platform. The server 202 can be a single physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, etc. basic cloud computing services, which are described herein.

[0062] In the electronic map platform composed of the terminal 201 and the server 202, the flow of the data processing method can include: when the map service provided by the front end of the electronic map platform involves map matching, the front end of the electronic map platform can send the positioning data of the target object at the current time to the server 202 through the terminal 201; the server 202 can determine the target road where the target object is located at the current time based on the data processing method provided in the embodiments of the present application, can generate a matching result based on the target road where the target object is located at the current time, and send the matching result to the terminal 201; the front end of the electronic map platform in the terminal 201 can provide the map service based on the matching result (i.e., the target road where the target object is located at the current time).

[0063] The map service can include one or more of map rendering, real-time navigation, path planning, traffic information, public transportation navigation, location search, location sharing, traffic flow analysis, and travel in tourism, thereby further facilitating the production and life of the user.

[0064] It can be understood that at each time when the map service provided by the front end of the electronic map platform involves map matching, the positioning data of the target object at the corresponding time can be sent, the target road where the target object is located at the corresponding time can be determined through map matching, and the map service can be provided based on the target road where the target object is located at the corresponding time according to the flow at the current time of the above data processing method, so that the matching accuracy between the object and the road can be improved in the process of providing the map service, the accuracy of the map service can be improved, more accurate map service can be provided, and the user experience can be improved.

[0065] The electronic map platform described in the embodiments of the present application is used to more clearly illustrate the technical solutions of the embodiments of the present application, and does not constitute a limitation on the technical solutions provided by the embodiments of the present application. It can be known by those skilled in the art that the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems as the system architecture evolves and new business scenarios appear.

[0066] Next, the application scenarios of the data processing method are introduced.

[0067] The data processing method can be applied to the vehicle navigation scene in the field of autonomous driving, or can be applied to the vehicle navigation scene in the field of active driving, and can be applied to the yaw identification scene, the voice broadcast scene, and the navigation route calculation scene in the vehicle navigation scene, etc.

[0068] The automatic driving refers to that the vehicle is controlled to travel by the automatic driving system on the vehicle, and the active driving scenario refers to that the vehicle is controlled to travel by the human driver. In the deviation identification scenario, after the actual road (i.e., the target road) where the target object is located at the current moment is obtained based on the data processing method provided in the embodiments of the present application, the actual road where the target object is located at the current moment can be compared with the road planned for the object at the current moment in the navigation route. If the actual road is inconsistent with the planned road, the identification result indicating the deviation can be generated, and further, the deviation prompt and the re-planned route can be performed. If the actual road is consistent with the planned road, the identification result indicating no deviation can be generated.

[0069] In the voice broadcast scenario, after the road where the object is located at the current moment is obtained based on the data processing method provided in the embodiments of the present application, the road where the object is located at the current moment can be broadcasted. In some embodiments, the name of the road where the target object is located at the current moment can be broadcasted when the name of the target road is inconsistent with the name of the road where the object is located at the previous moment. In the navigation route calculation scenario, after the road where the target object is located at the current moment is obtained based on the data processing method provided in the embodiments of the present application, the route of the target object can be planned based on the road where the target object is located at the current moment and the terminal position information, to obtain the route from the road where the target object is located at the current moment to the terminal.

[0070] When the data processing method provided in the embodiments of the present application is applied to the vehicle navigation scenarios (for example, the deviation identification scenario, the voice broadcast scenario, and the navigation route calculation scenario) described above, based on the improvement of the matching accuracy between the object and the road, the corresponding navigation and positioning experience adjustment of the vehicle navigation scenarios described above can be given, the navigation accuracy of the vehicle navigation scenarios is improved, and the navigation experience is improved. With the continuous increase of the number of cars, the application of map navigation is also more and more widely used. In the field of map navigation, accurate position positioning is very important, which can give accurate and reasonable driving guidance, reduce the cost of wrong road, and bring more comfortable driving experience.

[0071] The data processing method provided in the embodiments of the present application can also be applied to the field of robots, and the target road where the target object (i.e., the robot) is located can be determined in real time, and then the path planning and obstacle detection are combined to realize the tasks of transporting goods and cleaning.

[0072] The data processing method is introduced below.

[0073] The embodiment of the present application provides a data processing method, and the content introduced by the data processing method includes the process of first map matching (i.e. the process of coarse map matching) and the process of second map matching (i.e. fine map matching). As shown in FIG. 3, FIG. 3 is a flowchart of a data processing method provided by the embodiment of the present application, and the data processing method can include but is not limited to the following steps S301-S304.

[0074] S301, determining a plurality of candidate roads of a target object according to positioning data of the target object.

[0075] In step S301, the positioning data of the target object refers to first positioning data of the target object at a current moment, and specifically refers to data containing positioning information of the target object at the current moment.

[0076] The first positioning data can be obtained based on a positioning rule (i.e. a positioning technology), for example, the first positioning data can be GNSS positioning data, PPP (Precise Point Positioning) positioning data or RTK (Real-time kinematic) positioning data, etc. The GNSS positioning data refers to positioning data obtained based on a GNSS positioning technology, the PPP positioning data refers to positioning data obtained based on a PPP positioning technology, and the RTK positioning data refers to positioning data obtained based on an RTK positioning technology.

[0077] The RTK positioning technology can also be referred to as a carrier phase difference technology, which is a difference method for processing carrier phase observation of two measurement stations in real time. The carrier phase collected by the reference station is sent to the user receiver to solve the difference and obtain the coordinates. This is a new commonly used satellite positioning measurement method. The previous static, fast static and dynamic measurement needs to be solved after the event to obtain centimeter-level accuracy. RTK is a measurement method that can obtain real-time centimeter-level positioning accuracy in the field. It uses a carrier phase dynamic real-time difference method, which is a major milestone in GPS application. Its emergence brings new measurement principles and methods for engineering lofting, topographic mapping and various control measurements, and greatly improves the work efficiency.

[0078] Alternatively, the first positioning data can be fusion positioning data obtained by fusing the positioning data obtained based on the positioning rule (i.e. the positioning technology) and the positioning data obtained based on the sensor. For example, GNSS positioning data and sensor signals can be fused to obtain a final trajectory calculation point, and the positioning data of the calculation point is determined as the first positioning data.

[0079] The positioning data of the target object can include positioning coordinates of the target object, speed information (including a speed value (i.e., a speed magnitude) and a speed direction) of the target object, and a current positioning time (i.e., a current time).

[0080] In some embodiments, a plurality of roads within a preset range of the target object can be acquired, and initial road data (i.e., first road data) of each road can be acquired. Based on the positioning data of the target object and the initial road data of each road, a matching degree between the target object and each road can be determined. According to the matching degree between the target object and each road, a plurality of candidate roads of the target object can be determined from the plurality of roads, where the number of the candidate roads is less than or equal to the number of the roads.

[0081] Before the first map matching is performed, the first road data (i.e., the initial road data) of each road within a preset range (i.e., a preset range) of the target object can be acquired. The first road data refers to the tag-precise road data in the tag-precise map data.

[0082] In some embodiments, according to the positioning data of the target object, local map data within a certain range (i.e., a preset range, which can be a range extending outward from the current position as a center point) around the current position of the target object can be acquired. The local map data can include local tag-precise map data and local high-precision map data. The tag-precise map data can include tag-precise road data of roads within a certain range around the current position of the target object. The high-precision map data can include high-precision road data of roads within a certain range around the current position of the target object. The certain range here can refer to a circular range or a rectangular range with the target object as a center point, which is not limited in the embodiments of the present application.

[0083] The high-precision road data has a higher precision level than the tag-precise road data, and the elements included in the high-precision road data are more accurate. The tag-precise road data can include the length of a road, the number of lanes / width of a lane, road connectivity, road shape point representation, road attributes (e.g., viaduct, ramp, main / side road, and tunnel, etc.), road level (e.g., expressway, provincial road, and rural road, etc.), etc. The high-precision road data has more accurate and rich road element expression, including the range of a road surface (i.e., road boundary information), the position of each lane, lane line type, lane line color, lane arrow, etc. That is, compared with the tag-precise road data, the high-precision road data has road boundary information, and the road width can be easily calculated, while the tag-precise road data does not have road boundary information, and it is difficult to obtain the road width.

[0084] In addition to the standard-precision road data and the high-precision road data, a mapping relationship (a mapping relationship of SD-HD) between the standard-precision road data and the high-precision road data can be obtained. The mapping relationship can be used to express whether the standard-precision road data of a current road has corresponding high-precision road data and which high-precision road data.

[0085] The mapping relationship between the standard-precision road data and the high-precision road data is shown in FIG. 4. FIG. 4 is a schematic diagram of a mapping relationship between standard-precision road data and high-precision road data according to an embodiment of the present application. The standard-precision road data abstracts a road into a line segment (the road represented by the standard-precision road data can be referred to as a Link), and the high-precision road data can accurately express the surface information of the road (the road represented by the high-precision road data can be referred to as a LaneGroup (a lane group), which can include one or more lanes (Lanes)).

[0086] The LaneGroup 1 can include the Lane 1, the Lane 2, and the Lane 3. The LaneGroup 2 can include the Lane 4, the Lane 5, and the Lane 6. The LaneGroup 3 can include the Lane 7. The LaneGroup 4 can include the Lane 8, the Lane 9, and the Lane 10. The LaneGroup 5 can include the Lane 11. The LaneGroup 6 can include the Lane 12, the Lane 13, and the Lane 14.

[0087] In some embodiments, the positioning data is positioning data of the target object at a current time. According to the positioning data and the initial road data of each road, the distance between the target object and each road and the included angle between the target object and each road can be determined. That is, for each road, the distance between the target object and the road and the included angle between the target object and the road can be determined.

[0088] According to the distance between the target object and each road and the included angle between the target object and each road, the observation probability of the target object at the current time is determined. That is, for each road, the observation probability of the target object at the current time can be determined based on the distance corresponding to the road (the distance between the target object and the road) and the included angle corresponding to the road (the included angle between the target object and the road). Further, the transition probability is determined according to the connection relationship between the roads, and the matching degree between the target object and each road is determined based on the transition probability and the observation probability of the target object at the current time.

[0089] The positioning data can include longitude and latitude coordinates, and the initial road data can include geometric information of each road in the road network, and for each road, can include the starting point of the road, the end point of the road, and the direction of the road.

[0090] For each road, the vertical distance from the positioning point of the target object to the road can be calculated. And the angle between the positioning point and the direction of the road can be calculated. The observation probability is used to represent the probability of the target object being located on a certain road, which can be determined according to the distance between the target object and each road and the angle between the target object and each road. The closer the distance and the smaller the angle, the higher the observation probability.

[0091] In some embodiments, Gaussian distribution can be used to model the probability distribution of the distance and the angle, and the standard deviation of the distance and the standard deviation of the angle can be calculated respectively, so as to adjust the sensitivity of the observation probability by using the standard deviation of the distance and the standard deviation of the angle.

[0092] The transition probability is determined according to the connection relationship between the roads, wherein the transition probability is used to represent the probability of transferring from one road to another road, and the transition probability can be determined based on the connectivity between any two roads in the plurality of roads. If any two roads in the plurality of roads are connected, the transition probability is higher, and if any two roads in the plurality of roads are not connected, the transition probability is lower.

[0093] The observation probability of the target object at the previous time is obtained, and the matching degree between the target object and each road is determined based on the observation probability of the target object at the previous time, the transition probability, and the observation probability of the target object at the current time.

[0094] In some embodiments, the matching degree between the target object and each road is determined based on the observation probability and the transition probability using the Viterbi algorithm of the hidden Markov model. The Viterbi algorithm finds the most likely multiple candidate paths through dynamic programming, and the specific steps are as follows: for each positioning point, the observation probability on each road is calculated. For the first positioning point, the observation probability can be used as the matching degree, and for each subsequent positioning point after the first positioning end point, the transition probability from the previous positioning point to the current positioning point is calculated, and then the first product of the transition probability and the matching degree of the previous positioning point is calculated, and the second product of the observation probability of the current positioning point and the first product is calculated, and the second product is the matching degree. Until the positioning point corresponding to the current time, after obtaining the second product, the path with the second product greater than the probability threshold (i.e., the matching degree threshold) is selected as the candidate path, and the number of positioning points can be two or more, which can be determined according to actual use requirements.

[0095] In step S301, the first map matching refers to a rough map matching, which is a low-precision map matching. The process of the first map matching can include: ① determining the distance between the target object and each road and the included angle between the target object and each road according to the positioning data and the first road data of each road; ② determining the observation probability of the target object at the current time according to the distance between the target object and each road and the included angle between the target object and each road.

[0096] ③ determining the transition probability from the last time to the current time according to the connection relationship between the initial road and each road, wherein the initial road is the road where the target object was located at the last time of the current time; ④ determining the matching degree between the target object and each road according to the observation probability of the target object at the last time, the transition probability and the observation probability of the target object at the current time; ⑤ determining the candidate road of the target object according to the matching degree between the target object and each road.

[0097] In the process of the first map matching, any one of the roads around the target object can be represented as a reference road, and the reference road is the road where the target object is located at the current time. Taking the reference road as an example, the process of step ③ of determining the transition probability from the last time to the current time can include: if the road corresponding to the last time (i.e., the road where the target object was located at the last time) and the reference road do not have a connection relationship, the target transition probability (which can be a probability preset according to an empirical value) can be determined as the transition probability from the last time to the current time; if the road corresponding to the last time and the reference road have a connection relationship, the transition probability from the last time to the current time can be determined according to the difference between the connection road included angle corresponding to the connection relationship and the angle detected by the sensor of the target object.

[0098] In the process of the first map matching, after step ⑤ of determining the matching degree between the target object and each road, the matching degree between the target object and each road refers to the matching probability between the target object and each road. The road with a corresponding matching degree greater than a preset threshold can be determined as the candidate road of the target object.

[0099] The above first map matching process is a map matching process based on a Hidden Markov Model (HMM). The observation probability (or can be referred to as the emission probability) can be defined by the distance and the included angle between the target object and each road. The closer the distance between the roads, the greater the observation probability. The farther the distance between the roads, the smaller the observation probability. Similarly, the greater the included angle between the target object and the road, the smaller the observation probability. The smaller the included angle between the target object and the road, the greater the observation probability.

[0100] The transition probability can consider the connectivity relationship between roads, and the consistency of the connected road included angle and the angle change detected by the sensor. The more consistent the connected road included angle and the angle detected by the sensor, the greater the transition probability, and vice versa. That is, the less consistent the connected road included angle and the angle detected by the sensor, the smaller the transition probability.

[0101] After the observation probability and the transition probability are determined, the Viterbi algorithm can be used to obtain the matching probability between the target object and the road. The matching result of the first map matching can be represented as candidates, candidates.size() represents the number of current possible roads, and candidate[i].prob represents the matching probability between the target object and the ith road (that is, the matching degree between the target object and the road obtained by the first map matching).

[0102] An exemplary matching result of the first map matching process is shown in FIG. 5, which is a schematic diagram of a matching result of a map coarse matching according to an embodiment of the present application. At t1, it can be determined that the target object is located on candidate road 1. At this time, there is only one candidate road, and the matching probability (the matching degree between the candidate road and the target object) of the candidate road is 1.0. Subsequently, at t2, parallel road bifurcations are encountered, and the target object is located between the two parallel roads. The map matching has two candidate roads. The matching probability (the matching degree between candidate road 2 and the target object) of one of the candidate roads (candidate road 2) is 0.6 (candidate[2].prob = 0.6), and the matching probability (the matching degree between candidate road 1 and the target object) of the other candidate road (candidate road 1) is 0.4 (candidate[2].prob = 0.4).

[0103] S302, obtaining multiple pieces of road data of multiple candidate roads.

[0104] In some embodiments, the road data is second road data, and the candidate roads and the second road data have a one-to-one correspondence. That is, there is a one-to-one correspondence between each candidate road and the second road data.

[0105] In some embodiments, for each candidate road, if there is road data corresponding to the initial road data and having a precision greater than a precision threshold, the road data corresponding to the initial road data is determined as the road data of the candidate road.

[0106] The initial road data is the first road data, the first road data is the high-precision road data, and based on the mapping relationship between the high-precision road data and the high-precision road data, it is determined whether there is high-precision road data corresponding to the first road data. If there is, the road data with a precision greater than the precision threshold and corresponding to the initial road data can be determined as the road data of the candidate road (i.e., the second road data, that is, the high-precision road data).

[0107] If there is no road data corresponding to the initial road data, the road width of the candidate road is determined according to the lane width and the number of lanes in the initial road data of the candidate road, and the road data of the candidate road is determined according to the road width of the candidate road and the initial road data of the candidate road. In this way, the road data of each candidate road can be obtained accurately, and at least one road data with a precision greater than the precision threshold is included in the multiple road data, that is, at least one high-precision road data is included. The high-precision road data is used for subsequent determination of the target road, which can improve the accuracy of determining the target road.

[0108] S303, based on the positioning data and the road data of each candidate road, determining the matching degree between the target object and each candidate road.

[0109] The matching degree is used to indicate the probability that the target object is located on the corresponding candidate road. In some embodiments, the second map matching can be performed between the target object and each candidate road according to the positioning data and the second road data of each candidate road to obtain the matching degree between the target object and each candidate road.

[0110] Steps S302-S303 involve the process of second map matching, which can be triggered when the matching condition is met. The matching condition can include the following (1)-(3):

[0111] (1) The number of candidate roads obtained by the first map matching is greater than one;

[0112] (2) Among the candidate roads of the target object, at least one candidate road has high-precision map data;

[0113] (3) The detection result of the positioning accuracy detection of the positioning data indicates that the positioning data of the target object meets the positioning accuracy condition.

[0114] In some embodiments, before determining the matching degree between the target object and each candidate road based on the positioning data and the road data of each candidate road, if the number of the candidate roads is more than one, the road data of the candidate roads can be subjected to road data detection, if the detection result of the road data detection indicates that at least one of the road data has a precision greater than the precision threshold, the positioning data can be subjected to positioning precision detection, if the detection result of the positioning precision detection indicates that the positioning data satisfies the positioning precision condition, the determination of the matching degree between the target object and each candidate road based on the positioning data and the road data of each candidate road is triggered.

[0115] For the matching condition (3), the process of the positioning precision detection on the positioning data can include: for the positioning data of the target object obtained by using the target positioning rule, the solving state of the positioning data can be detected in the accurate solving state set corresponding to the target positioning rule, which is equivalent to obtaining the calculation state of the positioning data.

[0116] If the solving state of the positioning data is the accurate solving state in the accurate solving state set, the positioning precision information of the positioning data can be compared with the precision threshold; which is equivalent to if the solving state of the positioning data is the preset solving state, the positioning precision information of the positioning data is compared with the positioning precision threshold. If the positioning precision information is less than the positioning precision threshold, the detection result of the positioning precision detection can be generated, and the detection result of the positioning precision detection can indicate that the positioning data satisfies the positioning precision condition.

[0117] If the solving state of the positioning data is not the accurate solving state in the accurate solving state set, the detection result of the positioning precision detection can be generated, and the detection result of the positioning precision detection can indicate that the positioning data does not satisfy the positioning precision condition; if the positioning precision information is greater than or equal to the positioning precision threshold, the detection result of the positioning precision detection can be generated, and the detection result of the positioning precision detection can indicate that the positioning data does not satisfy the positioning precision condition.

[0118] For example, the positioning data of the target object is obtained by using the RTK positioning rule, the RTK positioning rule includes a plurality of solving states, and the precision corresponding to each solving state is different. For example, the solving states included in the RTK positioning rule can include fixed solution, floating solution, single-point solution, pseudo-range solution, no solution and known, the fixed solution can reach centimeter level precision, the floating solution can generally reach 10 centimeter precision, the precision of the single-point solution is usually several meters, the precision of the pseudo-range solution is similar to that of the single-point solution, the precision of the no solution is very low, and the known is set incorrectly and the precision is unknown.

[0119] The positioning accuracy information of the positioning data can be expressed by CEP (Circular Error Probable), which is defined as a circle with a target as the center and a radius of r. There is a certain probability that the actual positioning point will fall within this circle. Generally, it is expressed as CEPXX, where XX is a specific number representing the probability. For example, the CEP95 of the positioning accuracy information is 5m, indicating that there is a 95% probability that the actual positioning point will be within the circle with a radius of 5m centered at the given positioning point.

[0120] The accurate solution states in the accurate solution state set may include fixed solutions, floating point solutions, and pseudo-range solutions. If the solution state of the positioning data is any one of the fixed solution, the floating point solution, and the pseudo-range solution, and the positioning accuracy information of the positioning data is less than the positioning accuracy threshold th1 (for example, 3.0m or 5.0m, etc.), it can be determined that the positioning data meets the positioning accuracy condition.

[0121] Alternatively, for the matching condition (3), the process of positioning accuracy detection on the positioning data can include: obtaining the positioning accuracy information of the electronic map platform, and if the positioning accuracy information of the electronic map platform is less than the positioning accuracy threshold, a detection result of the positioning accuracy detection can be generated, which can indicate that the positioning data meets the positioning accuracy condition.

[0122] If the positioning accuracy information of the electronic map platform is greater than or equal to the positioning accuracy threshold, a detection result of the positioning accuracy detection can be generated, which can indicate that the positioning data does not meet the positioning accuracy condition.

[0123] The positioning accuracy information of the electronic map platform refers to the positioning error of the electronic map platform. The positioning accuracy information of the electronic map platform can be obtained by using a large number of test positioning points, the positioning data of which is known, which can be referred to as labeled positioning data. The actual positioning data of each test positioning point can be obtained by the electronic map platform. The positioning error of each test positioning point can be determined according to the difference between the actual positioning data and the labeled positioning data of each test positioning point. The positioning accuracy information of the electronic map platform can be determined according to the positioning errors of the test positioning points, for example, the positioning accuracy information of the electronic map platform can be the average of the positioning errors of the test positioning points. It can be seen that the restriction of the matching condition (1) can make the positioning data used for the second map matching be high-precision positioning data.

[0124] The above three matching conditions are met to trigger the second map matching, otherwise the second map matching is not performed. That is, the above three matching conditions are the premise of executing steps S302-S303. When the three matching conditions are met, steps S302-S303 are executed.

[0125] First, the case that satisfies the matching condition is introduced: if the number of candidate roads obtained by the first map matching is greater than one, high-precision road data detection can be performed on multiple candidate roads; if the detection result of the high-precision road data detection indicates that at least one of the multiple candidate roads has high-precision road data, positioning accuracy detection can be performed on the positioning data; if the detection result of the positioning accuracy detection indicates that the positioning data satisfies the positioning accuracy condition, step S302 (i.e., obtaining multiple pieces of road data of multiple candidate roads) is triggered, and step S303 (i.e., performing second map matching) is triggered.

[0126] When the matching condition is satisfied, in step S302, the road data of the candidate road is the second road data, and multiple pieces of second road data of multiple candidate roads can be obtained. For the same candidate road, the data accuracy of the second road data of the candidate road is higher than that of the first road data of the candidate road.

[0127] In some embodiments, the process of obtaining the second road data of the multiple candidate roads can include: any candidate road in the multiple candidate roads can be represented as the ith candidate road. Taking the ith candidate road as an example, if the ith candidate road has corresponding high-precision road data in the high-precision map data, the high-precision road data of the ith candidate road can be determined as the second road data of the ith candidate road; if the ith candidate road does not have corresponding high-precision road data in the high-precision map data, the first road data (i.e., the standard-precision road data) of the ith candidate road can be converted to obtain road data with higher data accuracy than the first road data as the second road data of the ith candidate road.

[0128] The way of data conversion is introduced here. As described in the foregoing, the road represented by the high-precision road data has a road surface and road boundary information, and the road width can be calculated through the road boundary information. The road represented by the standard-precision road data is abstracted as a line segment and does not have a road width. Therefore, the data conversion refers to converting the standard-precision road data without a road width into standard-precision road data with a road width.

[0129] The data conversion process can include: determining a road width of the ith candidate road according to the lane width and the number of lanes in the first road data of the ith candidate road; and determining second road data of the ith candidate road according to the road width of the ith candidate road and the first road data (i.e., the high-precision road data) of the ith candidate road. As can be seen, the embodiments of the present application only require that there is a candidate road covered by high-precision road data in the current area where the object is located, and the rest of the candidate roads not covered by high-precision road data can be subjected to data conversion on the high-precision road data of the candidate road to have a road width, thereby becoming high-precision road data for second map matching. This makes the embodiments of the present application not require that the object be completely in an area covered by high-precision map data to work, and can increase the effective scenarios and improve the recall ability.

[0130] When the matching condition is met, in step S303, the second map matching refers to map precision matching, which is high-precision map matching. The positioning data used in the second map matching process refers to the second positioning data of the target object at the current time, and the second positioning data is positioning data satisfying the positioning accuracy condition. The first positioning data can not be required to satisfy the positioning accuracy condition, or can also be required to satisfy the positioning accuracy condition.

[0131] The first positioning data and the second positioning data can be the same positioning data, or the first positioning data and the second positioning data can be different positioning data. For example, the first positioning data can be GNSS positioning data, and the second positioning data can be RTK positioning data satisfying the positioning accuracy condition, or the first positioning data and the second positioning data can both be RTK positioning data satisfying the positioning accuracy condition.

[0132] In some embodiments, the positioning data includes position information of the target object and speed information of the target object; the road data of each candidate road includes position information of the candidate road; and for each candidate road, a first matching degree between the target object and the candidate road is determined based on the position information of the candidate road and the speed information of the target object, and the first matching degree refers to a matching degree between a motion angle of the target object when the target object moves according to the speed information and a road angle indicated by the position information of the candidate road.

[0133] A second matching degree between the target object and the candidate road is determined based on the position information of the target object and the position information of the candidate road, and the second matching degree refers to a position matching degree between the target object and the candidate road. A matching degree between the target object and the candidate road is determined based on the first matching degree and the second matching degree.

[0134] Taking the ith candidate road as an example, the process of the second map matching can include: the positioning data can include position information of the target object and speed information of the target object; the second road data of the ith candidate road can include position information of the ith candidate road; a first matching degree between the target object and the ith candidate road can be determined according to the position information of the ith candidate road and the speed information of the target object, the first matching degree being a matching degree between a motion angle of the target object when the target object moves according to the speed information and a road angle indicated by the position information of the ith candidate road; a second matching degree between the target object and the ith candidate road can be determined according to the position information of the target object and the position information of the ith candidate road, the second matching degree being a position matching degree between the target object and the ith candidate road; and a matching degree between the target object and the ith candidate road can be determined according to the first matching degree and the second matching degree.

[0135] Next, a case that does not satisfy the matching condition is introduced: when the matching condition (1) is not satisfied, that is, when the number of candidate roads obtained by the first map matching is one, the second map matching is not performed, and the candidate road obtained by the first map matching can be determined as the target road where the target object is located.

[0136] When the matching condition (1) is satisfied and the matching condition (2) is not satisfied, that is, when the number of candidate roads obtained by the first map matching is greater than one and high-precision road data does not exist for each candidate road of the target object, the second map matching is not performed, and the candidate road corresponding to the maximum matching degree between the target object and each candidate road obtained by the first map matching can be determined as the target road where the target object is located.

[0137] When the matching condition (1) is satisfied, the matching condition (2) is satisfied, and the matching condition (3) is not satisfied, that is, when the number of candidate roads obtained by the first map matching is greater than one, high-precision map data exists for at least one candidate road of the target object, and the positioning data does not satisfy the positioning accuracy condition, the second map matching is not performed, and the candidate road corresponding to the maximum matching degree between the target object and each candidate road obtained by the first map matching can be determined as the target road where the target object is located.

[0138] S304, determining the target road where the target object is located from the plurality of candidate roads according to the matching degree between the target object and each candidate road.

[0139] In step S304, the matching degree between the target object and each candidate road obtained through the second map matching is obtained. The matching degree between the target object and each candidate road can be used to indicate the probability that the target object is located in the corresponding candidate road. According to the matching degree between the target object and each candidate road, the target road in which the target object is located is determined from the plurality of candidate roads.

[0140] In some embodiments, the candidate road with the largest matching degree is determined as the target road in which the target object is located. In some embodiments, the candidate road with a matching degree greater than a target matching degree threshold can be determined as the target road in which the target object is located.

[0141] In the embodiments of the present application, the first map matching has low matching accuracy and high matching efficiency. Through the low-precision map rough matching (i.e., the first map matching), the candidate road in which the target object can be matched can be quickly determined in the road within a certain range around the target object. Through the high-precision map fine matching (i.e., the second map matching), the matching accuracy between the target object and the candidate road can be improved. Moreover, the matching condition of the map fine matching is set in the embodiments of the present application. When the matching condition is met, the map fine matching is triggered. When the matching condition is met, it can be ensured that the positioning data used for the map fine matching is high-precision positioning data, and the road data is road data with higher accuracy than the standard-precision road data, so that the matching accuracy of the map fine matching is guaranteed.

[0142] In the embodiments of the present application, the plurality of candidate roads of the target object can be determined according to the positioning data of the target object. The plurality of road data of the plurality of candidate roads can be obtained. The candidate road and the road data are one-to-one corresponding. The plurality of road data includes at least one road data with an accuracy greater than an accuracy threshold. Then, the matching degree between the target object and each candidate road can be determined based on the positioning data and the road data of each candidate road. The matching degree is used to indicate the probability that the target object is located in the corresponding candidate road. According to the matching degree between the target object and each candidate road, the target road in which the target object is located is determined from the plurality of candidate roads.

[0143] Through the data processing method provided in the embodiments of the present application, the candidate road can be determined according to the positioning data of the target object first, which is equivalent to the first screening of the road. Then, the target road in which the target object is located is determined from the candidate road in combination with the road data with an accuracy greater than the accuracy threshold, which is equivalent to the second screening of the road. That is, the target road in which the target object is located can be determined through the screening of the road. Moreover, the target road is determined based on the road data with an accuracy greater than the accuracy threshold, which improves the accuracy of determining the target road in which the target object is located.

[0144] The embodiment of the present application provides a data processing method, and the content introduced by the data processing method includes the process of second map matching (i.e. the process of map fine matching) and the validity verification of a matching result. As shown in FIG. 6, FIG. 6 is a flowchart of another data processing method provided by the embodiment of the present application, and the data processing method can include but is not limited to the following steps S601-S606.

[0145] S601, performing first map matching between a target object and each road according to the positioning data of the target object and the first road data of each road around the target object, and determining a plurality of candidate roads of the target object in the each road.

[0146] In the embodiment of the present application, the execution process of step S601 is the same as that of step S301 in the embodiment shown in FIG. 3, and specific reference can be made to the related description of step S301 in the embodiment shown in FIG. 3, which will not be repeated here.

[0147] S602, obtaining second road data of the plurality of candidate roads, and the data accuracy of the second road data of the same candidate road is higher than that of the first road data.

[0148] In the embodiment of the present application, the execution process of step S602 is the same as that of step S302 in the embodiment shown in FIG. 3, and specific reference can be made to the related description of step S302 in the embodiment shown in FIG. 3, which will not be repeated here.

[0149] S603, determining a first matching degree between the target object and the candidate road according to the position information of the candidate road and the speed information of the target object, and the first matching degree refers to a matching degree between a motion angle of the target object and a road angle indicated by the position information of the candidate road when the target object moves according to the speed information.

[0150] In step S603, for each candidate road, the first matching degree between the target object and the candidate road refers to an angle speed matching degree, which is related to the angle difference between the motion angle of the target object and the road angle of the candidate road and the motion speed of the target object.

[0151] In some embodiments, taking the ith candidate road as an example, the position information of the ith candidate road can include the start point information and the end point information of the ith candidate road, that is, the position information of each candidate road includes the start point information and the end point information of the candidate road. The speed information can include the speed direction and the speed value (i.e., the speed size). Based on this, the determination process of the first matching degree between the target object and the ith candidate road can include: ① determining the road angle of the ith candidate road according to the start point information and the end point information; ② determining the motion angle of the target object as the angle indicated by the speed direction; ③ calculating the angle difference between the road angle of the ith candidate road and the motion angle of the target object; and ④ determining the first matching degree between the target object and the candidate road based on the angle difference and the speed value. In ④, the angle difference and the speed value can also be mapped to the matching degree to obtain the first matching degree.

[0152] In step ① of the determination process of the first matching degree described above, for the candidate road with high-precision road data (i.e., the second road data of the candidate road is determined according to the high-precision map data), the candidate road has a road boundary, and the shape of the road boundary of the candidate road can be represented by a boundary shape point (i.e., a boundary point). In this case, the start point information of the ith candidate road can include the position information of the start point in the boundary shape point, and the end point information of the ith candidate road can include the position information of the end point in the boundary shape point.

[0153] In some embodiments, the road angle of the ith candidate road can be determined based on the position information of the start point and the position information of the end point located on the same side boundary. For example, as shown in FIG. 7, which is a schematic diagram of a road represented by high-precision road data and low-precision road data according to an embodiment of the present application, the road boundary of the candidate road with high-precision road data is represented by boundary shape point A, boundary shape point B, boundary shape point C, and boundary shape point D, boundary shape point A and boundary shape point C can be used as the start point, and boundary shape point B and boundary shape point D can be used as the end point. The road angle of the ith candidate road can be determined according to the position information of the start point (boundary shape point A) and the position information of the end point (boundary shape point B) located on the same side boundary (e.g., the right side boundary).

[0154] For the candidate road with low-precision road data (i.e., the second road data of the candidate road is obtained by data conversion based on the low-precision map), the candidate road is abstracted as a line segment, and the candidate road can be represented by a road shape point (i.e., a road point). In this case, the start point information of the ith candidate road can include the position information of the start point in the road shape point, and the end point information of the ith candidate road can include the position information of the end point in the road shape point.

[0155] In some embodiments, the road angle of the ith candidate road can be determined based on the position information of the start point and the position information of the end point. For example, as shown in FIG. 7, the candidate road with the tagged road data can be represented by road shape point A and road shape point B, and the road angle of the ith candidate road can be determined according to the position information of the start point (road shape point A) and the end point (road shape point B).

[0156] For step ③ in the determination process of the first matching degree, refer to the following formula 1: angleDiff = calaAziDiff(RTK.angle, candidata[i].angle) Formula 1

[0157] In the above formula 1, angleDiff represents the angle difference, RTK.angle represents the motion angle of the target object, candidata[i].angle represents the road angle of the ith candidate road, and calaAziDiff(*,*) is an angle difference calculation method, and the calculation result is a value in [-180, 180). A positive number indicates that RTK.angle is in the clockwise direction of candidata[i].angle, and a negative number indicates that it is in the counterclockwise direction.

[0158] For step ④ in the determination process of the first matching degree, refer to the following formula 2: candidata[i].wAngleSpd = f(angleDiff, RTK.spd) Formula 2

[0159] In the above formula 2, candidata[i].wAngleSpd represents the first matching degree, angleDiff represents the angle difference, RTK.spd represents the speed value, and f(*,*) represents a mapping function of the angle difference and the speed value.

[0160] A possible mapping effect of the mapping function f(*,*) on the angle difference and the speed value is shown in FIG. 8, which is a schematic diagram of a mapping relationship between the angle speed and the first matching degree according to an embodiment of the present application. It is considered that the greater the included angle between the target object and the candidate road, the lower the possibility of the target object being located in the candidate road, and the smaller the matching probability (i.e., the first matching degree). Under the same included angle, the greater the speed, the lower the possibility of the target object being located in the candidate road, and the smaller the matching probability (i.e., the first matching degree). Based on the above consideration, the first matching degree candidata[i].wAngleSpd=f(angleDiff,RTK.spd) needs to satisfy the value in the range [0, 1], the smaller the angle difference and the smaller the speed, the greater the matching probability (i.e., the first matching degree), because the higher the possibility of the target object changing lanes in the candidate road; the greater the angle difference and the greater the speed, the smaller the matching probability (i.e., the first matching degree). Considering the actual situation, when ABS(angleDiff)>90, the target object has no possibility of being located in the ith candidate road, f(angleDiff,RTK.spd)=0.0, and ABS(*) represents taking the absolute value.

[0161] Alternatively, the mapping function f(*,*) can refer to the following formula 3 and formula 4:

[0162] In the above formula 3 and formula 4, λ and are adjustable parameters.

[0163] It can be seen that in step S603, when determining the first matching degree, the angle difference between the motion angle of the target object and the road angle of the candidate road is considered, which can reflect whether the target object moves according to the direction of the candidate road; in addition to considering the angle difference, the angle difference and the motion speed of the target object are comprehensively considered, because under the same angle difference, different speeds can correspond to different motion behaviors, for example, it can be a behavior of changing lanes in the road, but the target object is still located in the road, and for example, it can not be moving in the current road, but moving in other roads; therefore, comprehensively considering the angle difference and the motion speed can improve the accuracy of the first matching degree.

[0164] S604, determining a second matching degree between the target object and the candidate road according to the position information of the target object and the position information of the candidate road, the second matching degree being a position matching degree between the target object and the candidate road.

[0165] In step S604, the second matching degree between the target object and the candidate road is related to the positional relationship between the target object and the candidate road. In some embodiments, taking the ith candidate road as an example, the determination process of the second matching degree can include: ① modeling the accuracy distribution of the positioning data according to the positional information of the target object and the positional information of the ith candidate road to obtain an accuracy distribution function; ② calculating the area of the accuracy distribution function falling within the road boundary range of the ith candidate road; and ③ determining the area as the second matching degree between the target object and the ith candidate road.

[0166] In some embodiments, the positioning data further includes positioning accuracy information; in step ① of the determination process of the second matching degree, the variance can be determined based on the positioning accuracy information, and the accuracy distribution of the positioning data is modeled based on the variance, the positional information of the target object and the positional information of the candidate road to obtain the accuracy distribution function.

[0167] In step ① of the determination process of the second matching degree, in the case that the accuracy of the road data of the candidate road is greater than the accuracy threshold, that is, in the case that the second road data of the ith candidate road is high-precision road data in the high-precision map data, the ith candidate road has a road boundary, and the positional information of the ith candidate road can include the positional information of the boundary-forming point (i.e., the boundary point) of the candidate road, which refers to the point forming the shape of the road boundary of the candidate road.

[0168] The process of modeling the accuracy distribution of the positioning data in step ① can include: determining the variance according to the positioning accuracy information of the positioning data; determining the boundary projection distance of the target object to the road boundary of the ith candidate road according to the positional information of the target object and the positional information of the boundary-forming point; determining the mean value according to the boundary projection distance; and modeling the accuracy distribution function according to the mean value and the variance.

[0169] In some embodiments, the ith candidate road (candidate[i]) has corresponding high-precision road data (i.e., HD road data), and the positioning point corresponding to the target object can be projected to the left and right boundaries of the corresponding ith candidate road to obtain the boundary projection distance. As shown in FIG. 9, which is a schematic diagram of the projection of a target object to a road boundary according to an embodiment of the present application, for any two points A and B in the boundary-forming point, the vertical point M of the positioning point P corresponding to the target object to the line segment AB is calculated, and the position of M in the line segment AB is found, and the distance of PM is used to represent the boundary projection distance (i.e., the left boundary projection distance) of the positioning point to the boundary. The boundary projection distance needs to be signed, if P is on the left side of the AB vector, PM is positive, and if P is on the right side of the AB vector, PM is negative. The right boundary projection distance can be obtained in the same way.

[0170] Next, the left boundary projection distance Dleft and right boundary projection distance D right Then, the road width of the ith candidate road roadWidth i = D right -D left The mean value mean can be -D left , that is, the mean value is the opposite of the left boundary projection distance; the variance std = RTK.ACC / 2 (RTK.ACC is positioning accuracy information of the positioning data, which can be considered as CEP95, if it is CEP99, it can be divided by 3, or divided by other numbers, which is not limited by the embodiments of the present application), that is, the variance is obtained by dividing the positioning accuracy data of the positioning data by a preset value, which can be set according to actual use requirements. The accuracy distribution of the positioning data can be modeled according to the mean value mean and the variance std to obtain the accuracy distribution function.

[0171] In step ① of the second matching degree determination process, in the case that the road data of the candidate road is not road data with accuracy greater than the accuracy threshold, that is, in the case that the second road data of the ith candidate road is the high-precision road data in the high-precision map data, the second road data of the ith candidate road is determined according to the road width and the high-precision road data in the high-precision map data.

[0172] The position information of the ith candidate road can include the position information of the road shape point (i.e., road point) of the ith candidate road, and the road shape point refers to a point constituting the road shape of the ith candidate road. The road width can be determined according to the lane width and the number of lanes in the road data of the ith candidate road. In this case, the process of modeling the accuracy distribution of the positioning data in step ① can include: determining the variance according to the positioning accuracy information of the positioning data; determining the road projection distance of the target object to the ith candidate road according to the position information of the target object and the position information of the road shape point; determining the mean value according to the road width and the road projection distance; and modeling the accuracy distribution function according to the mean value and the variance.

[0173] In some embodiments, the ith candidate road (candidate[i]) does not have corresponding high-precision road data (HD road data), and the positioning point corresponding to the target object can be projected to the SD road data to obtain the road projection distance D, which also has positive and negative meanings. At this time, the road width of the ith candidate road can be calculated based on the number of lanes laneCnt in the SD road data (i.e., the first road data), and the road width of the ith candidate road roadWidth i = W(*laneCnt i; wherein, W represents a lane width of a lane, is a preset value, can be 3.0 m, 3.5 m, 3.75 m, etc., and can be adjusted according to different road scenes; laneCnt i represents the number of lanes of the ith candidate road.

[0174] Then, after obtaining the road projection distance D and the road width roadWidth i , the mean value mean = 0.5 * roadWidth i -D can be calculated, that is, the mean value is the result of calculating the road width roadWidth i of the ith candidate road and the road projection distance D; the variance std = RTK.ACC / 2 (RTK.ACC is positioning accuracy information of the specified bit data, which can be considered as CEP95 in meaning, if it is CEP99, it can be divided by 3, or divided by other numbers, which is not limited by the embodiments of the present application), that is, the variance is obtained by dividing the positioning accuracy data of the positioning data by the preset value. The accuracy distribution of the positioning data can be modeled according to the mean value mean and the variance std to obtain the accuracy distribution function, which is a Gaussian probability density function (i.e., a normal distribution function).

[0175] In step ② of the determination process of the second matching degree, the process of calculating the area in which the accuracy distribution function falls within the road boundary range of the ith candidate road can include: calculating a first area related to the left boundary projection distance of the target object according to the accuracy distribution function; the left boundary projection distance refers to the projection distance of the target object to the left road boundary of the ith candidate road; calculating a second area related to the right boundary projection distance of the target object according to the accuracy distribution function; the right boundary projection distance refers to the projection distance of the target object to the right road boundary of the ith candidate road.

[0176] If the target object is located within the road boundary range, the first area and the second area can be calculated according to the first rule to obtain the area in which the accuracy distribution function falls within the road boundary range of the ith candidate road; if the target object is located outside the road boundary range, the first area and the second area can be calculated according to the second rule to obtain the area in which the accuracy distribution function falls within the road boundary range of the ith candidate road.

[0177] In some embodiments, if the target object is located within the road boundary range, the area in which the accuracy distribution function falls within the road boundary range of the candidate road is determined based on the sum of the first area and the second area.

[0178] In some embodiments, if the target object is located within the road boundary range, the area in which the accuracy distribution function falls within the road boundary range of the candidate road is determined based on the difference between the first area and the second area.

[0179] In some embodiments, the two calculated values ​​based on the above-described normal distribution function (i.e., the first area and the second area) are as follows: Formulas 5-7: V1 = 1.0 - normalCDF((0.0 - mean) / std) Formula 5 V2 = 1.0 - normalCDF((roadWidth - mean) / std) Formula 6

[0180] In formulas 5 and 6 above, V1 represents the first area, V2 represents the second area, and erfc(x) is a complementary error function of a standard normal distribution. The calculation methods for the first and second areas can be found in the following code:

[0181] In the code above, candidate[i].wDis2Seg represents the second matching degree between the target object and the i-th candidate road; 0.5*(V1+V2) represents the first rule for calculating the first area and the second area when the target object is within the road boundary; 0.5*ABS(V1-V2) represents the second rule for calculating the first area and the second area when the target object is outside the road boundary.

[0182] Based on the above steps S604, the visualization of the calculation of the second matching degree (i.e., position matching degree) between the target object and the candidate road is shown in Figure 10. Figure 10 is a schematic diagram of the calculation of the second matching degree between the target object and the candidate road provided by an embodiment of this application. The meaning of the calculation of the second matching degree is the normal distribution function (N ~ (μ, σ)). 2 ) represents the normal distribution function, μ represents the mean, which is equal to mean, and σ 2 The variance is expressed as the area (std) within the road surface. Shaded area 1 represents the area of ​​the target object's location point P within the main road ("W_main" represents the width of the main road), which can be used as the second degree of matching between the target object and the main road; shaded area 2 represents the area of ​​the target object's location point P within the auxiliary road ("W_auxiliary" represents the width of the auxiliary road), which can also be used as the second degree of matching between the target object and the auxiliary road.

[0183] S605, determine the matching degree between the target object and the candidate road based on the first matching degree and the second matching degree.

[0184] In step S605, taking the ith candidate road as an example, after determining the first matching degree between the target object and the ith candidate road and the second matching degree between the target object and the ith candidate road, the matching degree between the target object and the candidate road can be determined according to the first matching degree and the second matching degree, as shown in the following formula 8: candidate[i].rtkProb=candidate[i].wDis2Seg*candidate[i].wAngleSpd

[0185] In the above formula 8, candidate[i].rtkProb represents the matching degree between the target object and the ith candidate road, candidate[i].wAngleSpd represents the first matching degree between the target object and the ith candidate road, and candidate[i].wDis2Seg represents the second matching degree between the target object and the ith candidate road.

[0186] In some embodiments, the product of the first matching degree and the second matching degree can be determined as the matching degree between the target object and the candidate road, in this way, the accurate matching degree between the target object and the candidate road can be obtained.

[0187] In step S606, the target road where the target object is located can be determined from the plurality of candidate roads according to the matching degree between the target object and each candidate road.

[0188] In step S606, the matching degree between the target object and each candidate road can be validity checked based on a matching degree threshold; if the matching degree between the target object and each candidate road passes the validity check, the matching degree between the target object and each candidate road can be normalized to obtain a processed matching degree; and the target road where the target object is located can be determined from the plurality of candidate roads according to the processed matching degree, that is, the target road where the target object is located can be determined from the plurality of candidate roads according to the normalized matching degree between the target object and each candidate road.

[0189] In some embodiments, the matching degree threshold value can include a first matching degree threshold value and a second matching degree threshold value, and the first matching degree threshold value is smaller than the second matching degree threshold value. The process of the validity check can include: comparing the matching degrees between the target object and each candidate road with the first matching degree threshold value; if the number of matching degrees greater than or equal to the first matching degree threshold value among the matching degrees between the target object and each candidate road is a target number (for example, the target number can be 1), then comparing the matching degrees greater than or equal to the first matching degree threshold value with the second matching degree threshold value; if the matching degrees greater than or equal to the first matching degree threshold value are greater than or equal to the second matching degree threshold value, then it can be determined that the matching degrees between the target object and each candidate road pass the validity check; if the number of matching degrees greater than or equal to the first matching degree threshold value is greater than the target number, then it can be determined that the validity check fails; if the matching degrees greater than or equal to the first matching degree threshold value are less than the second matching degree threshold value, then it can be determined that the validity check fails.

[0190] For example, the first matching degree threshold value th2 and the second matching degree threshold value th3 can be set, and 0 < th2 < th3 < 1 is satisfied. If conditions one and two are satisfied, then it can be determined that the matching degree is valid (i.e., passes the validity check), otherwise it can be determined that the matching degree is invalid (i.e., fails the validity check):

[0191] Condition one: for all candidate roads of the target object, there is and only one candidate road that satisfies candidate[i].wDis2Seg ≥ th2;

[0192] Condition two: for all candidate roads of the target object, the candidate road that satisfies candidate[i].wDis2Seg ≥ th2 also satisfies candidate[i].wDis2Seg ≥ th3.

[0193] It can be seen that, in the case of passing the validity check, among all candidate roads of the target object, only one candidate road has a matching degree greater than or equal to the second matching degree threshold value th3, and the matching degrees between the target object and the remaining candidate roads are all less than the first matching degree threshold value th2. In this way, the matching degree greater than or equal to the second matching degree threshold value th3 has a distinguishing degree compared with the remaining matching degrees.

[0194] In some embodiments, the matching degree threshold value includes a first matching degree threshold value and a second matching degree threshold value, and the first matching degree threshold value is smaller than the second matching degree threshold value. For the matching degrees between the target object and each candidate road, the matching degrees greater than or equal to the first matching degree threshold value are determined as target matching degrees,

[0195] If the target matching degree satisfies the following condition, it is determined that the matching degree between the target object and each candidate road passes the validity check: the number of target matching degrees is equal to the target number, the target number is 1, and the target matching degree is greater than or equal to the second matching degree threshold. The target number can be set according to actual use requirements.

[0196] If there is no target matching degree, it is determined that the validity check fails. If the number of target matching degrees is not equal to the target number, it is determined that the validity check fails. If the target matching degree is less than the second matching degree threshold, it is determined that the validity check fails. In this way, the validity of the matching degree can be checked, thereby improving the accuracy of subsequent determination of the target road.

[0197] In the case where the matching degree between the target object and each candidate road passes the validity check, the normalization method of the matching degree between the target object and each candidate road can refer to the following code:

[0198] In combination with the above code, the normalization method can include: using the matching degree between the target object and each candidate road, divided by the sum of the matching degree between the target object and each candidate road, to obtain the normalized matching degree between the target object and each candidate road, that is, to obtain the processed matching degree.

[0199] After normalization, that is, after obtaining the processed matching degree, the normalized matching degree between the target object and each candidate road can be used to replace the matching degree between the target object and each candidate road obtained by the first map matching to obtain the final matching degree between the target object and each candidate road. The replacement process is shown in the following code: for (int i = 0; i < candidate.size; i++) { candidate[i].prob = candidate[i].rtkProb;}

[0200] In step S604, according to the normalized matching degree between the target object and each candidate road, the target road in which the target object is located is determined from the plurality of candidate roads. For example, the candidate road corresponding to the maximum normalized matching degree (i.e. the processed matching degree) can be determined as the target road in which the target object is located.

[0201] The above content of step S604 introduces the case where the matching degree between the target object and each candidate road passes the validity check. For the case where the matching degree between the target object and each candidate road does not pass the validity check, the candidate road corresponding to the maximum matching degree obtained by the first map matching is determined as the target road in which the target object is located.

[0202] In the second map matching (i.e., the fine map matching) process in the embodiments of the present application, the matching degree between the target object and the candidate road is determined according to the angular velocity matching degree (i.e., the first matching degree) and the position matching degree (i.e., the second matching degree); the fine map matching process comprehensively considers the difference between the motion angle of the target object and the road angle of the candidate road, the motion speed of the target object, and the positional relationship between the target object and the candidate road; that is, the fine map matching process comprehensively considers various factors that may affect the matching between the target object and the candidate road, and can improve the matching accuracy of the fine map matching process. In addition, the effectiveness of the matching result of the fine map matching is verified, so that the matching result that passes the effectiveness verification has a distinguishing degree, which is conducive to accurately determining the target road where the target object is located based on the matching result of the fine map matching.

[0203] The above describes the method of the embodiments of the present application in detail. In order to better implement the above-mentioned scheme of the embodiments of the present application, the device of the embodiments of the present application is provided accordingly.

[0204] Please refer to FIG. 11, which is a structural schematic diagram of a data processing device provided by the embodiments of the present application. The data processing device can be arranged in the electronic device provided by the embodiments of the present application. The electronic device can be a terminal, or a server, or an electronic map platform composed of a terminal and a server. The data processing device shown in FIG. 11 can be a computer program running in the electronic device. The data processing device can be configured to execute part or all of the steps in the method embodiments shown in FIG. 3 or FIG. 6. Please refer to FIG. 11. The data processing device can include the following modules:

[0205] The processing module 1101 is configured to determine a plurality of candidate roads of the target object according to the positioning data of the target object.

[0206] The obtaining module 1102 is configured to obtain a plurality of road data of the plurality of candidate roads. The candidate road and the road data correspond to each other, and the plurality of road data includes at least one road data with a precision greater than a precision threshold.

[0207] The processing module 1101 is further configured to determine the matching degree between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads. The matching degree is used to indicate the probability that the target object is located on the corresponding candidate road.

[0208] The processing module 1101 is further configured to determine the target road where the target object is located from the plurality of candidate roads according to the matching degree between the target object and each of the candidate roads.

[0209] In some embodiments, the positioning data comprises position information of the target object and speed information of the target object; and the road data of each candidate road comprises position information of the candidate road.

[0210] The processing module 1101 is further configured to, for each candidate road, determine a first matching degree between the target object and the candidate road based on the position information of the candidate road and the speed information of the target object, the first matching degree being a matching degree between a movement angle of the target object moving at the speed information and a road angle indicated by the position information of the candidate road.

[0211] determine a second matching degree between the target object and the candidate road based on the position information of the target object and the position information of the candidate road, the second matching degree being a position matching degree between the target object and the candidate road.

[0212] determine a matching degree between the target object and the candidate road based on the first matching degree and the second matching degree.

[0213] In some embodiments, the position information of each candidate road comprises start point information and end point information of the candidate road; and the speed information comprises a speed direction and a speed value. The processing module 1101 is further configured to determine a road angle of the candidate road according to the start point information and the end point information; determine an angle indicated by the speed direction as a movement angle of the target object; calculate an angle difference between the road angle of the candidate road and the movement angle of the target object; and determine the first matching degree between the target object and the candidate road based on the angle difference and the speed value.

[0214] In some embodiments, the processing module 1101 is configured to model an accuracy distribution of the positioning data based on the position information of the target object and the position information of the candidate road to obtain an accuracy distribution function.

[0215] calculate an area in which the accuracy distribution function falls within a road boundary range of the candidate road.

[0216] determine the area as the second matching degree between the target object and the candidate road.

[0217] In some embodiments, the positioning data further comprises positioning accuracy information. The processing module 1101 is further configured to determine a variance based on the positioning accuracy information; and model an accuracy distribution of the positioning data based on the variance, the position information of the target object and the position information of the candidate road to obtain an accuracy distribution function.

[0218] In some embodiments, in a case where the precision of the road data of the candidate road is greater than the precision threshold, the candidate road has a road boundary, and the position information of the candidate road comprises position information of a boundary point of the candidate road.

[0219] The processing module 1101 is further configured to determine a boundary projection distance of the target object to the road boundary of the candidate road according to the position information of the target object and the position information of the boundary point, determine a mean value according to the boundary projection distance, and model the precision distribution of the positioning data according to the mean value and the variance to obtain the precision distribution function.

[0220] In some embodiments, in a case where the road data of the candidate road is not road data with precision greater than the precision threshold, the road data of the candidate road comprises a road width of the candidate road, and the position information of the candidate road comprises position information of a road point of the candidate road.

[0221] The processing module 1101 is further configured to determine a road projection distance of the target object to the candidate road according to the position information of the target object and the position information of the road point, determine a mean value according to the road width and the road projection distance, and model the precision distribution of the positioning data according to the mean value and the variance to obtain the precision distribution function.

[0222] In some embodiments, the processing module 1101 is further configured to calculate a first area related to a left boundary projection distance of the target object according to the precision distribution function, the left boundary projection distance being a projection distance of the target object to a left road boundary of the candidate road, calculate a second area related to a right boundary projection distance of the target object according to the precision distribution function, the right boundary projection distance being a projection distance of the target object to a right road boundary of the candidate road, determine an area of the precision distribution function falling within the road boundary range of the candidate road based on a sum of the first area and the second area in a case where the target object is located within the road boundary range, and determine the area of the precision distribution function falling within the road boundary range of the candidate road based on a difference between the first area and the second area in a case where the target object is located outside the road boundary range.

[0223] In some embodiments, the processing module 1101 is further configured to acquire a plurality of roads within a preset range of the target object, and acquire initial road data of each of the roads; determine a matching degree between the target object and each of the roads based on positioning data of the target object and the initial road data of each of the roads; and determine a plurality of candidate roads of the target object from the plurality of roads according to the matching degrees between the target object and each of the roads.

[0224] In some embodiments, the acquiring module 1102 is further configured to, for each of the candidate roads, if there is road data corresponding to the initial road data and having an accuracy greater than the accuracy threshold, determine the road data corresponding to the initial road data as road data of the candidate road; if there is no road data corresponding to the initial road data, determine a road width of the candidate road according to a lane width and a number of lanes in the initial road data of the candidate road; and determine road data of the candidate road according to the road width of the candidate road and the initial road data of the candidate road.

[0225] In some embodiments, the positioning data is positioning data of the target object at a current time; the processing module 1101 is further configured to determine a distance between the target object and each of the roads and an included angle between the target object and each of the roads according to the positioning data and the initial road data of each of the roads; determine an observation probability of the target object at the current time according to the distance between the target object and each of the roads and the included angle between the target object and each of the roads; determine a transition probability according to a connection relationship between the roads; and determine the matching degree between the target object and each of the roads based on the transition probability and the observation probability of the target object at the current time.

[0226] In some embodiments, the processing module 1101 is further configured to perform validity check on the matching degree between the target object and each of the candidate roads based on a matching degree threshold; if the matching degree between the target object and each of the candidate roads passes the validity check, normalize the matching degree between the target object and each of the candidate roads to obtain a processed matching degree; and determine a target road in which the target object is located from the plurality of candidate roads according to the processed matching degree.

[0227] In some embodiments, the matching degree threshold includes a first matching degree threshold and a second matching degree threshold, and the first matching degree threshold is smaller than the second matching degree threshold; the processing module 1101 is further configured to compare the matching degrees between the target object and each of the candidate roads with the first matching degree threshold; if the number of matching degrees greater than or equal to the first matching degree threshold among the matching degrees between the target object and each of the candidate roads is a target number, then compare the matching degrees greater than or equal to the first matching degree threshold with the second matching degree threshold; if the matching degrees greater than or equal to the first matching degree threshold are greater than or equal to the second matching degree threshold, then determine that the matching degrees between the target object and each of the candidate roads pass the validity check.

[0228] In some embodiments, the matching degree threshold includes a first matching degree threshold and a second matching degree threshold, and the first matching degree threshold is smaller than the second matching degree threshold; the processing module 1101 is further configured to determine, for the matching degrees between the target object and each of the candidate roads, a target matching degree as a matching degree greater than or equal to the first matching degree threshold; if the target matching degree satisfies the following conditions, then determine that the matching degrees between the target object and each of the candidate roads pass the validity check: the number of the target matching degrees is equal to a target number; and the target matching degrees are greater than or equal to the second matching degree threshold.

[0229] In some embodiments, the processing module 1101 is further configured to, before determining the matching degrees between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads, perform road data detection on the road data of the plurality of candidate roads; if a detection result of the road data detection indicates that at least one piece of road data in the plurality of pieces of road data has a precision greater than a precision threshold, then perform positioning precision detection on the positioning data; if a detection result of the positioning precision detection indicates that the positioning data satisfies a positioning precision condition, then trigger the determination of the matching degrees between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads.

[0230] In some embodiments, the processing module 1101 is further configured to obtain a solution state of the positioning data; if the solution state of the positioning data is a preset solution state, then compare positioning precision information of the positioning data with a positioning precision threshold; if the positioning precision information is smaller than the positioning precision threshold, then generate a detection result of the positioning precision detection, and the detection result of the positioning precision detection indicates that the positioning data satisfies a positioning precision condition.

[0231] According to one embodiment of the present application, each module in the data processing apparatus shown in FIG. 11 can be combined into one or several other modules respectively or all together, or some of the modules can be further split into a plurality of modules with smaller functions to implement the same operation, without affecting the implementation of the technical effects of the embodiments of the present application. The above modules are divided based on logical functions, and in actual applications, the functions of one module can also be implemented by a plurality of modules, or the functions of a plurality of modules can be implemented by one module. In other embodiments of the present application, the apparatus can also include other modules, and in actual applications, these functions can also be implemented by other modules, and can be implemented by a plurality of modules in cooperation.

[0232] According to another embodiment of the present application, the data processing apparatus shown in FIG. 11 and the data processing method of the embodiments of the present application can be constructed and implemented by running a computer program capable of performing each step involved in part or all of the methods shown in FIG. 3 or FIG. 6 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), a random access memory (RAM), a read-only memory (ROM), and the like. The computer program can be recorded on a computer readable storage medium such as a computer readable storage medium, loaded into the above computing device through the computer readable storage medium, and run therein.

[0233] In the embodiments of the present application, based on the positioning data of the target object and the first road data of each road around the target object (the first road data is low-precision road data), first map matching can be performed between the target object and each road, and a plurality of candidate roads of the target object can be determined among the roads around the target object. It can be seen that the first map matching is map matching based on positioning data and low-precision road data, the first map matching is low-precision map matching, and through the first map matching, the candidate roads where the target object can be located can be quickly determined among the roads around the target object. Based on the positioning data of the target object and the second road data of the plurality of candidate roads of the target object (the second road data is high-precision road data), second map matching can be performed between the target object and each candidate road, and a matching degree between the target object and each candidate road can be obtained. It can be seen that the second map matching is map matching based on positioning data and high-precision road data, the second map matching is high-precision map matching, and through the second map matching, the matching accuracy between the object and the road can be improved, so that the road where the object is located can be accurately determined based on the matching degree.

[0234] Based on the above method and the device embodiment, the embodiment of the present application provides an electronic device. Please refer to FIG. 12, which is a structural schematic diagram of an electronic device provided by the embodiment of the present application. The electronic device shown in FIG. 12 at least includes a processor 1201, an input interface 1202, an output interface 1203 and a memory 1204. Among them, the processor 1201, the input interface 1202, the output interface 1203 and the memory 1204 can be connected through a bus or other means.

[0235] The memory 1204 can be stored in the memory of the electronic device, and the memory 1204 is configured to store a computer program, the computer program including computer instructions, and the processor 1201 is configured to execute the computer program stored in the memory 1204. The processor 1201 (or called Central Processing Unit, CPU) is the computing core and control core of the electronic device, which is suitable for implementing the computer program, and specifically suitable for loading and executing the computer program to realize the corresponding method process or corresponding function.

[0236] The embodiment of the present application also provides a computer readable storage medium (Memory), which is a memory device in the electronic device, configured to store programs and data. It can be understood that the computer readable storage medium here can include the built-in storage medium in the electronic device, and of course can also include the expansion storage medium supported by the electronic device. The computer readable storage medium provides a storage space, which stores the operating system of the electronic device. And in the storage space, there is also a computer program suitable for being loaded and executed by the processor. It should be noted that the computer readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory; optionally, it can also be at least one computer readable storage medium located away from the aforementioned processor.

[0237] The electronic device can be a terminal, or a server, or an electronic map platform composed of a terminal and a server. In some embodiments, the electronic device includes at least one processor, a memory, at least one network interface and a user interface.

[0238] In some embodiments, the computer program stored in the memory 1204 can be loaded and executed by the processor 1201 to realize the corresponding steps in the above method shown in FIG. 3 or FIG. 6. In a specific implementation, the computer program in the memory 1204 is loaded and executed by the processor 1201 to perform the following steps:

[0239] The first map matching is performed between the target object and each road according to the positioning data of the target object and first road data of each road around the target object, and a plurality of candidate roads of the target object are determined in the roads;

[0240] Second road data of the plurality of candidate roads is acquired, and data accuracy of the second road data of a same candidate road is higher than that of the first road data of the same candidate road;

[0241] The second map matching is performed between the target object and each candidate road according to the positioning data and the second road data of each candidate road, and a matching degree between the target object and each candidate road is obtained, wherein the matching degree between the target object and each candidate road indicates a probability that the target object is located in the corresponding candidate road;

[0242] The target road where the target object is located is determined from the plurality of candidate roads according to the matching degree between the target object and each candidate road.

[0243] In some embodiments, the positioning data includes position information of the target object and speed information of the target object, and any candidate road in the plurality of candidate roads is represented as an ith candidate road, and the second road data of the ith candidate road includes position information of the ith candidate road;

[0244] The computer program in the memory 1204 is loaded and executed by the processor 1201, and is configured to perform the following steps when performing the second map matching between the target object and each candidate road according to the positioning data and the second road data of each candidate road, and obtaining the matching degree between the target object and each candidate road:

[0245] According to the position information of the ith candidate road and the speed information of the target object, a first matching degree between the target object and the ith candidate road is determined, wherein the first matching degree refers to a matching degree between a motion angle of the target object when the target object moves according to the speed information and a road angle indicated by the position information of the ith candidate road;

[0246] According to the position information of the target object and the position information of the ith candidate road, a second matching degree between the target object and the ith candidate road is determined, wherein the second matching degree refers to a position matching degree between the target object and the ith candidate road;

[0247] According to the first matching degree and the second matching degree, the matching degree between the target object and the ith candidate road is determined.

[0248] In some embodiments, the position information of the ith candidate road includes start point information and end point information of the ith candidate road; the speed information includes a speed direction and a speed value; the computer program in the memory 1204 is loaded and executed by the processor 1201, and configured to perform the following steps when determining the first matching degree between the target object and the ith candidate road according to the position information of the ith candidate road and the speed information of the target object:

[0249] determining a road angle of the ith candidate road according to the start point information and the end point information;

[0250] determining a motion angle of the target object as an angle indicated by the speed direction;

[0251] calculating an angle difference between the road angle of the ith candidate road and the motion angle of the target object;

[0252] mapping the angle difference and the speed value to obtain the first matching degree.

[0253] In some embodiments, the computer program in the memory 1204 is loaded and executed by the processor 1201, and configured to perform the following steps when determining the second matching degree between the target object and the ith candidate road according to the position information of the target object and the position information of the ith candidate road:

[0254] modeling an accuracy distribution of the positioning data according to the position information of the target object and the position information of the ith candidate road to obtain an accuracy distribution function;

[0255] calculating an area in which the accuracy distribution function falls within a road boundary range of the ith candidate road;

[0256] determining the area as the second matching degree between the target object and the ith candidate road.

[0257] In some embodiments, when the second road data of the ith candidate road is high-precision road data in high-precision map data, the ith candidate road has a road boundary, and the position information of the ith candidate road includes position information of a boundary forming point of the ith candidate road, the boundary forming point being a point constituting a road boundary shape of the ith candidate road;

[0258] The computer program in the memory 1204 is loaded and executed by the processor 1201, and configured to perform the following steps when modeling the accuracy distribution of the positioning data according to the position information of the target object and the position information of the ith candidate road to obtain the accuracy distribution function:

[0259] determining a variance according to positioning accuracy information of the positioning data;

[0260] According to the position information of the target object and the position information of the boundary shape point, a boundary projection distance of the target object to a road boundary of the ith candidate road is determined;

[0261] According to the boundary projection distance, a mean value is determined;

[0262] According to the mean value and the variance, a precision distribution function is modeled.

[0263] In some embodiments, when the second road data of the ith candidate road is determined according to a road width and the landmark road data in the landmark map data, the position information of the ith candidate road includes position information of road shape points of the ith candidate road, the road shape points refer to points constituting a road shape of the ith candidate road; the road width is determined according to a lane width and a lane number in the first road data of the ith candidate road;

[0264] The computer program in the memory 1204 is loaded and executed by the processor 1201, and when modeling a precision distribution of positioning data according to the position information of the target object and the position information of the ith candidate road to obtain a precision distribution function, the computer program is configured to perform the following steps:

[0265] According to the positioning precision information of the positioning data, a variance is determined;

[0266] According to the position information of the target object and the position information of the road shape point, a road projection distance of the target object to the ith candidate road is determined;

[0267] According to the road width and the road projection distance, a mean value is determined;

[0268] According to the mean value and the variance, a precision distribution function is modeled.

[0269] In some embodiments, when the computer program in the memory 1204 is loaded and executed by the processor 1201 to calculate an area in which the precision distribution function falls within a road boundary range of the ith candidate road, the computer program is configured to perform the following steps:

[0270] According to the precision distribution function, a first area related to a left boundary projection distance of the target object is calculated; the left boundary projection distance refers to a projection distance of the target object to a left road boundary of the ith candidate road;

[0271] According to the precision distribution function, a second area related to a right boundary projection distance of the target object is calculated; the right boundary projection distance refers to a projection distance of the target object to a right road boundary of the ith candidate road;

[0272] If the target object is located within the road boundary range, the first area and the second area are calculated according to a first rule to obtain the area in which the precision distribution function falls within the road boundary range of the ith candidate road;

[0273] If the target object is located outside the road boundary range, the first area and the second area are calculated according to the second rule to obtain an area in which the precision distribution function falls within the road boundary range of the ith candidate road.

[0274] In some embodiments, the first road data is high-precision road data in the high-precision map data; any candidate road in the plurality of candidate roads is represented as the ith candidate road; and the computer program in the memory 1204 is loaded and executed by the processor 1201 to obtain the second road data of the plurality of candidate roads, and is configured to perform the following steps:

[0275] If the ith candidate road has corresponding high-precision road data in the high-precision map data, the high-precision road data of the ith candidate road is determined as the second road data of the ith candidate road.

[0276] If the ith candidate road does not have corresponding high-precision road data in the high-precision map data, the road width of the ith candidate road is determined according to the lane width and the number of lanes in the first road data of the ith candidate road; and the second road data of the ith candidate road is determined according to the road width of the ith candidate road and the first road data of the ith candidate road.

[0277] In some embodiments, the computer program in the memory 1204 is loaded and further configured to perform the following steps by the processor 1201:

[0278] The matching degree between the target object and each candidate road is validity checked according to the matching degree threshold.

[0279] If the matching degree between the target object and each candidate road passes the validity check, the matching degree between the target object and each candidate road is normalized.

[0280] The computer program in the memory 1204 is loaded and executed by the processor 1201 to determine the target road in which the target object is located from the plurality of candidate roads according to the matching degree between the target object and each candidate road, and is configured to perform the following steps:

[0281] The target road in which the target object is located from the plurality of candidate roads is determined according to the normalized matching degree between the target object and each candidate road.

[0282] In some embodiments, the matching degree threshold includes a first matching degree threshold and a second matching degree threshold, and the first matching degree threshold is smaller than the second matching degree threshold; and the computer program in the memory 1204 is loaded and executed by the processor 1201 to perform the validity check on the matching degree between the target object and each candidate road according to the matching degree threshold, and is configured to perform the following steps:

[0283] The matching degree between the target object and each candidate road is compared with the first matching degree threshold;

[0284] If the number of matching degrees greater than or equal to the first matching degree threshold in the matching degrees between the target object and each candidate road is the target number, the matching degrees greater than or equal to the first matching degree threshold are compared with the second matching degree threshold;

[0285] If the matching degrees greater than or equal to the first matching degree threshold are greater than or equal to the second matching degree threshold, it is determined that the matching degrees between the target object and each candidate road pass the validity check.

[0286] In some embodiments, the computer program in the memory 1204 is loaded by the processor 1201 and is further configured to perform the following steps:

[0287] High-precision road data detection is performed on the plurality of candidate roads;

[0288] If the detection result of the high-precision road data detection indicates that at least one candidate road in the plurality of candidate roads has high-precision road data, positioning accuracy detection is performed on the positioning data;

[0289] If the detection result of the positioning accuracy detection indicates that the positioning data meets the positioning accuracy condition, the execution of obtaining the second road data of the plurality of candidate roads is triggered.

[0290] In some embodiments, the positioning data of the target object is obtained by using a target positioning rule; when the computer program in the memory 1204 is loaded by the processor 1201 and performs positioning accuracy detection on the positioning data, it is configured to perform the following steps:

[0291] The solving state of the positioning data is detected in the set of accurate solving states corresponding to the target positioning rule;

[0292] If the solving state of the positioning data is an accurate solving state in the set of accurate solving states, the positioning accuracy information of the positioning data is compared with the accuracy threshold;

[0293] If the positioning accuracy information is less than the accuracy threshold, a detection result of the positioning accuracy detection is generated, and the detection result of the positioning accuracy detection indicates that the positioning data meets the positioning accuracy condition.

[0294] In some embodiments, the positioning data is the positioning data of the target object at the current time; when the computer program in the memory 1204 is loaded by the processor 1201 and performs the first map matching between the target object and each road in the set of roads around the target object according to the positioning data of the target object and the first road data of each road in the set of roads, and determines the plurality of candidate roads of the target object in the set of roads, it is configured to perform the following steps:

[0295] According to the positioning data and the first road data of each road, a distance between the target object and each road and an included angle between the target object and each road are determined;

[0296] According to the distance between the target object and each road and the included angle between the target object and each road, an observation probability of the target object at the current moment is determined;

[0297] According to a connection relationship between a road corresponding to a last moment of the target object at the current moment and each road, a transition probability from the last moment to the current moment is determined;

[0298] According to the observation probability of the target object at the last moment, the transition probability and the observation probability of the target object at the current moment, a matching degree between the target object and each road is determined;

[0299] According to the matching degree between the target object and each road, a candidate road of the target object is determined.

[0300] In the embodiment of the application, based on the positioning data of the target object and the first road data of each road around the target object (the first road data is low-precision road data), first map matching can be performed between the target object and each road, and a plurality of candidate roads of the target object can be determined among the roads around the target object. It can be seen that the first map matching is map matching based on positioning data and low-precision road data, the first map matching is low-precision map matching, and through the first map matching, the candidate road where the target object can be located can be quickly determined among the roads around the target object. Based on the positioning data of the target object and the second road data of the plurality of candidate roads of the target object (the second road data is high-precision road data), second map matching can be performed between the target object and each candidate road, and a matching degree between the target object and each candidate road can be obtained. It can be seen that the second map matching is map matching based on positioning data and high-precision road data, the second map matching is high-precision map matching, and through the second map matching, the matching accuracy between the object and the road can be improved, so that the road where the object is located can be accurately determined based on the matching degree.

[0301] In the embodiment of the application, a plurality of candidate roads of the target object can be determined according to the positioning data of the target object, a plurality of road data of the plurality of candidate roads can be obtained, the candidate road and the road data are one-to-one corresponding, at least one road data with a precision greater than a precision threshold is included in the plurality of road data, and then the matching degree between the target object and each candidate road can be determined based on the positioning data and the road data of each candidate road. The matching degree is used to indicate the probability that the target object is located on the corresponding candidate road, and the target road where the target object is located is determined from the plurality of candidate roads according to the matching degree between the target object and each candidate road.

[0302] By means of the data processing method provided in the embodiments of the present application, the candidate road can be determined according to the positioning data of the target object, which is equivalent to a first screening of the road, and then the target road where the target object is located is determined from the candidate road in combination with the road data whose accuracy is greater than the accuracy threshold, which is equivalent to a second screening of the road, that is, the target road where the target object is located can be determined through the screening of the road, and the target road is determined based on the road data whose accuracy is greater than the accuracy threshold, thereby improving the accuracy of determining the target road where the target object is located.

[0303] The embodiments of the present application also provide a computer program product, which comprises a computer program stored in a computer readable storage medium. The processor of the electronic device reads the computer program from the computer readable storage medium, and the processor executes the computer program, so that the electronic device executes the above-mentioned data processing method.

[0304] Those skilled in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present application.

[0305] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined target, and can be realized in whole or in part by using software, hardware (such as processing circuit or memory) or combination thereof. Similarly, one processor (or multiple processors or memory) can be used to realize one or more modules or units. In addition, each module or unit can be a part of an integral module or unit that contains the function of the module or unit.

[0306] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in or transmitted by a computer readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (for example, coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, DVD), or a semiconductor medium (for example, solid state disk (SSD)) and the like.

[0307] The above merely describes the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

A data processing method is executed by an electronic device, and the method comprises: determining a plurality of candidate roads of a target object according to positioning data of the target object; obtaining a plurality of pieces of road data of the plurality of candidate roads, the candidate roads and the road data being in one-to-one correspondence, and the plurality of pieces of road data including at least one piece of road data with a precision greater than a precision threshold; determining a matching degree between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads; the matching degree is used to indicate a probability that the target object is located on a corresponding candidate road; determining a target road where the target object is located from the plurality of candidate roads according to the matching degree between the target object and each of the candidate roads. The method of claim 1, wherein, The positioning data comprises position information of the target object and speed information of the target object; and the road data of each of the candidate roads comprises position information of the candidate road; The determination of the matching degree between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads comprises: for each of the candidate roads, determining a first matching degree between the target object and the candidate road based on the position information of the candidate road and the speed information of the target object, the first matching degree being a matching degree between a motion angle of the target object and a road angle indicated by the position information of the candidate road when the target object moves at the speed information; determining a second matching degree between the target object and the candidate road based on the position information of the target object and the position information of the candidate road, the second matching degree being a position matching degree between the target object and the candidate road; determining the matching degree between the target object and the candidate road based on the first matching degree and the second matching degree. The method of claim 2, wherein, The position information of each of the candidate roads comprises start point information and end point information of the candidate road; and the speed information comprises a speed direction and a speed value; The determination of the first matching degree between the target object and the candidate road based on the position information of the candidate road and the speed information of the target object comprises: determining a road angle of the candidate road according to the start point information and the end point information; determining an angle indicated by the speed direction as a motion angle of the target object; calculating an angle difference between the road angle of the candidate road and the motion angle of the target object; determining the first matching degree between the target object and the candidate road based on the angle difference and the speed value. The method according to claim 2 or 3, wherein The determination of the second matching degree between the target object and the candidate road based on the position information of the target object and the position information of the candidate road comprises: modeling an accuracy distribution of the positioning data based on the position information of the target object and the position information of the candidate road to obtain an accuracy distribution function; calculating an area where the accuracy distribution function falls within a road boundary range of the candidate road; and determining the second matching degree between the target object and the candidate road based on the area. The area is determined as a second matching degree between the target object and the candidate road. The method of claim 4, wherein, The positioning data further comprises positioning accuracy information; and the accuracy distribution of the positioning data is modeled based on the position information of the target object and the position information of the candidate road to obtain an accuracy distribution function, comprising: a variance is determined based on the positioning accuracy information; the accuracy distribution of the positioning data is modeled based on the variance, the position information of the target object and the position information of the candidate road to obtain the accuracy distribution function. The method of claim 5, wherein, in a case where the accuracy of the road data of the candidate road is greater than the accuracy threshold, the candidate road has a road boundary, and the position information of the candidate road comprises position information of a boundary point of the candidate road; the accuracy distribution of the positioning data is modeled based on the variance, the position information of the target object and the position information of the candidate road to obtain the accuracy distribution function, comprising: a boundary projection distance of the target object to the road boundary of the candidate road is determined according to the position information of the target object and the position information of the boundary point; a mean value is determined according to the boundary projection distance; the accuracy distribution of the positioning data is modeled based on the mean value and the variance to obtain the accuracy distribution function. The method according to claim 5 or 6, wherein in a case where the road data of the candidate road is not the road data with accuracy greater than the accuracy threshold, the road data of the candidate road comprises a road width of the candidate road, and the position information of the candidate road comprises position information of a road point of the candidate road; the accuracy distribution of the positioning data is modeled based on the variance, the position information of the target object and the position information of the candidate road to obtain the accuracy distribution function, comprising: a road projection distance of the target object to the candidate road is determined according to the position information of the target object and the position information of the road point; a mean value is determined according to the road width and the road projection distance; the accuracy distribution of the positioning data is modeled based on the mean value and the variance to obtain the accuracy distribution function. The method according to any one of claims 4-7, wherein the area where the accuracy distribution function falls within the road boundary range of the candidate road is calculated, comprising: a first area related to a left boundary projection distance of the target object is calculated according to the accuracy distribution function; the left boundary projection distance refers to a projection distance of the target object to a left road boundary of the candidate road; a second area related to a right boundary projection distance of the target object is calculated according to the accuracy distribution function; the right boundary projection distance refers to a projection distance of the target object to a right road boundary of the candidate road; if the target object is located within the road boundary range, the area where the accuracy distribution function falls within the road boundary range of the candidate road is determined based on a sum of the first area and the second area; if the target object is located outside the road boundary range, the area where the accuracy distribution function falls within the road boundary range of the candidate road is determined based on a difference between the first area and the second area. The method according to any one of claims 1 to 8, wherein The method comprises the following steps: obtaining a plurality of roads in a preset range of the target object, and obtaining initial road data of each road; determining a matching degree between the target object and each road based on the positioning data of the target object and the initial road data of each road; determining a plurality of candidate roads of the target object from the plurality of roads according to the matching degree between the target object and each road. The method of claim 9, wherein, The method comprises the following steps: if there is road data corresponding to the initial road data and having a precision greater than the precision threshold for each candidate road, the road data corresponding to the initial road data is determined as the road data of the candidate road; if there is no road data corresponding to the initial road data, the road width of the candidate road is determined according to the lane width and the number of lanes in the initial road data of the candidate road, and the road data of the candidate road is determined according to the road width of the candidate road and the initial road data of the candidate road. The method according to claim 9 or 10, wherein The positioning data is the positioning data of the target object at the current time; The method comprises the following steps: determining the distance between the target object and each road and the included angle between the target object and each road according to the positioning data and the initial road data of each road; determining the observation probability of the target object at the current time according to the distance between the target object and each road and the included angle between the target object and each road; determining the transition probability according to the connection relationship between the roads; determining the matching degree between the target object and each road based on the transition probability and the observation probability of the target object at the current time. The method according to any one of claims 1-11, wherein, The method comprises the following steps: performing validity verification on the matching degree between the target object and each candidate road based on a matching degree threshold; if the matching degree between the target object and each candidate road passes the validity verification, normalizing the matching degree between the target object and each candidate road to obtain a processed matching degree; determining the target road where the target object is located from the plurality of candidate roads according to the processed matching degree. The method of claim 12, wherein The matching degree threshold comprises a first matching degree threshold and a second matching degree threshold, and the first matching degree threshold is less than the second matching degree threshold; The method comprises the following steps: comparing the matching degree between the target object and each candidate road with the first matching degree threshold; If the number of the matching degrees greater than or equal to the first matching degree threshold is a target number, the matching degrees greater than or equal to the first matching degree threshold are compared with the second matching degree threshold; If the matching degrees greater than or equal to the first matching degree threshold are greater than or equal to the second matching degree threshold, it is determined that the matching degrees between the target object and each of the candidate roads pass the validity check. The method of claim 12, wherein The matching degree threshold includes a first matching degree threshold and a second matching degree threshold, and the first matching degree threshold is less than the second matching degree threshold; The validity check of the matching degrees between the target object and each of the candidate roads based on the matching degree threshold includes: For the matching degrees between the target object and each of the candidate roads, the matching degrees greater than or equal to the first matching degree threshold are determined as target matching degrees; If the target matching degrees satisfy the following conditions, it is determined that the matching degrees between the target object and each of the candidate roads pass the validity check: The number of the target matching degrees is equal to a target number; The target matching degrees are greater than or equal to the second matching degree threshold. The method according to any one of claims 1 to 14, wherein Before the determination of the matching degrees between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads, the method further includes: Performing road data detection on the road data of the plurality of candidate roads; If the detection result of the road data detection indicates that at least one of the plurality of road data has a precision greater than a precision threshold, performing positioning precision detection on the positioning data; If the detection result of the positioning precision detection indicates that the positioning data satisfies a positioning precision condition, triggering the determination of the matching degrees between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads. The method of claim 15, wherein, The positioning precision detection on the positioning data includes: Obtaining a solution state of the positioning data; If the solution state of the positioning data is a preset solution state, comparing positioning precision information of the positioning data with a positioning precision threshold; If the positioning precision information is less than the positioning precision threshold, generating a detection result of the positioning precision detection, and the detection result of the positioning precision detection indicates that the positioning data satisfies a positioning precision condition. A data processing device, the device includes: A processing module configured to determine a plurality of candidate roads of a target object according to positioning data of the target object; An obtaining module configured to obtain a plurality of road data of the plurality of candidate roads, the candidate roads correspond to the road data one by one, and at least one of the plurality of road data has a precision greater than a precision threshold; The processing module is further configured to determine matching degrees between the target object and each of the candidate roads based on the positioning data and the road data of each of the candidate roads; The matching degrees are used to indicate probabilities that the target object is located on corresponding candidate roads; The processing module is further configured to determine a target road where the target object is located from the plurality of candidate roads according to matching degrees between the target object and each of the candidate roads. An electronic device includes: a memory configured to store computer-executable instructions or computer programs; a processor configured to implement the data processing method of any one of claims 1 to 16 when executing the computer-executable instructions or computer programs stored in the memory. A computer-readable storage medium storing a computer program, the computer program being adapted to be loaded and executed by a processor to implement the data processing method of any one of claims 1 to 16. A computer program product including a computer program, the computer program being executed by a processor to implement the data processing method of any one of claims 1 to 16.

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