Track confidence coefficient determination method and device, electronic equipment, storage medium and vehicle

Abstract

The invention relates to a track confidence coefficient determination method and device, electronic equipment, a storage medium and a vehicle, and the method comprises the steps: obtaining target matching data of a target track and at least one target road line matched with the target track from track matching road network data; the target matching data comprises a target number and a target length corresponding to each target road line; determining a track point matching ratio corresponding to each target road line based on a ratio of a target number corresponding to each target road line to a reference track point number; based on the ratio of the target length corresponding to each target road line to the total length of the target trajectory, determining a trajectory length matching ratio corresponding to each target road line; and determining the confidence of the target track based on the track point matching ratio corresponding to each target road line, the track length matching ratio corresponding to each target road line and a preset track confidence relationship.

Description

Technical Field

[0001] The present application relates to the field of automotive technology, and in particular to a trajectory confidence determination method, device, electronic device, storage medium, and vehicle. Background Art

[0002] Whether for assisted driving or autonomous driving, map data (such as standard maps or high-precision maps) plays a crucial role in vehicle operation. Currently, map data collection relies primarily on specialized surveying vehicles to collect various map data relevant to vehicle operation (such as speed limit information for roads and lanes). To ensure timely map data updates, surveying vehicles must be frequently used. However, this approach is costly, and in areas with frequent road construction, the information obtained may not meet the needs of autonomous driving. Therefore, some have proposed using public vehicle trajectory data as baseline data for standard road network speed limit mining. However, not every trajectory is suitable as baseline data, so confidence levels are needed to assess a trajectory's suitability as baseline data. Current confidence level calculation methods often rely on the accuracy of the hardware used to generate the trajectory; this approach is ineffective, especially when hardware issues arise. Therefore, calculating trajectory confidence levels and obtaining suitable trajectories as baseline data are pressing challenges. Summary of the Invention

[0003] The present application provides a trajectory confidence determination method, device, electronic device, storage medium and vehicle, which can effectively evaluate trajectory confidence so as to use trajectories with higher confidence to mark the speed limit values ​​of road network lanes, thereby improving the accuracy of the road network speed limit values.

[0004] In a first aspect, an embodiment of the present application provides a method for determining trajectory confidence, comprising: randomly selecting a trajectory as a target trajectory from trajectory matching road network data, obtaining target matching data of the target trajectory and at least one target road line matching the target trajectory; the trajectory matching road network data includes matching data of multiple trajectories and at least one road line matching each trajectory, the target matching data is used to indicate the target number of trajectory points in all trajectory points of the target trajectory that match each target road line, and the target length of the trajectory segment in the target trajectory that matches each target road line; based on the target number corresponding to each target road line and the reference trajectory, The method further comprises the following steps: determining a trajectory point matching ratio corresponding to each target road line based on a ratio of the number of target points; determining a trajectory length matching ratio corresponding to each target road line based on a ratio of the target length corresponding to each target road line to the total length of the target trajectory; and determining a target trajectory confidence level based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship. The preset trajectory confidence relationship is used to indicate the confidence level of the target trajectory and the corresponding relationship between the trajectory point matching ratio corresponding to each target road line and the trajectory length matching ratio corresponding to each target road line.

[0005] In some embodiments of the present application, a trajectory is randomly selected from trajectory matching road network data as a target trajectory, and target matching data of the target trajectory and at least one target road line matching the target trajectory is obtained, including: randomly selecting a trajectory from the trajectory matching road network data as the target trajectory, and obtaining initial matching data of the target trajectory and at least one target road line matching the target trajectory; filtering out data corresponding to trajectory points in the initial matching data whose error values ​​of the trajectory points are greater than or equal to an error threshold, to obtain the target matching data.

[0006] In some embodiments of the present application, the number of reference trajectory points is the sum of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the average of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the number of all trajectory points of the target trajectory.

[0007] In some embodiments of the present application, the number of reference trajectory points is the average of the number of targets corresponding to at least one target road line; based on the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points, the trajectory point matching ratio corresponding to each target road line is determined, including: determining the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points as the matching trajectory point ratio corresponding to each target road line; and normalizing the matching trajectory point ratio corresponding to each target road line to obtain the trajectory point matching ratio corresponding to each target road line.

[0008] In some embodiments of the present application, the normalization formula corresponding to the normalization process is:

[0009]

[0010] Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, min(p) is the minimum value among the ratios of matching trajectory points corresponding to at least one target road line, and max(p) is the maximum value among the ratios of matching trajectory points corresponding to at least one target road line;

[0011] Alternatively, the normalization formula corresponding to the normalization process is:

[0012]

[0013] Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, μ is the mean of the ratio of matching trajectory points corresponding to at least one target road line, and σ is the standard deviation of the ratio of matching trajectory points corresponding to at least one target road line.

[0014] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship is:

[0015]

[0016] Among them, o is the confidence of the target trajectory, h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0017] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship includes a first confidence formula and a second confidence formula, and the first confidence formula is:

[0018]

[0019] The second confidence formula is:

[0020]

[0021] Among them, o1 is the first confidence of the target trajectory, o2 is the first confidence of the target trajectory, and h i is the trajectory point matching ratio corresponding to the i-th target road line, k iis the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0022] In some embodiments of the present application, after determining the confidence of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship, the method further includes: if the confidence of the target trajectory meets the confidence requirement, determining the target trajectory as a valid trajectory mapping the speed limit value of at least one target road line; if the confidence of the target trajectory does not meet the confidence requirement, discarding the target trajectory.

[0023] In a second aspect, an embodiment of the present application provides a trajectory confidence determination device, comprising: an acquisition module, configured to randomly select a trajectory as a target trajectory from trajectory matching road network data, and obtain target matching data of the target trajectory and at least one target road line that matches the target trajectory; the trajectory matching road network data comprises matching data of multiple trajectories and at least one road line that matches each trajectory, the target matching data being used to indicate the target number of trajectory points that match each target road line in all trajectory points of the target trajectory, and the target length of the trajectory segment that matches each target road line in the target trajectory; a determination module, configured to determine the target length of each target road line based on the target road line corresponding to each target road line. The method includes determining a trajectory point matching ratio corresponding to each target road line based on the ratio of the number of target points to the number of reference trajectory points; determining a trajectory length matching ratio corresponding to each target road line based on the ratio of the target length corresponding to each target road line to the total length of the target trajectory; and determining the confidence of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship. The preset trajectory confidence relationship is used to indicate the confidence of the target trajectory and the corresponding relationship between the trajectory point matching ratio corresponding to each target road line and the trajectory length matching ratio corresponding to each target road line.

[0024] In some embodiments of the present application, the acquisition module is specifically configured to randomly select a trajectory as a target trajectory from the trajectory matching road network data, obtain initial matching data of the target trajectory and at least one target road line matching the target trajectory, and filter out data corresponding to trajectory points in the initial matching data whose error values ​​are greater than or equal to an error threshold to obtain target matching data.

[0025] In some embodiments of the present application, the number of reference trajectory points is the sum of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the average of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the number of all trajectory points of the target trajectory.

[0026] In some embodiments of the present application, the number of reference trajectory points is the average of the number of targets corresponding to at least one target road line; the determination module is specifically used to determine the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points as the matching trajectory point ratio corresponding to each target road line; the matching trajectory point ratio corresponding to each target road line is normalized to obtain the trajectory point matching ratio corresponding to each target road line.

[0027] In some embodiments of the present application, the normalization formula corresponding to the normalization process is:

[0028]

[0029] Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, min(p) is the minimum value among the ratios of matching trajectory points corresponding to at least one target road line, and max(p) is the maximum value among the ratios of matching trajectory points corresponding to at least one target road line;

[0030] Alternatively, the normalization formula corresponding to the normalization process is:

[0031]

[0032] Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, μ is the mean of the ratio of matching trajectory points corresponding to at least one target road line, and σ is the standard deviation of the ratio of matching trajectory points corresponding to at least one target road line.

[0033] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship is:

[0034]

[0035] Among them, o is the confidence of the target trajectory, h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0036] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship includes a first confidence formula and a second confidence formula, and the first confidence formula is:

[0037]

[0038] The second confidence formula is:

[0039]

[0040] Among them, o1 is the first confidence of the target trajectory, o2 is the first confidence of the target trajectory, and h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0041] In some embodiments of the present application, the determination module is further configured to, after determining the confidence of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship, determine the target trajectory as a valid trajectory that maps the speed limit value of at least one target road line if the confidence of the target trajectory meets the confidence requirement; and discard the target trajectory if the confidence of the target trajectory does not meet the confidence requirement.

[0042] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, wherein the processor is configured to execute a computer program stored in a memory, wherein when the computer program is executed by the processor, the steps of any one of the trajectory confidence determination methods provided in the first aspect are implemented.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the trajectory confidence determination methods provided in the first aspect.

[0044] In a fifth aspect, an embodiment of the present application provides a vehicle, comprising: a trajectory confidence determination device as described in the second aspect, or an electronic device as described in the third aspect, or a computer-readable storage medium as described in the fourth aspect.

[0045] In a sixth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a computer program or instructions. When the computer program product runs on a processor, the processor executes the computer program or instructions to implement the steps of the trajectory confidence determination method as described in the first aspect.

[0046] In the seventh aspect, an embodiment of the present application provides a chip, which includes a processor, a memory and a communication interface. The communication interface and the processor are coupled, the memory is used to store programs or instructions that can be run on the processor, and the processor is used to execute the programs or instructions to implement the steps of the trajectory confidence determination method as described in the first aspect.

[0047] The technical solution provided by the embodiments of the present application has the following advantages over the prior art: In the embodiments of the present application, target matching data corresponding to a target trajectory and at least one target road line matching the target trajectory is obtained from trajectory matching road network data; a trajectory point matching ratio corresponding to each target road line is determined based on the ratio of the number of targets corresponding to each target road line (the number of trajectory points in all trajectory points of the target trajectory that match each target road line) to the number of reference trajectory points (used to indicate the proportion of the trajectory points in the trajectory that cover the standard road network); a trajectory length matching ratio corresponding to each target road line is determined based on the ratio of the target length corresponding to each target road line (the target length of the trajectory segment in the target trajectory that matches each target road line) to the total length of the target trajectory (used to indicate the proportion of the trajectory length that covers the road network); and the confidence of the target trajectory is determined based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship. Under normal circumstances, if the track points in a trajectory cover a large proportion of the standard road network and the track length covers a large proportion of the road network, it means that the corresponding speed of this trajectory is mostly uniform or relatively high. Therefore, the speed limit value of the track points corresponding to the trajectory is closer to the actual speed limit value of the road. Therefore, the confidence of the trajectory is also higher. Using this trajectory to mine the speed limit of the standard road network can improve the accuracy of the road network speed limit value. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 A schematic diagram of a road segment and lane group provided in this application;

[0051] Figure 2 A flow chart of a trajectory confidence determination method provided in this application;

[0052] Figure 3 A flowchart of another trajectory confidence determination method provided by this application;

[0053] Figure 4 A flowchart of another trajectory confidence determination method provided by this application;

[0054] Figure 5 A flowchart of another trajectory confidence determination method provided in this application;

[0055] Figure 6 A schematic diagram of the structure of a trajectory confidence determination device provided by this application;

[0056] Figure 7 A schematic diagram of the hardware structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0057] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application can also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present application, not all of the embodiments.

[0059] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "first," "second," and the like generally distinguish objects of a class and do not limit the number of objects. For example, the first object may be one or more.

[0060] First, some nouns or terms involved in the claims and description of the present invention are explained below.

[0061] Tile: A map tile is a file that contains a slice of the map at a certain scale and within a certain map area. Map tiles are organized in a pyramid structure, and each tile is uniquely identified by its level, row, and column number.

[0062] Map match: Trajectory binding to the road network is a method of calculating which roads the trajectory passes through in the road network and determining the matching relationship between roads and trajectories.

[0063] The trajectory is the data generated during the vehicle's driving process. The trajectory can be composed of multiple trajectory points, where each trajectory point can include various specific information, such as specific location information, time information, speed information, etc.

[0064] Electronic maps are mainly divided into high-precision maps and standard maps.

[0065] Standard maps primarily provide positioning, search, and route planning capabilities in non-assisted or non-autonomous driving scenarios, and they cover the entire road network. Standard maps are composed of topological network elements based on road links, with meter-level accuracy. They only depict the location and shape of roads, resulting in relatively low accuracy, reaching only the road level, not the lane level.

[0066] The standard map is represented by the standard (SD) road network data. The roads in the standard map are represented by road lines, and each road can be composed of one or more road lines. For example, Figure 1 Figure (a) shows a schematic diagram of a road on a standard map. A road can be composed of road line A and road line B. That is, a road can be divided into multiple segments, each corresponding to a road line. A road line is composed of sampling points and is a digitized representation of an actual road. Sampling points are essential for describing a road line. A road line has at least two sampling points. If a road line represents a relatively straight road or a section of a relatively straight road, it can have only two sampling points.

[0067] In this embodiment, a road line can be a directional line segment, i.e., a vector. Road line data is the data for each sampling point on this road. Obtaining road lines and their corresponding road line data from a standard map involves obtaining the data for each sampling point corresponding to each road line from the standard road network data corresponding to the standard map.

[0068] High-precision maps are reconstructions of the road environment. They contain extensive information on lane locations, types, widths, and road elements such as traffic lights and signs, forming an accurate three-dimensional representation of the road network. High-precision maps are primarily used for assisted or autonomous driving, and they target specific areas only, not the entire road network. Because they are based on lane-level topological network elements, high-precision maps offer higher accuracy than standard maps, reaching the lane level. They also offer richer data dimensions, including detailed information such as lane line type, lane width, and roadside landmarks.

[0069] High-precision maps are represented by high-precision road network data. Roads in high-precision maps are represented by lane groups, and each road can be composed of one or more lane groups. Figure 1(b) in the figure shows a schematic diagram of a road in an HD map. A road can be composed of lane group A and lane group B, that is, a road can be divided into multiple sections, each section corresponding to a lane group. Each lane group includes multiple actual lane lines and at least one virtual lane line. Actual lane lines are lane lines actually marked on the road; virtual lane lines are virtual lines drawn between two actual lane lines. Virtual lane lines are generally the center lines between two actual lane lines. Virtual lane lines are generally not marked on the road, but they are needed in assisted driving or autonomous driving. For example, when the vehicle is autonomously driving, it needs to drive along virtual lane lines. Therefore, virtual lane lines are included in the lane groups of the HD map.

[0070] Both actual lane lines and virtual lane lines are composed of densely distributed points that describe the shape of the corresponding lane lines. Points on lane lines are generally referred to as shape points. Adjacent lane groups in a high-precision map are separated by lane group connectors, with the area between two adjacent lane group connectors forming a lane group. Each actual lane line and each virtual lane line in a lane group is a directional vector. The actual lane lines and virtual lane lines in the same lane have the same direction, which is the same as the direction vehicles are permitted to travel within the lane. Lane line data is the data for each shape point in the lane. To obtain lane groups and their corresponding lane group data in a high-precision map, the data for the actual lane line shape points and the data for the virtual lane line shape points in each lane group are obtained from the high-precision road network data corresponding to the high-precision map. For example, if a highway has three lanes, each lane group in the corresponding high-precision map includes four actual lane lines and three virtual lane lines. The four actual lane lines are used to distinguish the three lanes, and a virtual lane line is included between the two actual lane lines in each lane. For example, if the three lanes on a highway are divided into sections of 50 meters, then the four actual lane lines and three virtual lane lines within 50 meters form a lane group. Figure 1 Lane group A and lane group B are shown in (b) of FIG. On the same road, the road line and the lane group may not completely match. The length of the road segment indicated by the road line is usually longer than the length of the road segment indicated by the lane group. A lane group may correspond to one road line or two road lines (part of the lane group corresponds to one road line, and the other part of the lane group corresponds to the other road line).

[0071] The present application is applied to the scenario of determining the confidence of the trajectory. The electronic device in the embodiment of the present application can be a vehicle-mounted terminal, or a mobile phone, notebook, computer, etc. that is connected to the vehicle for communication. The specific method can be determined according to actual conditions and is not limited here.

[0072] The technical solution of this application is explained in detail below through several specific embodiments.

[0073] Figure 2 A flow chart of a trajectory confidence determination method provided in this application is shown as follows: Figure 2 As shown, the trajectory confidence determination method may include the following steps 101 to 104.

[0074] 101. Randomly select a trajectory from the trajectory-matching road network data as a target trajectory, and obtain target matching data for the target trajectory and at least one target road line that matches the target trajectory. The trajectory-matching road network data includes matching data for multiple trajectories and at least one road line that matches each trajectory. The target matching data indicates a target number of trajectory points that match each target road line among all trajectory points in the target trajectory, and a target length of a trajectory segment that matches each target road line in the target trajectory.

[0075] The trajectory matching road network data is used to indicate the matching relationship data between multiple trajectories in the target area and multiple road lines in the target area. The multiple trajectories in the target area can be all trajectories in the target area or a portion of the trajectories in the target area, which is not limited here. The multiple road lines in the target area can be all road lines in the target area.

[0076] The target area may be a tile in a map (such as a standard map), or an area where a province is located, or an area where a highway is located. The specific area may be determined based on actual conditions and is not limited here.

[0077] A trajectory is the historical driving trajectory of a user's vehicle, and a trajectory consists of multiple track points. Trajectory data represents a trajectory, and the trajectory data corresponding to a trajectory includes the data of each track point in the trajectory (including the speed limit of the track point) and the track length data.

[0078] The at least one target road line matched with the target trajectory is one or more road lines among the multiple road lines in the target area, the indicated road section of which overlaps with the road section indicated by the target trajectory.

[0079] For example, based on the latitude and longitude information in the data corresponding to the target trajectory and the latitude and longitude information in the data corresponding to each road line, it is possible to determine whether the road section indicated by the target trajectory overlaps with the road section indicated by a road line, and then determine the road line with the overlapping indicated road section as the target road line that matches the target trajectory.

[0080] For example, it is also possible to determine whether the road section indicated by the target trajectory overlaps with the road section indicated by a road line based on the road section identifier in the data corresponding to the target trajectory and the road section identifier in the data corresponding to each road line, and then determine the road line with the overlapping indicated road section as the target road line that matches the target trajectory.

[0081] In some embodiments of the present application, before the above 101, the trajectory matching road network data can be obtained through the following steps 201 to 203.

[0082] 201. Obtain road network data corresponding to multiple road lines within a target area from road network data corresponding to a standard map.

[0083] The specific method for obtaining the road network data within the target area in the road network data corresponding to the standard map can refer to existing related technologies and is not limited here.

[0084] Exemplarily, the road network data corresponding to the standard map is filtered according to the spatial boundary corresponding to the target area. The filtering method is to generate a regional space polygon based on the spatial boundary of the target area, filter the road lines that completely fall within the regional space polygon and the road lines that partially fall within the regional space polygon, and obtain road network data corresponding to multiple road lines.

[0085] 202. Obtain trajectory data corresponding to multiple trajectories in the target area from a vehicle driving trajectory database, where the vehicle driving trajectory database includes vehicle driving trajectory data in the target area and other areas.

[0086] The specific method for obtaining the trajectory data corresponding to the multiple trajectories in the target area from the vehicle driving trajectory database can refer to the existing related technologies and is not limited here.

[0087] Exemplarily, the trajectory data in the vehicle driving trajectory database is filtered according to the spatial boundary corresponding to the target area. The filtering method is to generate a regional spatial polygon according to the spatial boundary of the target area, filter the trajectories that are completely within the spatial polygon and the trajectories that are partially within the spatial polygon, and obtain trajectory data corresponding to multiple trajectories.

[0088] 203. Perform trajectory matching road network processing on the road network data corresponding to the multiple road lines in the target area and the trajectory data corresponding to the multiple trajectories in the target area to obtain trajectory matching road network data corresponding to the multiple trajectories and at least one road line respectively matched to each trajectory.

[0089] The trajectory matching road network processing can be implemented based on the trajectory binding road network (map matching) technology, or it can be implemented through other methods. For details, please refer to the existing related technologies and will not be described in detail here.

[0090] Different trajectories can match the same or different numbers of road lines. This means any trajectory can match one or multiple road lines, without any limitation. A road line is the smallest unit of a road; a trajectory can match multiple road lines, and different trajectories can also match the same road line. In other words, a trajectory can match two or more road lines (for example, at least one road line matching trajectory 1 includes road line 1, and at least one road line matching trajectory 2 also includes road line 1).

[0091] Any trajectory point on the target trajectory may match only one target road line, or may not match any target road line.

[0092] The target matching data is used to indicate the target number of trajectory points that match each target road line among all trajectory points in the target trajectory, as well as the target length of the trajectory segment that matches each target road line in the target trajectory. It can be understood that the target matching data includes the target number corresponding to each target road line and the target length corresponding to each target road line; it can also be understood that the target number corresponding to each target road line and the target length corresponding to each target road line can be calculated based on the target matching data; the specific details can be determined based on actual conditions and are not limited here.

[0093] If the number of targets corresponding to each target road line and the target length corresponding to each target road line can be calculated based on the target matching data, then after the above step 101, it is necessary to determine the number of trajectory points that match each target road line among all trajectory points in the target trajectory to obtain the number of targets corresponding to each target road line; it is also necessary to determine the length of the trajectory segment that matches each target road line in the target trajectory to obtain the target length corresponding to each target road line.

[0094] The target length corresponding to a target road line is the total length of at least one trajectory segment in the target trajectory that matches the target road line. If the target trajectory corresponds to a target road line with round-trip motion, the sum of the lengths of the trajectory segments of the target trajectory corresponding to the round-trip motion on the target road line is determined as the target length corresponding to the target road line.

[0095] For example, Figure 3As shown, trajectory A matches road line 1, road line 2, and road line 3. Trajectory A moves back and forth on road line 2 and road line 3, respectively, i.e., there are two matching trajectory segments on each. The number of targets corresponding to road line 1 is 5 (i.e., the number of trajectory points of trajectory A falling within road segment 1), the number of targets corresponding to road line 2 is 6 (i.e., the number of trajectory points of trajectory A falling within road segment 2), and the number of targets corresponding to road line 3 is 8 (i.e., the number of trajectory points of trajectory A falling within road segment 3). The target length corresponding to road line 1 is the length of the trajectory segment of trajectory A falling within road segment 1, the target length corresponding to road line 2 is the sum of the lengths of the two trajectory segments of trajectory A falling within road segment 2, and the target length corresponding to road line 3 is the sum of the lengths of the two trajectory segments of trajectory A falling within road segment 3.

[0096] 102. Determine a trajectory point matching ratio corresponding to each target road line based on a ratio of the number of targets corresponding to each target road line to the number of reference trajectory points.

[0097] Among them, the trajectory point matching ratio can be the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points, or it can be the value after normalizing the ratio of the number of targets corresponding to the target road line to the number of reference trajectory points, or it can be the product of the ratio of the number of targets corresponding to the target road line to the number of reference trajectory points and a preset coefficient. The specific value can be determined according to actual conditions and is not limited here.

[0098] The preset coefficient may be a number greater than 0 and less than 1.

[0099] In some embodiments of the present application, the number of reference trajectory points is the sum of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the average of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the number of all trajectory points of the target trajectory.

[0100] It can be understood that the number of reference trajectory points can be the sum of the target numbers corresponding to at least one target road line, or the number of reference trajectory points can be the average of the target numbers corresponding to at least one target road line; the number of reference trajectory points can also be the number of all trajectory points of the target trajectory, or the number of reference trajectory points can be other values, which can be determined according to actual conditions and are not limited here.

[0101] Among them, the trajectory point matching ratio is used to indicate the proportion of trajectory points in the trajectory covering the standard road network. The larger the trajectory point matching ratio, the greater the proportion of trajectory points in the trajectory covering the standard road network.

[0102] For example, Figure 3As shown, assuming that the number of reference trajectory points is the average of the number of targets corresponding to at least one target road line, and the trajectory point matching ratio is the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points, the number of reference trajectory points is (5+6+8) / 3=19 / 3, the trajectory point matching ratio corresponding to road line 1 is 5 / (19 / 3)=15 / 19, the trajectory point matching ratio corresponding to road line 2 is 6 / (19 / 3)=18 / 19, and the trajectory point matching ratio corresponding to road line 3 is 8 / (19 / 3)=24 / 19.

[0103] 103. Determine a trajectory length matching ratio corresponding to each target road line based on a ratio of a target length corresponding to each target road line to a total length of the target trajectory.

[0104] The trajectory length matching ratio may be the ratio of the target length corresponding to each target road line to the total length of the target trajectory; it may also be the value obtained by normalizing the ratio of the target length corresponding to each target road line to the total length of the target trajectory; or it may be the product of the ratio of the target length corresponding to each target road line to the total length of the target trajectory and a predetermined coefficient. The specific value may be determined based on actual conditions and is not limited here.

[0105] The predetermined coefficient may be a number greater than 0 and less than 1.

[0106] Among them, the trajectory length matching ratio is used to indicate the proportion of the road network covered by the trajectory length. The larger the trajectory length matching ratio, the greater the proportion of the road network covered by the trajectory length.

[0107] The formula for the trajectory length matching ratio can be:

[0108]

[0109] Among them, k i is the trajectory length matching ratio corresponding to the i-th target road line, L i is the length of the target trajectory covering the i-th target road line, and L is the total length of the target trajectory.

[0110] 104. Determine the confidence of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship.

[0111] The preset trajectory confidence relationship is used to indicate the confidence of the target trajectory, and the corresponding relationship between the trajectory point matching ratio corresponding to each target road line and the trajectory length matching ratio corresponding to each target road line.

[0112] The preset trajectory confidence relationship may be a trajectory confidence function relationship, which may be specifically determined based on actual conditions and is not limited here.

[0113] In an embodiment of the present application, target matching data corresponding to a target trajectory and at least one target road line that matches the target trajectory is obtained from trajectory matching road network data; a trajectory point matching ratio corresponding to each target road line (used to indicate the proportion of the trajectory points in the trajectory that cover the standard road network) is determined based on the ratio of the number of targets corresponding to each target road line (the number of trajectory points in all trajectory points of the target trajectory that match each target road line) to the number of reference trajectory points; a trajectory length matching ratio corresponding to each target road line (used to indicate the proportion of the road network covered by the trajectory length) is determined based on the ratio of the target length corresponding to each target road line (the target length of the trajectory segment in the target trajectory that matches each target road line) to the total length of the target trajectory; and a confidence level of the target trajectory is determined based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence level relationship. Under normal circumstances, if the track points in a trajectory cover a large proportion of the standard road network and the track length covers a large proportion of the road network, it means that the corresponding speed of this trajectory is mostly uniform or relatively high. Therefore, the speed limit value of the track points corresponding to the trajectory is closer to the actual speed limit value of the road. Therefore, the confidence of the trajectory is also higher. Using this trajectory to mine the speed limit of the standard road network can improve the accuracy of the road network speed limit value.

[0114] In some embodiments of the present application, the above step 101 can be specifically implemented through the following steps 101a and 101b.

[0115] 101a. Randomly select a trajectory from the trajectory matching road network data as a target trajectory, and obtain initial matching data of the target trajectory and at least one target road line matching the target trajectory.

[0116] 101b. Filter out data corresponding to trajectory points whose error values ​​in the initial matching data are greater than or equal to the error threshold to obtain target matching data.

[0117] The trajectory data corresponding to the trajectory includes the error value of each trajectory point. When GPS is collected, the corresponding error value will be directly generated according to the number of GPS satellites searched and the strength of the GPS signal.

[0118] Trajectory points with larger error values ​​will affect the judgment of trajectory confidence. Therefore, trajectory points whose corresponding error values ​​are greater than or equal to the error threshold can be filtered out to improve the accuracy of trajectory confidence.

[0119] In some embodiments of the present application, the number of reference trajectory points is the average of the target numbers corresponding to at least one target road line; Figure 2 ,like Figure 4 As shown, the above step 102 can be specifically implemented through the following steps 102a and 102b.

[0120] 102a. Determine the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points as the matching trajectory point ratio corresponding to each target road line.

[0121] 102b. Normalize the ratio of the matching trajectory points corresponding to each target road line to obtain the trajectory point matching ratio corresponding to each target road line.

[0122] The matching trajectory point ratio is the ratio of the number of targets corresponding to each target road line to the average number of targets corresponding to at least one target road line.

[0123] In the embodiment of the present application, the number of reference trajectory points is the average of the number of targets corresponding to at least one target road line, and after the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points is normalized, the numerical value is easy to quantify and the data fluctuation is small, thereby improving processing efficiency.

[0124] In some embodiments of the present application, the normalization formula corresponding to the normalization process is:

[0125]

[0126] Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, min(p) is the minimum value of the ratio of matching trajectory points corresponding to at least one target road line, and max(p) is the maximum value of the ratio of matching trajectory points corresponding to at least one target road line.

[0127] In some embodiments of the present application, the normalization formula corresponding to the normalization process is:

[0128]

[0129] Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, μ is the mean of the ratio of matching trajectory points corresponding to at least one target road line, and σ is the standard deviation of the ratio of matching trajectory points corresponding to at least one target road line.

[0130] In the embodiment of the present application, the normalization formula corresponding to the normalization process may also be other formulas, which can be determined based on actual conditions and are not limited here.

[0131] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship is:

[0132]

[0133] Among them, o is the confidence of the target trajectory, h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0134] In the embodiment of the present application, the trajectory confidence formula is a weighted product of the trajectory point matching ratio and the trajectory length matching ratio corresponding to each road line matched with the trajectory, which can better represent the confidence of the trajectory.

[0135] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship includes a first confidence formula and a second confidence formula, and the first confidence formula is:

[0136]

[0137] The second confidence formula is:

[0138]

[0139] Among them, o1 is the first confidence of the target trajectory, o2 is the first confidence of the target trajectory, and h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0140] The trajectory confidence formula is the weighted matching ratio of the trajectory points corresponding to each road line matched with the trajectory, and the weighted matching ratio of the trajectory length corresponding to each road line matched with the trajectory. Then the two confidence formulas are combined to better represent the confidence of the trajectory.

[0141] In some embodiments of the present application, after step 104 above, the trajectories may be screened based on their confidence levels so that trajectories with higher confidence levels can be used in the trajectory mining algorithm, for example, in a scenario where the speed limit values ​​of the trajectories are mapped to the speed limit values ​​of the standard map road network routes.

[0142] In some embodiments of the present application, Figure 4 ,like Figure 5As shown, after the above step 104, the trajectory confidence determination method provided in the embodiment of the present application may further include the following steps 106 and 107.

[0143] 106. When the confidence level of the target trajectory meets the confidence level requirement, determine the target trajectory as a valid trajectory that maps the speed limit value of at least one target road line.

[0144] 107. When the confidence of the target trajectory does not meet the confidence requirement, the target trajectory is discarded.

[0145] When the confidence of the trajectory is a value (o0), the confidence requirement is that the confidence of the trajectory is greater than or equal to the confidence threshold, that is, when the confidence of the target trajectory is greater than or equal to the confidence threshold, the confidence of the target trajectory meets the confidence requirement; when the confidence of the target trajectory is less than the confidence threshold, the confidence of the target trajectory does not meet the confidence requirement.

[0146] When the confidence of the trajectory is the first confidence (o1) and the second confidence (o2), the confidence requirement is that the first confidence of the trajectory is greater than or equal to the first confidence threshold, and the second confidence of the trajectory is greater than or equal to the second confidence threshold, that is, when the first confidence of the target trajectory is greater than or equal to the first confidence threshold, and the second confidence of the target trajectory is greater than or equal to the second confidence threshold, the confidence of the target trajectory meets the confidence requirement; when the first confidence of the target trajectory is less than the first confidence threshold, or the second confidence of the target trajectory is less than the second confidence threshold, the confidence of the target trajectory does not meet the confidence requirement.

[0147] In the embodiment of the present application, whether the confidence of the trajectory meets the confidence requirement is determined, and then whether to use the target trajectory to perform speed limit mining on the road network of the standard map is determined.

[0148] This application also provides a trajectory confidence determination device, Figure 6 This is a schematic diagram of the structure of a trajectory confidence determination device provided by this application, such as Figure 6 As shown, the trajectory confidence determination device includes:

[0149] The acquisition module 601 is used to randomly select a trajectory as a target trajectory from the trajectory matching road network data, and obtain target matching data of the target trajectory and at least one target road line matching the target trajectory; the trajectory matching road network data includes matching data of multiple trajectories and at least one road line matching each trajectory, and the target matching data is used to indicate the target number of trajectory points in all trajectory points of the target trajectory that match each target road line, and the target length of the trajectory segment in the target trajectory that matches each target road line; the determination module 602 is used to determine the target length of the target trajectory segment based on the ratio of the target number corresponding to each target road line to the number of reference trajectory points. The method further comprises the steps of: determining a trajectory point matching ratio corresponding to each target road line based on a value; determining a trajectory length matching ratio corresponding to each target road line based on a ratio of a target length corresponding to each target road line to the total length of the target trajectory; and determining a confidence level of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship. The preset trajectory confidence relationship is used to indicate a corresponding relationship between the confidence level of the target trajectory and the trajectory point matching ratio corresponding to each target road line and the trajectory length matching ratio corresponding to each target road line.

[0150] In some embodiments of the present application, the acquisition module 601 is specifically configured to randomly select a trajectory as a target trajectory from the trajectory matching road network data, obtain initial matching data of the target trajectory and at least one target road line matching the target trajectory, and filter out data corresponding to trajectory points in the initial matching data whose error values ​​are greater than or equal to an error threshold to obtain target matching data.

[0151] In some embodiments of the present application, the number of reference trajectory points is the sum of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the average of the target numbers corresponding to at least one target road line; or, the number of reference trajectory points is the number of all trajectory points of the target trajectory.

[0152] In some embodiments of the present application, the number of reference trajectory points is the average of the number of targets corresponding to at least one target road line; the determination module 602 is specifically used to determine the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points as the matching trajectory point ratio corresponding to each target road line; and normalize the matching trajectory point ratio corresponding to each target road line to obtain the trajectory point matching ratio corresponding to each target road line.

[0153] In some embodiments of the present application, the normalization formula corresponding to the normalization process is:

[0154]

[0155] Among them, hi is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, min(p) is the minimum value among the ratios of matching trajectory points corresponding to at least one target road line, and max(p) is the maximum value among the ratios of matching trajectory points corresponding to at least one target road line;

[0156] Alternatively, the normalization formula corresponding to the normalization process is:

[0157]

[0158] Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, μ is the mean of the ratio of matching trajectory points corresponding to at least one target road line, and σ is the standard deviation of the ratio of matching trajectory points corresponding to at least one target road line.

[0159] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship is:

[0160]

[0161] Among them, o is the confidence of the target trajectory, h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0162] In some embodiments of the present application, the trajectory confidence formula corresponding to the preset trajectory confidence relationship includes a first confidence formula and a second confidence formula, and the first confidence formula is:

[0163]

[0164] The second confidence formula is:

[0165]

[0166] Among them, o1 is the first confidence of the target trajectory, o2 is the first confidence of the target trajectory, and h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of at least one target road line.

[0167] In some embodiments of the present application, the determination module 602 is further configured to, after determining the confidence of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship, determine the target trajectory as a valid trajectory that maps the speed limit value of at least one target road line if the confidence of the target trajectory meets the confidence requirement; and discard the target trajectory if the confidence of the target trajectory does not meet the confidence requirement.

[0168] It should be noted that the above-mentioned trajectory confidence determination device can be the electronic device in the above-mentioned method embodiment of the present application, or it can be a functional module and / or functional entity in the electronic device that can realize the functions of the device embodiment, and the embodiment of the present application does not limit it.

[0169] In the embodiment of the present application, each module can implement the trajectory confidence determination method provided in the above method embodiment and achieve the same technical effect. To avoid repetition, it will not be described here.

[0170] Figure 7 The structural diagram of an electronic device provided in an embodiment of the present application is used to exemplify the electronic device that implements the method for determining the confidence level of an arbitrary trajectory in an embodiment of the present application, and should not be understood as a specific limitation on the embodiment of the present application.

[0171] like Figure 7 As shown, the electronic device 700 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the electronic device 700 are also stored in the RAM 703. The processor 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0172] Typically, the following devices may be connected to the I / O interface 705: an input device 706 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 708 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 709. The communication device 709 may allow the electronic device 700 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 700 is shown as having various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0173] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processor 701, the functions defined in the arbitrary trajectory confidence determination method provided in the embodiment of the present application can be executed.

[0174] It should be noted that the computer-readable medium mentioned above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0175] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0176] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0177] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: randomly select a trajectory as a target trajectory from the trajectory matching road network data, and obtain target matching data of the target trajectory and at least one target road line matching the target trajectory; the trajectory matching road network data includes matching data of multiple trajectories and at least one road line matching each trajectory, and the target matching data is used to indicate the target number of trajectory points that match each target road line in all trajectory points of the target trajectory, and the target length of the trajectory segment that matches each target road line in the target trajectory; based on the target road line corresponding to each target road line, the target road line is matched to the target road line. The method further comprises determining a trajectory point matching ratio corresponding to each target road line based on a ratio of the number of targets to the number of reference trajectory points; determining a trajectory length matching ratio corresponding to each target road line based on a ratio of the target length corresponding to each target road line to the total length of the target trajectory; and determining a confidence level of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship. The preset trajectory confidence relationship is used to indicate the confidence level of the target trajectory and the corresponding relationship between the trajectory point matching ratio corresponding to each target road line and the trajectory length matching ratio corresponding to each target road line.

[0178] In an embodiment of the present application, a computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include but are not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the computer, partially on the computer, as an independent software package, partially on the computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0179] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0180] The units involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of a unit does not, in some cases, constitute a limitation on the unit itself.

[0181] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0182] In the context of the present application, computer-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of computer-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0183] An embodiment of the present application further provides a vehicle, comprising: the above-mentioned trajectory confidence determination device, or the above-mentioned electronic device, or the above-mentioned computer-readable storage medium.

[0184] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

[0185] In addition, although adopting specific order to describe each operation, this should not be interpreted as requiring these operations to be executed in the specific order shown or in sequential order.Under certain environment, multitasking and parallel processing may be advantageous.Similarly, although comprising some specific implementation details in the above discussion, these should not be interpreted as limiting the scope of the application.Some features described in the context of separate embodiment can also be implemented in a single embodiment in combination.On the contrary, the various features described in the context of a single embodiment also can be implemented in multiple embodiments individually or in the mode of any suitable subcombination.

[0186] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A method for determining trajectory confidence, characterized in that: The method comprises: Randomly selecting a trajectory as a target trajectory from the trajectory matching road network data, and obtaining target matching data of the target trajectory and at least one target road line matching the target trajectory; the trajectory matching road network data includes matching data of multiple trajectories and at least one road line matching each trajectory, and the target matching data is used to indicate a target number of trajectory points respectively matching each target road line among all trajectory points of the target trajectory, and a target length of a trajectory segment respectively matching each target road line in the target trajectory; Determining a trajectory point matching ratio corresponding to each target road line based on a ratio of the number of targets corresponding to each target road line to the number of reference trajectory points; Determining a trajectory length matching ratio corresponding to each target road line based on a ratio of a target length corresponding to each target road line to a total length of the target trajectory; The confidence of the target trajectory is determined based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship, wherein the preset trajectory confidence relationship is used to indicate the confidence of the target trajectory and the corresponding relationship between the trajectory point matching ratio corresponding to each target road line and the trajectory length matching ratio corresponding to each target road line.

2. The method according to claim 1, characterized in that The step of randomly selecting a trajectory as a target trajectory from the trajectory matching road network data and obtaining target matching data of the target trajectory and at least one target road line matching the target trajectory includes: Randomly select a trajectory from the trajectory matching road network data as a target trajectory, and obtain initial matching data of the target trajectory and at least one target road line matching the target trajectory; The data corresponding to the trajectory points whose error values ​​in the initial matching data are greater than or equal to the error threshold are filtered out to obtain the target matching data.

3. The method according to claim 1, characterized in that The number of reference trajectory points is the sum of the number of targets corresponding to the at least one target road line; or, The number of reference trajectory points is the average of the target numbers corresponding to the at least one target road line; or, The number of reference trajectory points is the number of all trajectory points of the target trajectory.

4. The method according to claim 3, characterized in that The number of reference trajectory points is the average of the target numbers corresponding to the at least one target road line; The determining the trajectory point matching ratio corresponding to each target road line based on the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points includes: Determine the ratio of the number of targets corresponding to each target road line to the number of reference trajectory points as the matching trajectory point ratio corresponding to each target road line; The matching trajectory point ratios corresponding to each target road line are normalized to obtain the trajectory point matching ratios corresponding to each target road line.

5. The method according to claim 4, characterized in that The normalization formula corresponding to the normalization process is: Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, min(p) is the minimum value among the ratios of matching trajectory points corresponding to the at least one target road line, and max(p) is the maximum value among the ratios of matching trajectory points corresponding to the at least one target road line; Alternatively, the normalization formula corresponding to the normalization process is: Among them, h i is the trajectory point matching ratio corresponding to the i-th target road line, p i is the ratio of matching trajectory points corresponding to the i-th target road line, μ is the mean of the ratios of matching trajectory points corresponding to the at least one target road line, and σ is the standard deviation of the ratios of matching trajectory points corresponding to the at least one target road line.

6. The method according to claim 1, characterized in that The trajectory confidence formula corresponding to the preset trajectory confidence relationship is: Among them, o is the confidence of the target trajectory, h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of the at least one target road line.

7. The method according to claim 1, characterized in that The trajectory confidence formula corresponding to the preset trajectory confidence relationship includes a first confidence formula and a second confidence formula, and the first confidence formula is: The second confidence formula is: Among them, o1 is the first confidence of the target trajectory, o2 is the first confidence of the target trajectory, and h i is the trajectory point matching ratio corresponding to the i-th target road line, k i is the trajectory length matching ratio corresponding to the i-th target road line, and N is the number of the at least one target road line.

8. The method according to any one of claims 1 to 7, characterized in that After determining the confidence of the target trajectory based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship, the method further includes: In a case where the confidence of the target trajectory meets the confidence requirement, determining the target trajectory as a valid trajectory mapping the speed limit value of the at least one target road line; If the confidence of the target trajectory does not meet the confidence requirement, the target trajectory is discarded.

9. A trajectory confidence determination device, characterized in that: include: an acquisition module, configured to randomly select a trajectory as a target trajectory from the trajectory matching road network data, and acquire target matching data of the target trajectory and at least one target road line matching the target trajectory; the trajectory matching road network data includes matching data of multiple trajectories and at least one road line matching each trajectory, and the target matching data is used to indicate a target number of trajectory points respectively matching each target road line among all trajectory points of the target trajectory, and a target length of a trajectory segment respectively matching each target road line in the target trajectory; A determination module, configured to determine a trajectory point matching ratio corresponding to each target road line based on a ratio of the number of targets corresponding to each target road line to the number of reference trajectory points; Determining a trajectory length matching ratio corresponding to each target road line based on a ratio of a target length corresponding to each target road line to a total length of the target trajectory; The confidence of the target trajectory is determined based on the trajectory point matching ratio corresponding to each target road line, the trajectory length matching ratio corresponding to each target road line, and a preset trajectory confidence relationship, wherein the preset trajectory confidence relationship is used to indicate the confidence of the target trajectory and the corresponding relationship between the trajectory point matching ratio corresponding to each target road line and the trajectory length matching ratio corresponding to each target road line.

10. An electronic device, characterized in that: include: A processor, wherein the processor is used to execute a computer program stored in a memory, wherein the computer program, when executed by the processor, implements the steps of the trajectory confidence determination method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the trajectory confidence determination method according to any one of claims 1 to 8 are implemented.

12. A vehicle, characterized in that: include: The trajectory confidence determination device as claimed in claim 9, or the electronic device as claimed in claim 10, or including the computer-readable storage medium as claimed in claim 11.

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