Road type identification method and device, electronic equipment, storage medium and vehicle

Abstract

The invention relates to a road type identification method and device, electronic equipment, a storage medium and a vehicle, and the method comprises the steps: carrying out the clustering of road point clouds in a BEV road perception image according to the distance between points, obtaining N point clusters, N being a positive integer, and each point cluster in the N point clusters being used for indicating different road segments; one of the N point clusters serves as a target point cluster, singular value decomposition is conducted on the target point cluster, a first singular value and a second singular value are obtained, the first singular value is used for indicating the variance of point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used for indicating the variance of point cloud distribution of the target point cluster in the main direction of the target point cluster; the second singular value is used for indicating a variance of point cloud distribution of the target point cluster in a direction perpendicular to a main direction, and the main direction of the target point cluster is a driving direction of a target road section indicated by the target point cluster; and determining the road type of the target road section indicated by the target point cluster based on the difference between the first singular value and the second singular value. The method can improve the accuracy of road type identification.

Description

Technical Field

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

[0002] In the field of autonomous driving, autonomous vehicles plan their driving paths based on high-precision maps. Therefore, high-precision maps are an important technology for achieving safe autonomous driving. In order to improve the accuracy of map production, it is necessary to accurately identify the road type when obtaining road data for map production.

[0003] At present, the method for identifying the road type is: for each road line indicated by the road data, first calculate the curvature of every three points in the road line, then accumulate and calculate the average curvature of the road line, and determine the road type of the section indicated by the road data based on the average curvature of the road line. However, the accuracy of the current method for identifying the road type is low, and there may be misidentification, such as identifying the intersection section as a large curvature curve, etc. Therefore, how to accurately identify the road type of the section indicated by the road data has become an urgent problem to be solved. Summary of the invention

[0004] The present application provides a road type identification method, device, electronic device, storage medium and vehicle, which can improve the efficiency of road type identification while reducing computing power consumption.

[0005] In a first aspect, an embodiment of the present application provides a road type identification method, the method comprising: clustering a road point cloud in a BEV road perception image according to the spacing between points to obtain N point clusters, where N is a positive integer, and each of the N point clusters is used to indicate a different road segment; taking one of the N point clusters as a target point cluster, performing singular value decomposition on the target point cluster to obtain a first singular value and a second singular value, the first singular value being used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value being used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction, the main direction of the target point cluster being the driving direction of the target road segment indicated by the target point cluster; determining the road type of the target road segment indicated by the target point cluster based on the difference between the first singular value and the second singular value.

[0006] In a second aspect, an embodiment of the present application provides a road type identification device, including: a clustering unit, used to cluster the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters, N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment; a singular value decomposition unit, used to take one point cluster among the N point clusters as a target point cluster, perform singular value decomposition on the target point cluster, and obtain a first singular value and a second singular value, the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction, and the main direction of the target point cluster is the driving direction of the target road segment indicated by the target point cluster; a determination unit, used to determine the road type of the target road segment indicated by the target point cluster based on the difference between the first singular value and the second singular value.

[0007] 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 type identification methods provided in the first aspect are implemented.

[0008] 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 type identification methods provided in the first aspect are implemented.

[0009] In a fifth aspect, an embodiment of the present application provides a vehicle, comprising: a road type identification 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.

[0010] 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 type identification method as described in the first aspect.

[0011] 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 type identification method as described in the first aspect.

[0012] 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, the road point cloud in the BEV road perception image is clustered according to the point spacing to obtain N point clusters, where N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment; one point cluster among the N point clusters is used as a target point cluster, and the target point cluster is subjected to singular value decomposition to obtain a first singular value and a second singular value, the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction, and the main direction of the target point cluster is the driving direction of the target road segment indicated by the target point cluster; based on the difference between the first singular value and the second singular value, the road type of the target road segment indicated by the target point cluster is determined. In this solution, the road point cloud in the BEV road perception image is clustered according to the distance between the points to obtain N point clusters; one of the N point clusters is used as the target point cluster, and the target point cluster is subjected to singular value decomposition to obtain the first singular value and the second singular value; based on the difference between the first singular value and the second singular value, the road type of the target road segment indicated by the target point cluster is determined. In this way, different road segments in the road data can be distinguished by clustering, and the first singular value and the second singular value are obtained by the singular value decomposition algorithm. Based on the difference between the first singular value and the second singular value, the road type is determined. Compared with the prior art, the road type of the road segment indicated by the road data can be accurately identified, and the recognition accuracy of the road type of the road segment indicated by the road data can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] 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.

[0014] 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.

[0015] Figure 1 A schematic diagram of a road type identification method provided in this application;

[0016] Figure 2 A schematic diagram of a clustering method provided in this application;

[0017] Figure 3 A schematic diagram of another clustering method provided for this application;

[0018] Figure 4 A schematic diagram of a flow chart of a method for determining a road type provided in this application;

[0019] Figure 5 A schematic diagram of the structure of a road type identification device provided in this application;

[0020] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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.

[0024] First, some nouns or terms involved in the claims and description of the present invention are explained below.

[0025] BEV (Bird's Eye View) perception: is a perception technology that observes scenes from a bird's eye view, which can provide more comprehensive and accurate environmental perception information. BEV perception has been widely used and studied in the fields of autonomous driving, intelligent transportation, logistics and distribution, because it can effectively solve the limitations of traditional monocular and binocular vision perception technologies in scene perception range, blind spots, posture changes, etc.

[0026] Clustering: It is the process of grouping data objects based on information found in the data that describes the objects and their relationships. The goal is that objects within a group are similar to each other (related), while objects in different groups are different (unrelated). The greater the similarity within a group and the greater the difference between groups, the better the clustering effect.

[0027] SVD (Singular Value Decomposition) decomposition is to decompose a matrix into three matrices multiplied by each other, where the first matrix is ​​a unit orthogonal matrix, the second matrix is ​​a diagonal matrix, and the third matrix is ​​also a unit orthogonal matrix. Specifically, given an m×n matrix A, SVD decomposition can be expressed as A=U∑V^T, where U is an m×n unit orthogonal matrix, V is an n×n unit orthogonal matrix, ∑ is a diagonal matrix, and the elements on the diagonal are the singular values ​​of A.

[0028] In the field of autonomous driving, autonomous vehicles plan their driving paths based on high-precision maps. Therefore, high-precision maps are an important technology for achieving safe autonomous driving. In order to improve the accuracy of map production, it is necessary to accurately identify the road type when obtaining road data for map production.

[0029] Currently, the method for identifying road types is: for each road line indicated by the road data, first calculate the curvature of every three points in the road line, then accumulate and calculate the average curvature of the road line, and based on the average curvature of the road line, determine the road type of the section indicated by the road data.

[0030] However, when the road type recognition method is used to identify the road type, the process is relatively complicated. For example, for a road line with 20 points, when the road type recognition method is used to identify the road type, a curvature is calculated for every three points, and it needs to be traversed 18 times to obtain the average curvature of the road line. Therefore, the current method of identifying the road type is inefficient and consumes more computing power.

[0031] In response to the above technical problems, an embodiment of the present application provides a road type recognition method, device, electronic device, storage medium and vehicle, the method comprising: clustering the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters, N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment; taking one point cluster among the N point clusters as a target point cluster, performing singular value decomposition on the target point cluster to obtain a first singular value and a second singular value, the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction, and the main direction of the target point cluster is the driving direction of the target road segment indicated by the target point cluster; based on the difference between the first singular value and the second singular value, determining the road type of the target road segment indicated by the target point cluster. In this solution, the road point cloud in the BEV road perception image is clustered according to the distance between the points to obtain N point clusters; one of the N point clusters is used as the target point cluster, and the target point cluster is subjected to singular value decomposition to obtain the first singular value and the second singular value; based on the difference between the first singular value and the second singular value, the road type of the target road segment indicated by the target point cluster is determined. In this way, different road segments in the road data can be distinguished by clustering, and the first singular value and the second singular value are obtained by the singular value decomposition algorithm. Based on the difference between the first singular value and the second singular value, the road type is determined. Compared with the prior art, the road type of the road segment indicated by the road data can be accurately identified, and the recognition accuracy of the road type of the road segment indicated by the road data can be improved.

[0032] The present application is applied to the road type recognition scenario. The electronic device in the embodiment of the present application can be a vehicle-mounted terminal, or a mobile phone, notebook, computer, etc. that is connected to the vehicle for communication. The specific details can be determined based on actual conditions and are not limited here.

[0033] The technical solution of the present application is explained in detail below through several specific embodiments.

[0034] Figure 1 A schematic diagram of a road type identification method provided in this application, such as Figure 1 As shown, the road type recognition method may include the following steps S101 to S104:

[0035] S101. Clustering the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters, where N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment.

[0036] Among them, the road point cloud in the BEV road perception image can be a 2D intersection point cloud, and the 2D intersection point cloud can be split into roads using a clustering algorithm.

[0037] In some embodiments, it is also possible to identify whether the road indicated by the BEV road perception image is an intersection scene and what kind of intersection scene it is. The road type identification method further includes:

[0038] When N is equal to 1, it is determined that the road scene indicated by the road point cloud is a non-intersection scene;

[0039] When N is equal to 2, it is determined that the road scene indicated by the road point cloud is a two-way intersection scene;

[0040] When N is equal to 3, it is determined that the road scene indicated by the road point cloud is a three-way intersection scene;

[0041] When N is equal to 4, it is determined that the road scene indicated by the road point cloud is a four-way intersection scene.

[0042] Among them, non-intersection scenes refer to straight road or curve scenes, two-way intersections include intersections in two directions, such as L-shaped intersections, three-way intersections include intersections in three directions, such as three-way intersections, T-junctions, etc., and four-way intersection scenes include intersections in four directions, such as crossroads, X-shaped intersections, etc.

[0043] In the embodiment of the present application, the road point cloud in the BEV road perception image is clustered according to the point spacing to obtain N point clusters, which are used to indicate different road segments. When N is equal to 1, the road scene indicated by the road point cloud is determined to be a non-intersection scene; when N is greater than 1, the road scene indicated by the road point cloud is determined to be an intersection scene; specifically, when N is equal to 2, the road scene indicated by the road point cloud is determined to be a two-way intersection scene; when N is equal to 3, the road scene indicated by the road point cloud is determined to be a three-way intersection scene; when N is equal to 4, the road scene indicated by the road point cloud is determined to be a four-way intersection scene. In this way, according to the number of point clusters obtained by clustering, the road scene indicated by the road point cloud in the BEV road perception image is directly obtained, so that more road information is provided for downstream map production, and map data that is closer to the real road scene can be obtained.

[0044] The embodiment of the present application does not limit the specific clustering method of "clustering the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters".

[0045] Figure 2 A schematic diagram of a clustering method provided in an embodiment of the present application.

[0046] In some embodiments, Figure 2 As shown, the clustering method may include the following steps S101A to S101B:

[0047] S101A. Determine the clustering distance.

[0048] S101B, based on the clustering distance, cluster the road point cloud in the BEV road perception image according to the distance between points to obtain N point clusters.

[0049] Among them, the above-mentioned cluster distance can refer to the distance between different road segments in the embodiment of the present application. The distance can be the Euclidean distance between different road segments, or other distances, such as cosine distance, Manhattan distance, etc. The specific distance can be determined according to actual conditions and is not limited here.

[0050] In an embodiment of the present application, a clustering distance is determined; based on the clustering distance, the road point cloud in the BEV road perception image is clustered according to the distance between the points to obtain N point clusters. It should be understood that when the road point cloud is clustered according to the distance between the points, the most intuitive difference between the point clouds of each category is the distance. For example, the distance between the point clouds of the same road segment is usually smaller than the distance between the point clouds of different road segments (that is, the distance between two point clouds on a road segment is usually smaller than the distance between the point cloud on the road segment and the point cloud on other road segments); therefore, a parameter that can reflect the distance between different road segments: the clustering distance is used to cluster the road point cloud according to the distance between the points to obtain N point clusters, and the N point clusters can more accurately reflect the multiple road segments indicated by the road point cloud.

[0051] In a possible implementation, the above S101B "based on clustering distance, clustering the road point cloud in the BEV road perception image according to the distance between points to obtain N point clusters" includes the following S101B1 to S101B5.

[0052] S101B1. Select a point P10 from the road point cloud in the BEV road perception image.

[0053] S101B2. Determine the distance from P10 to M points other than P10 in the road point cloud. When the distance is less than or equal to the clustering distance, determine the point and P10 as points in the same point cluster C1. When the distance is greater than the clustering distance, determine the point and P10 as points in different point clusters.

[0054] S101B3. Traverse all unvisited points in C1 and repeat step S101-A2-12.

[0055] S101B4. When all points in C1 are visited, the C1 clustering ends.

[0056] S101B5. Select a point P20 from the road point cloud, where P20 is any point in the road point cloud except C1, and iterate the above steps S101B1 to S101B4 repeatedly until all points in the road point cloud are visited, then terminate all clustering and obtain N point clusters.

[0057] Other embodiments of the present invention are further optimized based on the above embodiments. Figure 3 A schematic diagram of another clustering method provided in an embodiment of the present application. In some embodiments, Figure 3 As shown, the above S101B "based on the clustering distance, clustering the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters" may include the following steps S101B11 to S101B13:

[0058] S101B11. Based on the clustering distance, the road point cloud is clustered according to the distance between points to obtain M point clusters, where M is an integer greater than or equal to N.

[0059] S101B12. Determine the number of point clouds in each of the M point clusters.

[0060] S101B13. Determine the point clusters whose point cloud quantity is greater than or equal to the quantity threshold among the M point clusters as N point clusters.

[0061] In the embodiment of the present application, based on the clustering distance, the road point cloud is clustered according to the distance between the points to obtain M point clusters, where M is an integer greater than or equal to N; the number of point clouds in each of the M point clusters is determined; the point clusters in the M point clusters whose number of point clouds is greater than or equal to the number threshold are determined as N point clusters. In this way, the point cloud number threshold for each point cluster is set, and the point clusters in the M point clusters obtained by clustering whose number of point clouds is greater than or equal to the number threshold are determined as N point clusters. In this way, in the clustering stage of the road point cloud, the road point cloud is pre-screened, thereby avoiding the influence of noise on the clustering results, improving the accuracy of the clustering results, and further improving the accuracy of road type recognition.

[0062] S102. Take one of the N point clusters as the target point cluster, perform singular value decomposition on the target point cluster, and obtain a first singular value and a second singular value, wherein the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction.

[0063] In the embodiment of the present application, the main direction of the point cluster may be the driving direction of the corresponding road segment. The main direction of the target point cluster is the driving direction of the target road segment indicated by the target point cluster.

[0064] Among them, singular value decomposition is also called SVD decomposition. By performing SVD decomposition on the target point cluster, a singular vector V1 and a singular vector V2, as well as a first singular value corresponding to the singular vector V1 and a second singular value corresponding to the singular vector V2 can be obtained. The singular vector V1 is used to indicate the main direction of the target point cluster, and the singular vector V2 is used to indicate the direction of the target point cluster perpendicular to the main direction. The first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the direction perpendicular to the main direction.

[0065] It should be understood that the main direction of the target point cluster is usually the direction in which the car is traveling, and the direction perpendicular to the main direction is usually the road width direction.

[0066] S103: Determine the road type of the target road segment indicated by the target point cluster based on the difference between the first singular value and the second singular value.

[0067] The embodiment of the present application does not limit the specific method of “determining the road type of the target road segment indicated by the target point cluster based on the difference between the first singular value and the second singular value”.

[0068] Figure 4 It is a flowchart of a method for determining a road type provided in an embodiment of the present application.

[0069] In some embodiments, Figure 4 As shown, determining the road type of the target road segment indicated by the target point cluster in the above step S103 may specifically include the following steps S103A1 to S103A4.

[0070] S103A1. Determine a first parameter according to a first singular value and a second singular value, where the first parameter is used to indicate a difference between the first singular value and the second singular value.

[0071] In some embodiments, “determining a first parameter according to the first singular value and the second singular value” in S103A1 includes: determining a ratio of the first singular value to the second singular value as the first parameter.

[0072] In the embodiment of the present application, the ratio of the first singular value to the second singular value is determined as the first parameter, wherein the ratio of the first singular value to the second singular value can clearly reflect the difference between the first singular value and the second singular value. In this way, the process of determining the first parameter is simple and clear, and is also relatively efficient.

[0073] In some embodiments, “determining a first parameter according to the first singular value and the second singular value” in S103A1 includes: determining a difference between the first singular value and the second singular value as the first parameter.

[0074] In the embodiment of the present application, the difference between the first singular value and the second singular value is determined as the first parameter. The difference between the first singular value and the second singular value can clearly reflect the difference between the first singular value and the second singular value. In this way, the process of determining the first parameter is simple and clear, and is also relatively efficient.

[0075] In some embodiments, "determining a first parameter according to the first singular value and the second singular value" in S103A1 includes: comparing the first singular value with the second singular value to obtain a first ratio, and determining the product of the first ratio and a preset value as the first parameter.

[0076] S103A2: When the first parameter is greater than or equal to the first threshold, determine that the road type of the target road segment indicated by the target point cluster is a straight road.

[0077] S103A3: When the first parameter is less than the first threshold value and greater than the second threshold value, determine that the road type of the target road segment indicated by the target point cluster is a small curvature curve.

[0078] S103A4: When the first parameter is less than or equal to the second threshold, determine that the road type of the target road segment indicated by the target point cluster is a large curvature curve.

[0079] The first threshold is greater than the second threshold, and the curvature of the small curvature curve is smaller than the curvature of the large curvature curve.

[0080] It should be understood that, first, before obtaining the BEV road perception image, it is necessary to collect the road information of X meters in front and behind the vehicle, and the road information of Y meters to the left and right of the vehicle through the on-board camera, and then obtain the BEV road perception image based on the road information obtained by the on-board camera. When actually collecting road information, Y does not exceed the road width, while X is much larger than the road width. Therefore, the point cloud distribution in the main direction of the road perception image is relatively discrete, while the point cloud distribution in the direction perpendicular to the main direction is relatively concentrated. Therefore, the variance of the point cloud distribution in the main direction is large, while the variance of the point cloud distribution in the direction perpendicular to the main direction is small. Further, for a straight road, the difference between the variance of the point cloud distribution in the main direction and the variance of the point cloud distribution in the direction perpendicular to the main direction is the largest, for a small curvature curve, the difference between the variance of the point cloud distribution in the main direction and the variance of the point cloud distribution in the direction perpendicular to the main direction is second, and for a large curvature curve, the difference between the variance of the point cloud distribution in the main direction and the variance of the point cloud distribution in the direction perpendicular to the main direction is the smallest, therefore, according to the first parameter, it can be determined that the target road segment indicated by the target point cluster is a straight road or a small curvature curve or a large curvature curve. The larger the first parameter, the closer the road type is to a straight road.

[0081] In some embodiments, determining the road type of the target road segment indicated by the target point cluster in the above step S103 may specifically include the following steps S103B1 to S103B4.

[0082] S103B1. Determine a second parameter according to the first singular value and the second singular value, where the second parameter is used to indicate a difference between the first singular value and the second singular value.

[0083] In some embodiments, “determining a second parameter according to the first singular value and the second singular value” in S103B1 includes: determining a ratio of the second singular value to the first singular value as the second parameter.

[0084] S103B2: When the second parameter is greater than or equal to a fourth threshold, determine that the road type of the target road segment indicated by the target point cluster is a large curvature curve.

[0085] S103B3: When the second parameter is less than the fourth threshold value and greater than the fifth threshold value, determine that the road type of the target road segment indicated by the target point cluster is a small curvature curve.

[0086] S103B4: When the second parameter is less than or equal to the fifth threshold, determine that the road type of the target road segment indicated by the target point cluster is a straight road.

[0087] Among them, the fourth threshold is greater than the fifth threshold, and the curvature of the small curvature curve is smaller than the curvature of the large curvature curve.

[0088] Other embodiments of the present invention are further optimized based on the above embodiments. In some embodiments, the above S103 is specifically based on the following steps S103C1 to S103C5 to determine the road type of the target road segment indicated by the target point cluster.

[0089] S103C1. Determine a first parameter according to the first singular value and the second singular value, where the first parameter is used to indicate a difference between the first singular value and the second singular value.

[0090] S103C2: When the first parameter is greater than or equal to the first threshold, determine that the road type of the target road segment indicated by the target point cluster is a straight road.

[0091] S103C3: When the first parameter is less than the first threshold value and greater than the second threshold value, determine that the road type of the target road segment indicated by the target point cluster is a small curvature curve.

[0092] S103C4: When the first parameter is less than or equal to the second threshold value and greater than the third threshold value, determine that the road type of the target road segment indicated by the target point cluster is a medium curvature curve.

[0093] S103C5: When the first parameter is less than or equal to the third threshold, determine that the road type of the target road segment indicated by the target point cluster is a large curvature curve.

[0094] Among them, the first threshold is greater than the second threshold, the second threshold is greater than the third threshold, the curvature of the small curvature curve is smaller than the curvature of the medium curvature curve, and the curvature of the medium curvature curve is smaller than the curvature of the large curvature curve.

[0095] In summary, the embodiments of the present application provide a road type recognition method, device, electronic device, storage medium and vehicle, the method comprising: clustering the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters, N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment; taking one point cluster among the N point clusters as a target point cluster, performing singular value decomposition on the target point cluster to obtain a first singular value and a second singular value, the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction; based on the difference between the first singular value and the second singular value, determining the road type of the target road segment indicated by the target point cluster. In this solution, the road point cloud in the BEV road perception image is clustered according to the distance between the points to obtain N point clusters; one of the N point clusters is used as the target point cluster, and the target point cluster is subjected to singular value decomposition to obtain the first singular value and the second singular value; based on the difference between the first singular value and the second singular value, the road type of the target road segment indicated by the target point cluster is determined. In this way, different road segments in the road data can be distinguished by clustering, and the first singular value and the second singular value are obtained by the singular value decomposition algorithm. Based on the difference between the first singular value and the second singular value, the road type is determined. Compared with the prior art, the road type of the road segment indicated by the road data can be accurately identified, and the recognition accuracy of the road type of the road segment indicated by the road data can be improved.

[0096] The present application also provides a road type identification device, Figure 5 A schematic diagram of the structure of a road type identification device provided in this application, such as Figure 5 As shown, the road type identification device 50 includes:

[0097] The clustering unit 51 is used to cluster the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters, where N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment; the singular value decomposition unit 52 is used to take one point cluster among the N point clusters as a target point cluster, perform singular value decomposition on the target point cluster, and obtain a first singular value and a second singular value, where the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction, and the main direction of the target point cluster is the driving direction of the target road segment indicated by the target point cluster; the determination unit 53 is used to determine the road type of the target road segment indicated by the target point cluster based on the difference between the first singular value and the second singular value. In some embodiments of the present application, the determination unit 53 is specifically used to determine a first parameter based on the difference between the first singular value and the second singular value, where the first parameter is used to indicate the difference between the first singular value and the second singular value; when the first parameter is greater than or equal to a first threshold, the road type of the target point cluster is determined to be a straight road; when the first parameter is less than the first threshold and greater than the second threshold, the road type of the target point cluster is determined to be a small curvature curve; when the first parameter is less than or equal to the second threshold, the road type of the target point cluster is determined to be a large curvature curve; the first threshold is greater than the second threshold, and the curvature of the small curvature curve is less than the curvature of the large curvature curve.

[0098] In some embodiments of the present application, the determination unit 53 is specifically configured to determine a ratio of the first singular value to the second singular value as the first parameter.

[0099] In some embodiments of the present application, the determination unit 53 is specifically configured to determine a difference between the first singular value and the second singular value as the first parameter.

[0100] In some embodiments of the present application, the clustering unit 51 is specifically used to determine the clustering distance; based on the clustering distance, the road point cloud is clustered according to the distance between points to obtain N point clusters.

[0101] In some embodiments of the present application, the clustering unit 51 is specifically used to cluster the road point cloud according to the point spacing based on the cluster spacing to obtain M point clusters, where M is an integer greater than or equal to N; determine the number of point clouds in each of the M point clusters; and determine the point clusters in the M point clusters whose point cloud numbers are greater than or equal to a quantity threshold as N point clusters.

[0102] In some embodiments of the present application, the determination unit 53 is also used to determine that the road scene indicated by the road point cloud is a non-intersection scene when N is equal to 1; to determine that the road scene indicated by the road point cloud is a two-way intersection scene when N is equal to 2; to determine that the road scene indicated by the road point cloud is a three-way intersection scene when N is equal to 3; and to determine that the road scene indicated by the road point cloud is a four-way intersection scene when N is equal to 4.

[0103] It should be noted that the above-mentioned road type identification device can be the electronic device in the above-mentioned method embodiment of the present application, or it can be a functional module and / or functional entity in the electronic device that can realize the functions of the device embodiment, and the embodiment of the present application is not limited.

[0104] In the embodiments of the present application, each module can implement the road type identification method provided in the above method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0105] Figure 6 The structural diagram of an electronic device provided in the embodiment of the present application is used to exemplify the electronic device that implements any road type recognition method in the embodiment of the present application, and should not be understood as a specific limitation on the embodiment of the present application.

[0106] like Figure 6 As shown, the electronic device 600 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0107] Typically, the following devices may be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although an electronic device 600 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.

[0108] 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 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processor 601, the functions defined in any road type identification method provided in the embodiment of the present application can be executed.

[0109] 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.

[0110] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText 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.

[0111] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0112] The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements any road type recognition method.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] An embodiment of the present application also provides a vehicle, comprising: the above-mentioned road type recognition device, or the above-mentioned electronic device, or the above-mentioned computer-readable storage medium.

[0119] 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.

[0120] 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.

[0121] 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 road type recognition method, It is characterized in that The method comprises: Clustering the road point cloud in the BEV road perception image according to the point spacing to obtain N point clusters, where N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment; Taking one of the N point clusters as a target point cluster, performing singular value decomposition on the target point cluster to obtain a first singular value and a second singular value, wherein the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in the main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction, and the main direction of the target point cluster is the driving direction of the target road segment indicated by the target point cluster; Based on the difference between the first singular value and the second singular value, a road type of the target road segment indicated by the target point cluster is determined.

2. The method according to claim 1, It is characterized in that The determining the road type of the target point cluster based on the difference between the first singular value and the second singular value includes: Determine a first parameter according to the first singular value and the second singular value, where the first parameter is used to indicate a difference between the first singular value and the second singular value; When the first parameter is greater than or equal to a first threshold, determining that the road type of the target point cluster is a straight road; When the first parameter is less than the first threshold value and greater than the second threshold value, determining that the road type of the target point cluster is a small curvature curve; When the first parameter is less than or equal to the second threshold, determining that the road type of the target point cluster is a large curvature curve; The first threshold is greater than the second threshold, and the curvature of the small curvature curve is smaller than the curvature of the large curvature curve.

3. The method according to claim 2, It is characterized in that The step of determining a first parameter according to the first singular value and the second singular value comprises: A ratio of the first singular value to the second singular value is determined as the first parameter.

4. The method according to claim 2, It is characterized in that The step of determining a first parameter according to the first singular value and the second singular value comprises: A difference between the first singular value and the second singular value is determined as the first parameter.

5. The method according to claim 1, It is characterized in that The road point cloud in the BEV road perception image is clustered according to the distance between points to obtain N point clusters, including: Determine the cluster spacing; Based on the clustering distance, the road point cloud is clustered according to the distance between points to obtain the N point clusters.

6. The method according to claim 5, It is characterized in that The step of clustering the road point cloud according to the point spacing based on the cluster spacing to obtain the N point clusters includes: Based on the clustering distance, clustering the road point cloud according to the distance between points to obtain M point clusters, where M is an integer greater than or equal to N; Determine the number of point clouds of each point cluster in the M point clusters; The point clusters whose point cloud quantity is greater than or equal to the quantity threshold among the M point clusters are determined as the N point clusters.

7. The method according to any one of claims 1 to 6, It is characterized in that The method further comprises: When N is equal to 1, determining that the road scene indicated by the road point cloud is a non-intersection scene; When N is equal to 2, determining that the road scene indicated by the road point cloud is a two-way intersection scene; When N is equal to 3, determining that the road scene indicated by the road point cloud is a three-way intersection scene; When N is equal to 4, it is determined that the road scene indicated by the road point cloud is a four-way intersection scene.

8. A road type recognition device, It is characterized in that include: A clustering unit, used for clustering the road point cloud in the BEV road perception image according to the distance between points to obtain N point clusters, where N is a positive integer, and each point cluster in the N point clusters is used to indicate a different road segment; a singular value decomposition unit, configured to take one of the N point clusters as a target point cluster, perform singular value decomposition on the target point cluster, and obtain a first singular value and a second singular value, wherein the first singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a main direction of the target point cluster, and the second singular value is used to indicate the variance of the point cloud distribution of the target point cluster in a direction perpendicular to the main direction, and the main direction of the target point cluster is the driving direction of the target road segment indicated by the target point cluster; A determining unit is used to determine a road type of a target road segment indicated by the target point cluster based on a difference between the first singular value and the second singular value.

9. An electronic device, It is characterized in that include: A processor, wherein the processor is used to execute a computer program stored in a memory, wherein the computer program, when executed by the processor, implements the steps of the road type recognition method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the road type identification method according to any one of claims 1 to 7 are implemented.

11. A vehicle, It is characterized in that include: The road type recognition device as claimed in claim 8, or the electronic device as claimed in claim 9, or includes the computer-readable storage medium as claimed in claim 10.

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