Road line confidence coefficient determination method and device, equipment, medium and vehicle

Abstract

The invention relates to a road line confidence coefficient determination method and apparatus, a device, a medium and a vehicle. The method comprises the steps of obtaining target matching data of a target road line and at least one target track matched with the target road line from track matching road network data; determining a track point matching ratio corresponding to the target road line based on a ratio of the number of matching track points corresponding to the target road line to the number of reference track points; determining a track length matching ratio corresponding to the target road line based on the ratio of the effective matching length corresponding to the target road line to the total length of the target road line; based on the average speed corresponding to each road line, carrying out speed normalization processing on the average speed corresponding to the target road line to obtain a speed standard value corresponding to the target road line; and determining the confidence of the target road line based on the track point matching ratio corresponding to the target road line, the track length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a method, device, electronic device, storage medium and vehicle for determining road line confidence. Background Art

[0002] As one of the standard electronic maps, it can provide necessary data information for the operation of functions such as assisted driving and the production of high-precision maps, such as the speed limit of road lines in the road; the existing method of assigning speed limit values ​​in standard maps is mainly to use professional surveying and mapping vehicles to collect speed limit values ​​and then complete the assignment work; at the same time, in order to ensure that map data can be updated in a timely manner, it is necessary to use surveying and mapping vehicles frequently, but this method is costly. For example, when there is frequent road construction in a certain area, the information obtained by this method may not meet actual needs.

[0003] Therefore, some people have proposed a technology that uses the driving trajectory data of social vehicles as basic data to mine the speed limit of the standard road network. However, the speed limit values ​​of the road lines in the actual roads are not necessarily accurate. Therefore, it is necessary to use confidence to identify whether the speed limit values ​​of the road lines are suitable for the operation of functions such as assisted driving, or whether they are suitable for the production of high-precision maps.

[0004] At present, the confidence calculation method is mostly based on the hardware accuracy of the generated trajectory; this method does not work well in practice, especially when there are hardware problems. Therefore, how to obtain a more accurate confidence value of the road line speed limit value has become an urgent problem to be solved. Summary of the invention

[0005] The present application provides a road line confidence determination method, device, electronic device, storage medium and vehicle, which can effectively evaluate the confidence of a road line and provide accurate driving advice to users.

[0006] In a first aspect, an embodiment of the present application provides a method for determining a road line confidence, comprising: randomly selecting a road line as a target road line from trajectory matching road network data, and obtaining target matching data of the target road line and at least one target trajectory matching the target road line; the trajectory matching road network data comprises matching data of multiple road lines and at least one trajectory respectively matching each road line, and the target matching data is used to indicate the number of matching trajectory points and the effective matching length corresponding to the target road line; the number of matching trajectory points is the total number of trajectory points matching a road line among all trajectory points of at least one trajectory matching a road line, and the effective matching length is the maximum value of the lengths of the parts of a road line respectively matching each trajectory;

[0007] Determining a trajectory point matching ratio corresponding to the target road line based on a ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points;

[0008] Determine a trajectory length matching ratio corresponding to the target road line based on a ratio of an effective matching length corresponding to the target road line to a total length of the target road line;

[0009] Based on the average speed corresponding to each road line, the average speed corresponding to the target road line is normalized to obtain the speed standard value corresponding to the target road line; the average speed is the average of the speed limit values ​​of each sampling point on a road line;

[0010] Based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship, the confidence of the target road line is determined to determine the confidence of each road line in the multiple road lines, and the target confidence relationship is the corresponding relationship between the confidence of the target road line and the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line and the speed standard value corresponding to the target road line.

[0011] In some embodiments of the present application, a road line is randomly selected from the trajectory matching road network data as a target road line, and target matching data of the target road line and at least one target trajectory matching the target road line is obtained, including:

[0012] Randomly select a road line as a target road line from the trajectory matching road network data, and obtain initial matching data of the target road line and at least one target trajectory matching the target road line;

[0013] 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.

[0014] In some embodiments of the present application, the number of reference trajectory points is the sum of the numbers of matching trajectory points corresponding to the multiple road lines;

[0015] or,

[0016] The number of reference trajectory points is the average number of matching trajectory points corresponding to multiple road lines.

[0017] In some embodiments of the present application, the number of reference trajectory points is an average of the number of matching trajectory points corresponding to the plurality of road lines, and the trajectory point matching ratio corresponding to the target road line is determined based on the ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points, including:

[0018] Determine the ratio of the number of matching trajectory points corresponding to the plurality of road lines to the number of reference trajectory points as the matching trajectory point ratio corresponding to the plurality of road lines;

[0019] Based on the matching trajectory point ratios corresponding to the plurality of road lines, the matching trajectory point ratio corresponding to the target road line is subjected to trajectory point normalization processing to obtain the trajectory point matching ratio corresponding to the target road line.

[0020] In some embodiments of the present application, the normalization formula corresponding to the normalization processing of the trajectory points is:

[0021]

[0022] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, min(t) is the minimum value among the ratios of matching trajectory points corresponding to multiple road lines, and max(t) is the maximum value among the ratios of matching trajectory points corresponding to multiple road lines;

[0023] Alternatively, the normalization formula corresponding to the normalization of trajectory points is:

[0024]

[0025] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, μ1 is the mean of the ratios of matching trajectory points corresponding to multiple road lines, and σ1 is the standard deviation of the ratios of matching trajectory points corresponding to multiple road lines.

[0026] In some embodiments of the present application, the road line confidence formula corresponding to the target confidence relationship is:

[0027] o j =h j ·q j ·z j

[0028] Among them, j is the confidence of the jth road line, s j is the trajectory point matching ratio corresponding to the jth road line, q j is the trajectory length matching ratio corresponding to the jth road line, z j is the speed standard value corresponding to the j-th road line.

[0029] In some embodiments of the present application, the normalization formula corresponding to the speed normalization process is:

[0030]

[0031] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, min(v) is the minimum value among the average speeds corresponding to multiple road lines, and max(v) is the maximum value among the average speeds corresponding to multiple road lines;

[0032] Alternatively, the normalization formula corresponding to the speed normalization process is:

[0033]

[0034] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, μ2 is the mean of the average speeds corresponding to multiple road lines, and σ2 is the standard deviation of the average speeds corresponding to multiple road lines.

[0035] In some embodiments of the present application, the confidence of the target road line is determined based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line, and the target confidence relationship. After determining the confidence of each road line in the multiple road lines, the method further includes:

[0036] The confidence of the target road line and the average of the confidences of at least two road lines adjacent to the target road line are determined as the final confidence of the target road line to determine the final confidence of each road line in the multiple road lines.

[0037] In a second aspect, an embodiment of the present application provides a road line confidence determination device, comprising: an acquisition module, used to randomly select a road line as a target road line from trajectory matching road network data, and acquire target matching data of the target road line and at least one target trajectory matching the target road line; the trajectory matching road network data includes matching data of multiple road lines and at least one trajectory matching each road line respectively, and the target matching data is used to indicate the number of matching trajectory points and the effective matching length corresponding to the target road line; the number of matching trajectory points is the total number of trajectory points matching a road line among all trajectory points of at least one trajectory matching a road line, and the effective matching length is the maximum value of the lengths of the parts of a road line matching each trajectory respectively;

[0038] A determination module, configured to determine a matching ratio of trajectory points corresponding to the target road line based on a ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points;

[0039] Determine a trajectory length matching ratio corresponding to the target road line based on a ratio of an effective matching length corresponding to the target road line to a total length of the target road line;

[0040] Based on the average speed corresponding to each road line, the average speed corresponding to the target road line is normalized to obtain the speed standard value corresponding to the target road line; the average speed is the average of the speed limit values ​​of each sampling point on a road line;

[0041] Based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship, the confidence of the target road line is determined to determine the confidence of each road line in the multiple road lines, and the target confidence relationship is the corresponding relationship between the confidence of the target road line and the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line and the speed standard value corresponding to the target road line.

[0042] In some embodiments of the present application, the acquisition module is specifically used to randomly select a road line as a target road line from the trajectory matching road network data, and obtain initial matching data of the target road line and at least one target trajectory matching the target road line;

[0043] 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.

[0044] In some embodiments of the present application, the number of reference trajectory points is the sum of the numbers of matching trajectory points corresponding to the multiple road lines;

[0045] or,

[0046] The number of reference trajectory points is the average number of matching trajectory points corresponding to multiple road lines.

[0047] In some embodiments of the present application, the number of reference trajectory points is an average of the number of matching trajectory points corresponding to the plurality of road lines, and the determination module is specifically used to determine the ratio of the number of matching trajectory points corresponding to the plurality of road lines to the number of reference trajectory points as the ratio of matching trajectory points corresponding to the plurality of road lines;

[0048] Based on the matching trajectory point ratios corresponding to the plurality of road lines, the matching trajectory point ratio corresponding to the target road line is subjected to trajectory point normalization processing to obtain the trajectory point matching ratio corresponding to the target road line.

[0049] In some embodiments of the present application, the normalization formula corresponding to the normalization processing of the trajectory points is:

[0050]

[0051] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, min(t) is the minimum value among the ratios of matching trajectory points corresponding to multiple road lines, and max(t) is the maximum value among the ratios of matching trajectory points corresponding to multiple road lines;

[0052] Alternatively, the normalization formula corresponding to the normalization of trajectory points is:

[0053]

[0054] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, μ1 is the mean of the ratios of matching trajectory points corresponding to multiple road lines, and σ1 is the standard deviation of the ratios of matching trajectory points corresponding to multiple road lines.

[0055] In some embodiments of the present application, the road line confidence formula corresponding to the target confidence relationship is:

[0056] o j =h j ·q j ·z j

[0057] Among them, j is the confidence of the jth road line, s j is the trajectory point matching ratio corresponding to the jth road line, q j is the trajectory length matching ratio corresponding to the jth road line, z j is the speed standard value corresponding to the j-th road line.

[0058] In some embodiments of the present application, the normalization formula corresponding to the speed normalization process is:

[0059]

[0060] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, min(v) is the minimum value among the average speeds corresponding to multiple road lines, and max(v) is the maximum value among the average speeds corresponding to multiple road lines;

[0061] Alternatively, the normalization formula corresponding to the speed normalization process is:

[0062]

[0063] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, μ2 is the mean of the average speeds corresponding to multiple road lines, and σ2 is the standard deviation of the average speeds corresponding to multiple road lines.

[0064] In some embodiments of the present application, the determination module is also used to determine the confidence of the target road line based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship, so as to determine the confidence of each road line in the multiple road lines, and then determine the confidence of the target road line and the average of the confidences of at least two road lines adjacent to the target road line as the final confidence of the target road line, so as to determine the final confidence of each road line in the multiple road lines.

[0065] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, wherein the processor is used to execute a computer program stored in a memory, and when the computer program is executed by the processor, the steps of any one of the road line confidence determination methods provided in the first aspect are implemented.

[0066] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any one of the road line confidence determination methods provided in the first aspect are implemented.

[0067] In a fifth aspect, an embodiment of the present application provides a vehicle, comprising: a road line 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.

[0068] 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 road line confidence determination method as described in the first aspect.

[0069] 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 is coupled to the processor, the memory is used to store programs or instructions that can be run on the processor, and the processor is used to execute the program or instructions to implement the steps of the road line confidence determination method as described in the first aspect.

[0070] The technical solution provided by the embodiment of the present application has the following advantages over the prior art: in the embodiment of the present application, target matching data corresponding to a target road line and at least one target trajectory matching the target road line are obtained from trajectory matching road network data; a trajectory point matching ratio corresponding to the target road line is determined (used to indicate the proportion of trajectory points in at least one trajectory matched by the road line); a trajectory length matching ratio corresponding to the target road line is determined (used to indicate the proportion of the maximum length effectively covered by each trajectory matched to the road line); a speed standard value corresponding to the target road line is determined (used to indicate the size of the corresponding speed limit value on the road line); based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship, the confidence of the target road line is determined to determine the confidence of each road line among the multiple road lines. Because under normal circumstances, if the proportion of trajectory points in at least one trajectory covered by the road line is large, the proportion of the maximum length of each trajectory matched by the road line is large, and the corresponding speed limit value on the road line is large, the probability that the corresponding speed is uniform or relatively high is relatively large, so the speed limit value of the corresponding sampling point on the road line is closer to the actual speed limit value of the road, which means that the confidence of this road line is high. Using this road line for driving strategy guidance can provide users with accurate driving suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] 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.

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0073] Figure 1 A schematic diagram of a road line and lane group of a road section provided in this application;

[0074] Figure 2 A schematic diagram of a process flow of a road line confidence determination method provided in this application;

[0075] Figure 3 A schematic diagram of a flow chart of another method for determining the confidence level of a road line provided in this application;

[0076] Figure 4 A schematic diagram of a flow chart of another method for determining road line confidence provided by the present application;

[0077] Figure 5 A schematic diagram of a flow chart of another method for determining road line confidence provided in the present application;

[0078] Figure 6 A schematic diagram of the structure of a road line confidence determination device provided in this application;

[0079] Figure 7 A schematic diagram of the hardware structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0080] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0081] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, rather than all of the embodiments.

[0082] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0083] First, some nouns or terms involved in the claims and description of the present invention are explained below.

[0084] Tile: A map tile is a map slice file that contains a series of scales and a certain map range. Map tiles are organized in a pyramid structure, and each tile can be uniquely marked by level, row and column number.

[0085] Map match: Binding a trajectory to a road network is a way of calculating which roads a trajectory passes through in the road network and determining the matching relationship between roads and trajectories.

[0086] 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.

[0087] Electronic maps are mainly divided into high-precision maps and standard maps.

[0088] Standard maps mainly provide positioning, retrieval, and path planning functions in non-assisted driving or non-autonomous driving scenarios, and standard maps can achieve full road network coverage. Among them, standard maps are maps composed of topological network elements based on road lines (links), with an accuracy of meters, and only depict the location and shape of roads. Standard maps have relatively low accuracy and can only reach the road level, not the lane level.

[0089] The standard map is represented by 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 (a) in the figure shows a schematic diagram of a road in a standard map. A road can be composed of a road line A and a road line B, that is, a road can be divided into multiple sections, each section corresponding to a road line. A road line is composed of sampling points and is the digitized representation of an actual road. Among them, sampling points are points necessary to describe a road line. There are at least 2 sampling points on a road line. When a road line represents a relatively straight road or a relatively straight road section, there can be only 2 sampling points on this road line.

[0090] In this embodiment, the road line can be a line segment with a direction, that is, a vector. The road line data is the data of each sampling point on this road. The road line in the standard map and its corresponding road line data are obtained, that is, the data of each sampling point corresponding to each road line is obtained from the standard road network data corresponding to the standard map.

[0091] A high-precision map is a reconstruction of the road environment. It contains a large amount of information about lane positions, types, widths, and road elements such as traffic lights and traffic signs, forming an accurate three-dimensional representation of the road network. High-precision maps are mainly used for assisted driving or autonomous driving, and they are only for specific areas, not all road networks. Since high-precision maps are based on lane-level topological network elements, compared with standard maps, high-precision maps are more accurate and can reach the lane level. The data dimensions of high-precision maps are also richer, adding detailed information such as lane line type, lane width, and roadside landmarks.

[0092] HD maps are represented by HD road network data. Roads in HD maps are represented by lane groups, and each road can consist of one or more lane groups. Figure 1(b) in the figure is a schematic diagram of a road in a high-precision 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 corresponds to a lane group. Each lane group includes multiple actual lane lines and at least one virtual lane line. The actual lane line is the lane line actually marked on the road; the virtual lane line is the virtual line between two actual lane lines. The virtual lane line is generally the center line between two actual lane lines. The virtual lane line is generally not marked on the road, but it is necessary to use the virtual lane line in the process of assisted driving or automatic driving. For example, the vehicle needs to drive along the virtual lane line during automatic driving, so the virtual lane line is included in the lane group of the high-precision map.

[0093] Both the actual lane line and the virtual lane line are composed of some densely distributed points that describe the shape of the corresponding lane line. The points on the lane line are generally called shape points. In the high-precision map, adjacent lane groups are separated by lane group connecting lines. A lane group is between two adjacent lane group connecting lines. Each actual lane line and each virtual lane line in the lane group is a directional vector, and the actual lane line and the virtual lane line in the same lane have the same direction, which is the same as the direction in which vehicles are allowed to travel in the lane. Lane line data is the data of each shape point in the lane. The lane group in the high-precision map and its corresponding lane group data are obtained, that is, the data of the actual lane line shape points and the data of 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 there are three lanes on a highway, each lane group in the corresponding high-precision map includes 4 actual lane lines and 3 virtual lane lines, wherein the 4 actual lane lines are used to distinguish the three lanes, and a virtual lane line is also included between the two actual lane lines of 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 shown in (b) of FIG. In the same road, the road line and the lane group may not be completely matched. 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 (a part of the lane group corresponds to one of the road lines, and the other part of the lane group corresponds to the other road line).

[0094] The present application is applied to the scenario of determining the confidence of a road line. The electronic device in the embodiment of the present application may be a vehicle-mounted terminal, or a mobile phone, notebook, computer, etc. that is communicatively connected to the vehicle. The specific details may be determined based on actual conditions and are not limited here.

[0095] The technical solution of the present application is explained in detail below through several specific embodiments.

[0096] Figure 2 A schematic diagram of a road line confidence determination method provided in this application, such as Figure 2 As shown, the road line confidence determination method may include the following steps 101 to 105.

[0097] 101. Randomly select a road line as a target road line from trajectory matching road network data, and obtain target matching data of the target road line and at least one target trajectory matching the target road line; the trajectory matching road network data includes matching data of multiple road lines and at least one trajectory matching each road line respectively, and the target matching data is used to indicate the number of matching trajectory points and the effective matching length corresponding to the target road line; the number of matching trajectory points is the total number of trajectory points matching one road line among all trajectory points of at least one trajectory matching one road line, and the effective matching length is the maximum value of the lengths of the parts of one road line matching each trajectory respectively.

[0098] The track matching road network data is used to indicate the matching relationship data between the multiple tracks of the target area and the multiple road lines in the target area. The multiple tracks of the target area can be all tracks of the target area or part of the tracks of the target area, which is not limited here. The multiple road lines of the target area can be all road lines in the target area.

[0099] The target area may be a tile in a map (such as a standard map), or an area where a province is located in the map, or an area where a highway is located. The specific area may be determined based on actual conditions and is not limited here.

[0100] The trajectory is the historical driving trajectory of the user's vehicle, and a trajectory consists of multiple trajectory points. The trajectory is represented by trajectory data, and the trajectory data corresponding to a trajectory includes the data of each trajectory point in the trajectory (including the speed limit value of the trajectory point) and the length data of the trajectory.

[0101] The at least one target track matched with the target road line is a track or multiple tracks in the target area, wherein the indicated road section overlaps with the road section indicated by the target road line.

[0102] For example, based on the longitude and latitude information in the data corresponding to the target road line and the longitude and latitude information in the data corresponding to each trajectory, it can be determined whether the section indicated by the target road line overlaps with the section indicated by a trajectory, and then the trajectory with the overlapping indicated section is determined as the target trajectory that matches the target road line.

[0103] Exemplarily, it is also possible to determine whether the section indicated by the target road line overlaps with the section indicated by a trajectory based on the section identifier in the data corresponding to the target road line and the section identifier in the data corresponding to each trajectory, and then determine the trajectory with the overlapping indicated section as the target trajectory that matches the target road line.

[0104] 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.

[0105] 201. Obtain road network data corresponding to a plurality of road lines within a target area from the road network data corresponding to a standard map.

[0106] 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 the existing relevant technology and is not limited here.

[0107] 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 according to the spatial boundary of the target area, screen 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 the road network data corresponding to multiple road lines.

[0108] 202. Obtain trajectory data corresponding to a plurality of trajectories in a target area from a vehicle driving trajectory database, wherein the vehicle driving trajectory database includes vehicle driving trajectory data in the target area and other areas.

[0109] Among them, the specific method of obtaining the trajectory data corresponding to multiple trajectories in the target area from the vehicle driving trajectory database can refer to the existing related technology, which is not limited here.

[0110] 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 the trajectory data corresponding to multiple trajectories.

[0111] 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 road lines and at least one trajectory respectively matched with each road line.

[0112] Among them, the trajectory matching road network processing can be implemented based on the trajectory binding road network (map match) technology, and can also be implemented by other methods. For details, please refer to the existing related technologies and will not be described in detail here.

[0113] The number of trajectories matched by different road lines may be the same or different. That is, any road line may match one trajectory or multiple trajectories, which is not limited here. A road line is the smallest unit of a road. A road line may match multiple trajectories, and different road lines may also match the same trajectory, that is, two or more road lines may match the same trajectory (for example, at least one trajectory matched with road line 1 includes trajectory 1, and at least one trajectory matched with road line 2 also includes trajectory 1).

[0114] Any track point on a target track may only match the target road line, may only match any road line except the target road line, or may not match any road line.

[0115] Among them, the number of matching trajectory points is the total number of trajectory points matching one road line among all trajectory points of at least one trajectory matching one road line, and the number of matching trajectory points corresponding to the target road line is the total number of trajectory points matching the target road line among all trajectory points of at least one target trajectory matching the target road line; the number of matching trajectory points corresponding to the first road line is the total number of trajectory points matching the first road line among all trajectory points of at least one first trajectory matching the first road line; the number of matching trajectory points corresponding to the second road line is the total number of trajectory points matching the second road line among all trajectory points of at least one second trajectory matching the second road line, and so on, which will not be repeated here.

[0116] Among them, the effective matching length is the maximum value among the lengths of the parts of a road line that match each trajectory respectively, then the effective matching length corresponding to the target road line is the maximum value among the lengths of the parts of the target road line that match each target trajectory respectively; the effective matching length corresponding to the first road line is the maximum value among the lengths of the parts of the first road line that match each first trajectory (one of the at least one first trajectory that matches the first road line); the effective matching length corresponding to the second road line is the maximum value among the lengths of the parts of the second road line that match each second trajectory (one of the at least one second trajectory that matches the second road line), and so on, which will not be repeated here.

[0117] The target matching data is used to indicate the number of matching trajectory points and the effective matching length corresponding to the target road line. It can be understood that the target matching data includes the number of matching trajectory points and the effective matching length corresponding to the target road line; it can also be understood that the number of matching trajectory points and the effective matching length corresponding to the 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.

[0118] If the number of matching trajectory points and the effective matching length corresponding to the target road line can be calculated based on the target matching data, then after the above step 101, it is necessary to determine the total number of trajectory points that match the target road line respectively among all trajectory points of at least one target trajectory to obtain the number of matching trajectory points corresponding to the target road line; it is also necessary to determine the maximum value of the length of the part of the target road line that matches at least one target trajectory respectively to obtain the effective matching length corresponding to the target road line.

[0119] Among them, the effective matching length corresponding to the target road line is the maximum value of the lengths of the parts of the target road line that match each target trajectory respectively. If at least one target trajectory is three target trajectories, namely target trajectory 1, target trajectory 2, and target trajectory 3, the length of the part of the target road line that matches target trajectory 1 is length A, the length of the part of the target road line that matches target trajectory 2 is length B, and the length of the part of the target road line that matches target trajectory 3 is length C, and length A is greater than length B and length A is less than length C, then the effective matching length corresponding to the target road line is length C.

[0120] For example, Figure 3 As shown, road line A matches track 1, track 2, and track 3. The number of track points on track 1 that match road line A is 6 (i.e., the number of track points on track 1 that fall within road segment 2), the number of track points on track 2 that match road line A is 3 (i.e., the number of track points on track 2 that fall within road segment 2), and the number of track points on track 3 that match road line A is 3 (i.e., the number of track points on track 3 that fall within road segment 2). The number of matching track points corresponding to road line A is 6+3+3=12. The overlapping part of the road segment indicated by trajectory 1 and the road segment indicated by road line A is road segment 2, and the length 1 of road segment 2 is the length of the part of road line A that matches trajectory 1; the overlapping part of the road segment indicated by trajectory 2 and the road segment indicated by road line A is the part of the mid-length of road segment 2 that is length 2, and the length 2 is the length of the part of road line A that matches trajectory 1; the overlapping part of the road segment indicated by trajectory 3 and the road segment indicated by road line A is the part of the mid-length of road segment 2 that is length 3, and the length 3 is the length of the part of road line A that matches trajectory 1, wherein length 1 is the maximum value of length 1, length 2 and length 3, so the effective matching length corresponding to road line A is length 1.

[0121] 102. Determine a trajectory point matching ratio corresponding to the target road line based on a ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points.

[0122] Among them, the trajectory point matching ratio corresponding to the target road line can be the ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points, or it can be the value after normalizing the ratio of the number of matching trajectory points 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 matching trajectory points corresponding to the target road line to the number of reference trajectory points and a predetermined coefficient. The specific ratio can be determined according to actual conditions and is not limited here.

[0123] The predetermined coefficient may be a number greater than 0 and less than 1.

[0124] In some embodiments of the present application, the number of reference trajectory points may be the sum of the numbers of matching trajectory points corresponding to multiple road lines; or, the number of reference trajectory points may be the average of the numbers of matching trajectory points corresponding to multiple road lines; the number of reference trajectory points may also be other values, which can be determined based on actual conditions and are not limited here.

[0125] The trajectory point matching ratio is used to indicate the proportion of trajectory points in at least one trajectory covered and matched by the road line. The larger the trajectory point matching ratio is, the larger the proportion of trajectory points in at least one trajectory covered and matched by the road line is.

[0126] For example, Figure 3 As shown, assuming that the number of reference trajectory points is the number of reference trajectory points, the number of reference trajectory points can also be the average number of matching trajectory points corresponding to multiple road lines, the trajectory point matching ratio is the ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points, the number of reference trajectory points is 15, and the trajectory point matching ratio corresponding to road line A is 12 / 15=4 / 5.

[0127] 103. Determine a trajectory length matching ratio corresponding to the target road line based on a ratio of an effective matching length corresponding to the target road line to a total length of the target road line.

[0128] Among them, the trajectory length matching ratio corresponding to the target road line can be the ratio of the effective matching length corresponding to the target road line to the total length of the target road line; it can also be the value after normalizing the ratio of the effective matching length corresponding to the target road line to the total length of the target road line, or it can be the product of the ratio of the effective matching length corresponding to the target road line to the total length of the target road line and a preset coefficient. The specific value can be determined according to actual conditions and is not limited here.

[0129] The preset coefficient may be a number greater than 0 and less than 1.

[0130] Among them, the effective lengths of the target road line matching each target trajectory are different. The trajectory length matching ratio corresponding to the target road line is used to indicate the proportion of the maximum length in the effective length of the target road line matching each target trajectory. The larger the trajectory length matching ratio, the larger the proportion of the maximum length in the effective length of the target road line matching each target trajectory.

[0131] Among them, the formula corresponding to the trajectory length matching ratio can be:

[0132]

[0133] Among them, q j is the trajectory length matching ratio corresponding to the jth road line, L j is the maximum value of the effective lengths of the j-th road line and each trajectory matching it, and L0 is the length of the j-th road line.

[0134] 104. Based on the average speed corresponding to each road line, the average speed corresponding to the target road line is normalized to obtain a speed standard value corresponding to the target road line; the average speed is the average of the speed limit values ​​of each sampling point in a road line.

[0135] Among them, the average speed of the target road line is the average of the speed limit values ​​of each sampling point in the target road line; the average speed of the first road line is the average of the speed limit values ​​of each sampling point in the first road line; the average speed of the second road line is the average of the speed limit values ​​of each sampling point in the second road line; and so on.

[0136] Exemplarily, assuming that there are 10 road lines (including the target road line) in the target area, the speed standard value corresponding to the target road line is obtained by normalizing the average speed corresponding to the target road line based on the average speeds corresponding to the 10 road lines.

[0137] In some embodiments of the present application, the normalization formula corresponding to the speed normalization process is:

[0138]

[0139] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, min(v) is the minimum value among the average speeds corresponding to multiple road lines, and max(v) is the maximum value among the average speeds corresponding to multiple road lines;

[0140] Alternatively, the normalization formula corresponding to the speed normalization process is:

[0141]

[0142] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, μ2 is the mean of the average speeds corresponding to multiple road lines, and σ2 is the standard deviation of the average speeds corresponding to multiple road lines.

[0143] In the embodiment of the present application, the normalization formula corresponding to the speed normalization process may also be other formulas, which may be specifically determined based on actual conditions and are not limited here.

[0144] Among them, the speed standard value is used to indicate the size of the corresponding speed limit value on the road line. The larger the speed standard value, the larger the corresponding speed limit value on the road line.

[0145] 105. Determine the confidence of the target road line based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line, and the target confidence relationship, so as to determine the confidence of each road line in the multiple road lines.

[0146] The target confidence relationship is the corresponding relationship between the confidence of the target road line, the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, and the speed standard value corresponding to the target road line.

[0147] The target confidence relationship may be a road line confidence function relationship, which may be specifically determined based on actual conditions and is not limited here.

[0148] In an embodiment of the present application, target matching data corresponding to a target road line and at least one target trajectory matching the target road line are obtained from trajectory matching road network data; a trajectory point matching ratio corresponding to the target road line is determined (used to indicate the proportion of trajectory points in at least one trajectory matched by the road line); a trajectory length matching ratio corresponding to the target road line is determined (used to indicate the proportion of the maximum length effectively covered by each trajectory matched to the road line); a speed standard value corresponding to the target road line is determined (used to indicate the size of the corresponding speed limit value on the road line); based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship, the confidence of the target road line is determined to determine the confidence of each road line among the multiple road lines. Because under normal circumstances, if the proportion of trajectory points in at least one trajectory covered by the road line is large, the proportion of the maximum length of each trajectory matched by the road line is large, and the corresponding speed limit value on the road line is large, the probability that the corresponding speed is uniform or relatively high is relatively large, so the speed limit value of the corresponding sampling point on the road line is closer to the actual speed limit value of the road, which means that the confidence of this road line is high. Using this road line for driving strategy guidance can provide users with accurate driving suggestions.

[0149] In some embodiments of the present application, the above step 101 can be specifically implemented through the following steps 101a and 101b.

[0150] 101a. Randomly select a road line as a target road line from the trajectory matching road network data, and obtain initial matching data of the target road line and at least one target trajectory matching the target road line.

[0151] 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.

[0152] 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.

[0153] Trajectory points with larger error values ​​will affect the judgment of the road line confidence. Therefore, trajectory points whose error values ​​corresponding to trajectory points are greater than or equal to the error threshold can be filtered out, thereby improving the accuracy of the road line confidence.

[0154] In some embodiments of the present application, the number of reference trajectory points is the average number of matching trajectory points corresponding to multiple road lines; Figure 2 ,like Figure 4 As shown, the above step 102 can be specifically implemented through the following steps 102a and 102b.

[0155] 102a. Determine the ratio of the number of matching trajectory points corresponding to the plurality of road lines to the number of reference trajectory points as the matching trajectory point ratio corresponding to the plurality of road lines.

[0156] The matching trajectory point ratio is the ratio of the total number of matching trajectory points between the target road line and at least one target trajectory to the average number of matching trajectory points corresponding to multiple road lines.

[0157] 102b. Based on the matching trajectory point ratios corresponding to the plurality of road lines, respectively, a trajectory point normalization process is performed on the matching trajectory point ratio corresponding to the target road line to obtain a trajectory point matching ratio corresponding to the target road line.

[0158] In the embodiment of the present application, the number of reference trajectory points is the average number of matching trajectory points corresponding to multiple road lines, and after the trajectory point matching ratio corresponding to the target road line is normalized, the value is easy to quantify and the data fluctuation is small, thereby improving processing efficiency.

[0159] In some embodiments of the present application, the normalization formula corresponding to the normalization processing of the trajectory points is:

[0160]

[0161] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, min(t) is the minimum value among the ratios of matching trajectory points corresponding to multiple road lines, and max(t) is the maximum value among the ratios of matching trajectory points corresponding to multiple road lines;

[0162] Alternatively, the normalization formula corresponding to the normalization of trajectory points is:

[0163]

[0164] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, μ1 is the mean of the ratios of matching trajectory points corresponding to multiple road lines, and σ1 is the standard deviation of the ratios of matching trajectory points corresponding to multiple road lines.

[0165] In the embodiment of the present application, the normalization formula corresponding to the normalization processing of the trajectory points may also be other formulas, which can be determined according to actual conditions and are not limited here.

[0166] In some embodiments of the present application, the road line confidence formula corresponding to the target confidence relationship is:

[0167] o j =h j ·q j ·z j

[0168] Among them, j is the confidence of the jth road line, s j is the trajectory point matching ratio corresponding to the jth road line, q j is the trajectory length matching ratio corresponding to the jth road line, z j is the speed standard value corresponding to the j-th road line.

[0169] In the embodiment of the present application, the road line confidence formula corresponding to the target confidence relationship is respectively proportional to the trajectory point matching ratio corresponding to the road line, the trajectory length matching ratio corresponding to the road line and the speed standard value corresponding to the road line. In this way, when the proportion of trajectory points in at least one trajectory covered by the road line is large, the proportion of the maximum length effectively covered by each trajectory matched to the road line is large, and the corresponding speed limit value on the road line is large, then the probability that the speed limit value corresponding to the road line is uniform or relatively high is relatively large, so that the speed limit value of the corresponding sampling point on the road line is closer to the actual speed limit value of the road, which further indicates that the confidence of this road line is high.

[0170] In some embodiments of the present application, the road line confidence formula corresponding to the target confidence relationship can also be composed of s j ,q j 、z j Other formulas are not limited here.

[0171] In some embodiments of the present application, Figure 4 ,like Figure 5 As shown, after the above step 105, the trajectory confidence determination method provided in the embodiment of the present application may further include the following step 106.

[0172] 106. Determine an average of the confidence of the target road line and the confidences of at least two road lines adjacent to the target road line as a final confidence of the target road line, so as to determine a final confidence of each road line in the plurality of road lines.

[0173] The average of the confidence of the target road line and the confidence of at least two road lines adjacent to the target road line may be the arithmetic mean or geometric mean of the confidence of the target road line and the confidence of at least two road lines adjacent to the target road line.

[0174] Among them, at least two road lines adjacent to the target road line can be a predecessor road line of the target road line and a successor road line of the target road line, or at least two predecessor road lines of the target road line, or at least two successor road lines of the target road line, or at least one predecessor road line of the target road line and at least one successor road line of the target road line. The specific details can be determined according to actual conditions and are not limited here.

[0175] In the embodiment of the present application, since the confidences corresponding to adjacent road lines are generally close, there is a correlation, and generally there will not be large fluctuations, therefore, the confidence of the target road line and the average of the confidences of at least two road lines adjacent to the target road line are determined as the final confidence of the target road line, thereby achieving smoothing of the confidence of the target road line, thereby eliminating some fluctuations in the confidence of adjacent road lines caused by special roads or special trajectories.

[0176] The confidence of the road line obtained by the method provided in the embodiment of the present application will be stored in the speed limit information of each sampling point in the standard road network data of the standard map, and then the corresponding driving strategy (regulatory control strategy) will be formulated in combination with the confidence of this road line and the speed limit value of the sampling point, so as to provide users with accurate driving suggestions. For example, when the confidence of the road line is very high, the driving strategy will be formulated strictly in accordance with the speed limit value of the road line. If the confidence of the road line is relatively low, some driving reminders may be sent to the user in advance in some complex scenarios (such as rush hour in the morning and evening) (such as the confidence of the current road line is low, whether it is necessary to slow down), and then the user can control the driving speed to be lower than the speed limit value corresponding to the road line.

[0177] In the embodiment of the present application, after the confidence level of each road line is determined, it can also provide a good guide for mapping the speed limit information of the high-precision map through the speed limit information of the standard map to improve the accuracy of the speed limit information of the high-precision map.

[0178] The present application also provides a road line confidence determination device, Figure 6 A schematic diagram of the structure of a road line confidence determination device provided in this application, such as Figure 6As shown, the road line confidence determination device includes: an acquisition module 601, which is used to randomly select a road line as a target road line from the trajectory matching road network data, and obtain the target road line and the target matching data of at least one target trajectory matching the target road line; the trajectory matching road network data includes a plurality of road lines and matching data of at least one trajectory respectively matching each road line, and the target matching data is used to indicate the number of matching trajectory points and the effective matching length corresponding to the target road line; the number of matching trajectory points is the total number of trajectory points matching a road line among all trajectory points of at least one trajectory matching a road line, and the effective matching length is the maximum value of the lengths of the parts of a road line respectively matching each trajectory; a determination module 602 is used to determine the matching trajectory points corresponding to the target road line based on the ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points. ratio; based on the ratio of the effective matching length corresponding to the target road line to the total length of the target road line, determine the trajectory length matching ratio corresponding to the target road line; based on the average speed corresponding to each road line, perform speed normalization on the average speed corresponding to the target road line to obtain the speed standard value corresponding to the target road line; the average speed is the average of the speed limit values ​​of each sampling point in a road line; based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship, determine the confidence of the target road line to determine the confidence of each road line in the multiple road lines, the target confidence relationship is the corresponding relationship between the confidence of the target road line, the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line and the speed standard value corresponding to the target road line.

[0179] In some embodiments of the present application, the acquisition module 601 is specifically used to randomly select a road line as a target road line from the trajectory matching road network data, obtain initial matching data of the target road line and at least one target trajectory matching the target road line; filter out data corresponding to trajectory points in the initial matching data whose error values ​​are greater than or equal to an error threshold, and obtain target matching data.

[0180] In some embodiments of the present application, the number of reference trajectory points is the sum of the numbers of matching trajectory points corresponding to the multiple road lines; or, the number of reference trajectory points is the average of the numbers of matching trajectory points corresponding to the multiple road lines.

[0181] In some embodiments of the present application, the number of reference trajectory points is the average number of matching trajectory points corresponding to multiple road lines respectively, and the determination module 602 is specifically used to determine the ratio of the number of matching trajectory points corresponding to multiple road lines respectively to the number of reference trajectory points as the matching trajectory point ratio corresponding to the multiple road lines respectively; based on the matching trajectory point ratios corresponding to the multiple road lines respectively, the matching trajectory point ratio corresponding to the target road line is normalized to obtain the trajectory point matching ratio corresponding to the target road line.

[0182] In some embodiments of the present application, the normalization formula corresponding to the normalization processing of the trajectory points is:

[0183]

[0184] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, min(t) is the minimum value among the ratios of matching trajectory points corresponding to multiple road lines, and max(t) is the maximum value among the ratios of matching trajectory points corresponding to multiple road lines;

[0185] Alternatively, the normalization formula corresponding to the normalization of trajectory points is:

[0186]

[0187] Among them, s j is the trajectory point matching ratio corresponding to the jth road line, t j is the ratio of matching trajectory points corresponding to the jth road line, μ1 is the mean of the ratios of matching trajectory points corresponding to multiple road lines, and σ1 is the standard deviation of the ratios of matching trajectory points corresponding to multiple road lines.

[0188] In some embodiments of the present application, the road line confidence formula corresponding to the target confidence relationship is:

[0189] o j =h j ·q j ·z j

[0190] Among them, j is the confidence of the jth road line, s j is the trajectory point matching ratio corresponding to the jth road line, q j is the trajectory length matching ratio corresponding to the jth road line, z j is the speed standard value corresponding to the j-th road line.

[0191] In some embodiments of the present application, the normalization formula corresponding to the speed normalization process is:

[0192]

[0193] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, min(v) is the minimum value among the average speeds corresponding to multiple road lines, and max(v) is the maximum value among the average speeds corresponding to multiple road lines;

[0194] Alternatively, the normalization formula corresponding to the speed normalization process is:

[0195]

[0196] Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, μ2 is the mean of the average speeds corresponding to multiple road lines, and σ2 is the standard deviation of the average speeds corresponding to multiple road lines.

[0197] In some embodiments of the present application, the determination module 602 is further used to determine the confidence of the target road line based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line, and the target confidence relationship, so as to determine the confidence of each road line in the multiple road lines, and then determine the confidence of the target road line and the average of the confidences of at least two road lines adjacent to the target road line as the final confidence of the target road line, so as to determine the final confidence of each road line in the multiple road lines.

[0198] It should be noted that the above-mentioned road line 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 function of the device embodiment, and the embodiment of the present application is not limited.

[0199] In the embodiment of the present application, each module can implement the road line confidence determination method provided in the above method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0200] 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 of any road line in the embodiment of the present application, and should not be understood as a specific limitation on the embodiment of the present application.

[0201] like Figure 7As 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. In the RAM 703, various programs and data required for the operation of the electronic device 700 are also stored. 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.

[0202] Typically, the following devices may be connected to the I / O interface 705: input devices 706 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 708 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 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 having various devices is shown, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0203] 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 any road line confidence determination method provided in the embodiment of the present application can be executed.

[0204] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may 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 the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit 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 of the above.

[0205] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may 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.

[0206] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0207] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: randomly selects a road line as a target road line from the trajectory matching road network data, and obtains target matching data of the target road line and at least one target trajectory matching the target road line; the trajectory matching road network data includes a plurality of road lines and matching data of at least one trajectory respectively matching each road line, and the target matching data is used to indicate the number of matching trajectory points and the effective matching length corresponding to the target road line; the number of matching trajectory points is the total number of trajectory points matching a road line among all trajectory points of at least one trajectory matching a road line, and the effective matching length is the maximum value among the lengths of the parts of a road line respectively matching each trajectory; based on the ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points, determine the target road line to the target road line. The method comprises the following steps: determining a trajectory point matching ratio corresponding to the target road line; determining a trajectory length matching ratio corresponding to the target road line based on a ratio of an effective matching length corresponding to the target road line to a total length of the target road line; performing speed normalization processing on the average speed corresponding to the target road line based on the average speed corresponding to each road line, so as to obtain a speed standard value corresponding to the target road line; the average speed is an average of speed limit values ​​of each sampling point in a road line; determining the confidence of the target road line based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and a target confidence relationship, so as to determine the confidence of each road line in the multiple road lines, wherein the target confidence relationship is a corresponding relationship between the confidence of the target road line, the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line and the speed standard value corresponding to the target road line.

[0208] In an embodiment of the present application, a computer program code for performing the operation 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 completely on the computer, partially on the computer, as an independent software package, partially on the computer and partially on the remote computer, or completely 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).

[0209] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the 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 square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0210] 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.

[0211] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, 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 chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0212] In the context of the present application, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0213] An embodiment of the present application also provides a vehicle, comprising: the above-mentioned road line confidence determination device, or the above-mentioned electronic device, or the above-mentioned computer-readable storage medium.

[0214] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0215] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a 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 can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0216] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for determining road line confidence, characterized in that: The method comprises: Randomly select a road line as a target road line from the trajectory matching road network data, and obtain target matching data of the target road line and at least one target trajectory matching the target road line; the trajectory matching road network data includes matching data of multiple road lines and at least one trajectory matching each road line, and the target matching data includes the number of matching trajectory points and the effective matching length corresponding to the target road line; the number of matching trajectory points is the total number of trajectory points matching the road line among all trajectory points of at least one trajectory matching the road line, and the effective matching length is the maximum value of the lengths of the parts of the road line matching each trajectory respectively; Determining a trajectory point matching ratio corresponding to the target road line based on a ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points; Determining a trajectory length matching ratio corresponding to the target road line based on a ratio of an effective matching length corresponding to the target road line to a total length of the target road line; Based on the average speed corresponding to each road line, the average speed corresponding to the target road line is normalized to obtain the speed standard value corresponding to the target road line; the average speed is the average of the speed limit values ​​of each sampling point in a road line; Based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and a target confidence relationship, the confidence of the target road line is determined to determine the confidence of each road line in the multiple road lines, and the target confidence relationship is the corresponding relationship between the confidence of the target road line, the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line and the speed standard value corresponding to the target road line.

2. The method according to claim 1, characterized in that The step of randomly selecting a road line as a target road line from the trajectory matching road network data, and obtaining target matching data of the target road line and at least one target trajectory matching the target road line comprises: Randomly select a road line as a target road line from the trajectory matching road network data, and obtain initial matching data of the target road line and at least one target trajectory matching the target road line; 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 numbers of matching trajectory points corresponding to the multiple road lines respectively; or, The number of reference trajectory points is an average of the numbers of matching trajectory points corresponding to the multiple road lines.

4. The method according to claim 3, characterized in that The number of reference trajectory points is an average of the numbers of matching trajectory points corresponding to the plurality of road lines respectively, and determining the trajectory point matching ratio corresponding to the target road line based on the ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points includes: Determine the ratio of the number of the matching trajectory points respectively corresponding to the plurality of road lines to the number of the reference trajectory points as the matching trajectory point ratio respectively corresponding to the plurality of road lines; Based on the matching trajectory point ratios respectively corresponding to the plurality of road lines, the trajectory point normalization processing is performed on the matching trajectory point ratio corresponding to the target road line to obtain the trajectory point matching ratio corresponding to the target road line.

5. The method according to claim 4, characterized in that The normalization formula corresponding to the normalization process of the trajectory points is: Among them, s j is the matching ratio of the trajectory points corresponding to the jth road line, t j is the ratio of the matching trajectory points corresponding to the j-th road line, min(t) is the minimum value among the ratios of the matching trajectory points corresponding to the multiple road lines, and max(t) is the maximum value among the ratios of the matching trajectory points corresponding to the multiple road lines; Alternatively, the normalization formula corresponding to the trajectory point normalization process is: Among them, s j is the matching ratio of the trajectory points corresponding to the jth road line, t j is the ratio of the matching trajectory points corresponding to the j-th road line, μ1 is the mean of the ratios of the matching trajectory points corresponding to the multiple road lines, and σ1 is the standard deviation of the ratios of the matching trajectory points corresponding to the multiple road lines.

6. The method according to claim 1, characterized in that The road line confidence formula corresponding to the target confidence relationship is: o j =h j ·q j ·z j Among them, j is the confidence of the jth road line, s j is the matching ratio of the trajectory points corresponding to the jth road line, q j is the trajectory length matching ratio corresponding to the jth road line, z j is the speed standard value corresponding to the j-th road line.

7. The method according to claim 1, characterized in that The normalization formula corresponding to the speed normalization process is: Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the j-th road line, min(v) is the minimum value of the average speeds corresponding to the multiple road lines, and max(v) is the maximum value of the average speeds corresponding to the multiple road lines; Alternatively, the normalization formula corresponding to the speed normalization process is: Among them, z j is the speed standard value corresponding to the jth road line, v j is the average speed corresponding to the jth road line, μ2 is the mean of the average speeds corresponding to multiple road lines, and σ2 is the standard deviation of the average speeds corresponding to the multiple road lines.

8. The method according to any one of claims 1 to 7, characterized in that After determining the confidence of the target road line based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and the target confidence relationship, and determining the confidence of each road line in the plurality of road lines, the method further includes: The confidence of the target road line and the average of the confidences of at least two road lines adjacent to the target road line are determined as the final confidence of the target road line to determine the final confidence of each road line in the multiple road lines.

9. A road line confidence determination device, characterized in that: include: an acquisition module, configured to randomly select a road line as a target road line from trajectory matching road network data, and acquire target matching data of the target road line and at least one target trajectory matching the target road line; the trajectory matching road network data includes matching data of multiple road lines and at least one trajectory matching each road line respectively, and the target matching data includes the number of matching trajectory points and an effective matching length corresponding to the target road line; the number of matching trajectory points is the total number of trajectory points matching the road line among all trajectory points of at least one trajectory matching the road line, and the effective matching length is the maximum value among the lengths of the parts of the road line matching each trajectory respectively; a determination module, configured to determine a trajectory point matching ratio corresponding to the target road line based on a ratio of the number of matching trajectory points corresponding to the target road line to the number of reference trajectory points; Determining a trajectory length matching ratio corresponding to the target road line based on a ratio of an effective matching length corresponding to the target road line to a total length of the target road line; Based on the average speed corresponding to each road line, the average speed corresponding to the target road line is normalized to obtain the speed standard value corresponding to the target road line; the average speed is the average of the speed limit values ​​of each sampling point in a road line; Based on the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line, the speed standard value corresponding to the target road line and a target confidence relationship, the confidence of the target road line is determined to determine the confidence of each road line in the multiple road lines, and the target confidence relationship is the corresponding relationship between the confidence of the target road line, the trajectory point matching ratio corresponding to the target road line, the trajectory length matching ratio corresponding to the target road line and the speed standard value corresponding to the target road line.

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

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

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