CN113947906A

CN113947906A - Traffic network detection method and device and electronic equipment

Info

Publication number: CN113947906A
Application number: CN202111217912.0A
Authority: CN
Inventors: 慎东辉
Original assignee: Apollo Zhilian Beijing Technology Co Ltd
Current assignee: Apollo Zhilian Beijing Technology Co Ltd
Priority date: 2021-10-19
Filing date: 2021-10-19
Publication date: 2022-01-18

Abstract

The disclosure provides a traffic network detection method, a traffic network detection device and electronic equipment, and relates to the technical field of artificial intelligence such as automatic driving and intelligent traffic. The implementation scheme is as follows: when detecting whether the traffic networks of different versions are consistent, first attribute data of a first traffic network and second attribute data of a second traffic network can be respectively acquired; the traffic network comprises intersections and road information, and the attribute data of the road information is described based on the attribute data of the intersections and/or the attribute data of the road segments closest to the intersections; and on the basis of the first attribute data and the second attribute data described by the attribute data of the intersection and/or the attribute data of the road segment, the consistency of the first road information in the first traffic network and the second road information in the second traffic network is detected, the calculation complexity is low, whether the traffic networks of different versions are consistent or not can be efficiently detected, and therefore the consistency detection of the traffic networks of different versions is realized.

Description

Traffic network detection method and device and electronic equipment

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to the field of artificial intelligence technologies such as automatic driving and intelligent transportation, and in particular, to a method and an apparatus for detecting a traffic network, and an electronic device.

Background

The traffic network plays a vital role in the fields of automatic driving, intelligent traffic and the like. Taking the field of automatic driving as an example, consistency detection is carried out on different versions of traffic networks, which is beneficial to improving the driving safety of automatic driving vehicles.

Therefore, how to detect whether the traffic networks of different versions are consistent is an urgent problem to be solved by those skilled in the art.

Disclosure of Invention

The present disclosure provides a method, an apparatus, and an electronic device for detecting a traffic network, which can efficiently detect whether traffic networks of different versions are consistent, thereby implementing consistency detection of traffic networks of different versions.

According to a first aspect of the present disclosure, there is provided a method for detecting a traffic network, which may include:

respectively acquiring first attribute data of a first traffic network and second attribute data of a second traffic network; the traffic network comprises intersections and road information, and the attribute data of the road information is described based on the attribute data of the intersections and/or the attribute data of the road segments closest to the intersections.

And detecting consistency of first road information in the first traffic network and second road information in the second traffic network based on the first attribute data and the second attribute data.

According to a second aspect of the present disclosure, there is provided a detection device of a traffic network, which may include:

the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for respectively acquiring first attribute data of a first traffic network and second attribute data of a second traffic network; the traffic network comprises intersections and road information, and the attribute data of the road information is described based on the attribute data of the intersections and/or the attribute data of the road segments closest to the intersections.

And the detection unit is used for detecting the consistency of the first road information in the first traffic network and the second road information in the second traffic network based on the first attribute data and the second attribute data.

According to a third aspect of the present disclosure, there is provided an electronic device, which may include:

at least one processor; and

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

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of detecting a traffic network according to the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to execute the method for detecting a traffic network according to the first aspect.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of detecting a traffic network according to the first aspect.

According to the technical scheme disclosed by the invention, whether the traffic networks of different versions are consistent or not can be efficiently detected, so that the consistency detection of the traffic networks of different versions is realized.

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

Drawings

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

fig. 1 is a schematic flow chart of a method for detecting a traffic network according to a first embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a traffic network provided by an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of a method for detecting consistency of first road information and second road information according to a third embodiment of the disclosure;

fig. 4 is a schematic structural diagram of a detection device for a traffic network according to a fourth embodiment of the present disclosure;

fig. 5 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In embodiments of the present disclosure, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the access relationship of the associated object, meaning that there may be three relationships, e.g., A and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. In the description of the text of the present disclosure, the character "/" generally indicates that the former and latter associated objects are in an "or" relationship. In addition, in the embodiments of the present disclosure, "first", "second", "third", "fourth", "fifth", and "sixth" are only used to distinguish the contents of different objects, and have no other special meaning.

The technical scheme provided by the embodiment of the disclosure can be applied to the technical fields of automatic driving, intelligent transportation and the like of a traffic network. Taking the field of automatic driving as an example, consistency detection of different versions of traffic networks is helpful to improve the driving safety of automatic driving vehicles.

At present, when a traffic network is manufactured, identifiers corresponding to the same physical space in traffic networks of different versions are inconsistent due to different sources of multi-platform collected traffic network data and other reasons; or, for reasons such as road repair, the labels corresponding to the same physical space are inconsistent compared with the new traffic network, that is, the traffic networks of different versions are inconsistent, which results in lower safety of the automatic driving vehicle. Therefore, how to detect whether the traffic networks of different versions are consistent is an urgent problem to be solved by those skilled in the art.

In the related art, when detecting whether the identifiers corresponding to the same physical space in the traffic network are consistent, that is, whether the traffic networks of different versions are consistent, spatial coordinate calculation is usually performed based on points, lines, and planes in the physical space, and whether the identifiers corresponding to the same physical space are consistent is determined according to the calculation result, thereby implementing consistency detection on the traffic networks of different versions.

However, the spatial coordinate calculation based on the points, lines, and planes in the physical space can realize the consistency detection of the traffic networks of different versions, but the calculation complexity of tens of millions by tens of millions needs to be executed, and the calculation complexity is high, so that the detection efficiency is low.

In order to efficiently detect whether different versions of traffic networks are consistent, in a normal situation, for different versions of traffic networks, attribute data of corresponding intersections and attribute data of road segments closest to intersections, namely links, are generally consistent, therefore, it can be considered that the attribute data of intersections and the attribute data of road segments closest to intersections, namely links, are used as basic data, and road information except intersections in the traffic networks is described based on the attribute data of intersections and/or the attribute data of road segments closest to intersections, so that whether different versions of traffic networks are consistent can be detected based on the attribute data of intersections and/or the attribute data of road segments closest to intersections, and the calculation complexity is lower compared with the calculation of space coordinates based on points, lines and planes in a physical space, whether the traffic networks of different versions are consistent or not can be efficiently detected, and therefore consistency detection of the traffic networks of different versions is achieved.

Wherein, the intersection, i.e. cross, can be understood as the set containing branches and lanes in all physical directions of the intersection; the link is a small line segment expressing two-point communication in a traffic network, the length of the link is generally 1-500 m, the ends of the link are all broken points in a road, such as a bifurcation, a left-right turning exit, and no broken point exists between the links.

Based on the above technical concept, embodiments of the present disclosure provide a method for detecting a traffic network, and the method for detecting a traffic network provided by the present disclosure will be described in detail with specific embodiments. It is to be understood that the following detailed description may be combined with other embodiments, and that the same or similar concepts or processes may not be repeated in some embodiments.

Example one

Fig. 1 is a flowchart illustrating a method for detecting a traffic network according to a first embodiment of the present disclosure, where the method for detecting a traffic network may be executed by software and/or a hardware device, for example, the hardware device may be a terminal or a server. For example, referring to fig. 1, the method for detecting a traffic network may include:

s101, first attribute data of a first traffic network and second attribute data of a second traffic network are obtained respectively.

The first traffic network and the second traffic network are two traffic networks of different versions. For any one of the first traffic network and the second traffic network, the traffic network includes intersection and road information, and the attribute data of the road information is described based on the attribute data of the intersection and/or the attribute data of the road segment closest to the intersection.

For example, attribute data for an intersection may include an identification and spatial coordinates of a node of the intersection; for example, if the intersection is a regularly shaped intersection, the node may be a center point of the intersection; in addition, the attribute data of the intersection can also comprise the identifications and the spatial coordinates of a plurality of nodes of the intersection; the nodes can be nodes corresponding to a plurality of branches included in the intersection; here, the embodiment of the present disclosure is only described by taking the two ways that the attribute data of the intersection can be used as examples, but the embodiment of the present disclosure is not limited thereto.

It can be understood that, when determining attribute data of an intersection, in view of the fact that the attribute data of the intersection includes an identifier and a spatial coordinate of a plurality of nodes of the intersection, the intersection can be described more accurately with respect to the attribute data of the intersection including an identifier and a spatial coordinate of one node of the intersection, and therefore, the identifiers and the spatial coordinates of the plurality of nodes of the intersection are used to describe the intersection, so that the accuracy of consistency detection can be improved when consistency detection is performed subsequently based on the attribute data of the intersection.

In the example, the road segment is expressed by a minimum break line segment of the map network, that is, there is no break in the middle of a line segment drawn between the start point and the end point of the road segment. The attribute data of the road segment may include information such as an identification of the road segment, attribute data of a start node of the road segment, attribute data of an end node of the road segment, and a length of the road segment. The node may be understood as a minimum spatial granularity point expression in a traffic network, and the attribute data of the node may include an identifier and/or a spatial coordinate of the node, and may also include other attribute information. For example, the identifier of the node may be obtained by rounding the spatial coordinates of the node in centimeter level according to spatial precision and then performing hash code calculation, for example, the identifier may be obtained by generating nonrepeating hash code calculation by using an algorithm such as MD 5.

It is understood that when the attribute data of a node of a road segment far from the intersection changes, it is said that the road segment is interrupted, but in general, even if the road segment is interrupted, the attribute data of a node of the road segment near the intersection side is not affected, that is, the attribute data of a node of the road segment near the intersection side remains unchanged.

For example, the first traffic network and the second traffic network may be different versions of traffic networks generated by different road network platforms, or may be different versions of traffic networks generated by the same road network platform. When the first traffic network and the second traffic network are traffic networks of different versions generated by the same road network platform, it can be understood that the first traffic network is an original traffic network, and due to reasons such as road repair, a physical space in the first traffic network changes, and the second traffic network is a traffic network with a changed physical space.

For example, when acquiring the first attribute data of the first traffic network, the first attribute data of the first traffic network sent by another device may be received, or the first attribute data of the first traffic network may be acquired locally, or the first attribute data of the first traffic network may be acquired by another method, and may be specifically set according to actual needs.

It should be noted that, a method for acquiring the second attribute data of the second traffic network is similar to the method for acquiring the first attribute data of the first traffic network, and reference may be made to the related description for acquiring the first attribute data of the first traffic network, and here, the embodiment of the disclosure is not repeated.

After the first attribute data of the first traffic network and the second attribute data of the second traffic network are respectively obtained, consistency between the first road information in the first traffic network and the second road information in the second traffic network may be detected based on the first attribute data and the second attribute data, that is, the following S102 is executed:

s102, detecting consistency of first road information in the first traffic network and second road information in the second traffic network based on the first attribute data and the second attribute data.

The first road information is road information in a first traffic network, and the second road information is road information in a second traffic network, and the description is given by the first road information and the second road information only for distinguishing the road information in the first traffic network from the road information in the second traffic network.

It can be seen that, in the embodiment of the present disclosure, when detecting whether traffic networks of different versions are consistent, first attribute data of a first traffic network and second attribute data of a second traffic network may be respectively obtained; the traffic network comprises intersections and road information, and the attribute data of the road information is described based on the attribute data of the intersections and/or the attribute data of the road segments closest to the intersections; and on the basis of the first attribute data and the second attribute data described by the attribute data of the intersection and/or the attribute data of the road segment, the consistency of the first road information in the first traffic network and the second road information in the second traffic network is detected, the calculation complexity is low, whether the traffic networks of different versions are consistent or not can be efficiently detected, and therefore the consistency detection of the traffic networks of different versions is realized.

Based on the embodiment shown in fig. 1, it can be seen that, in order to implement consistency detection on first road information in a first traffic network and second road information in a second traffic network, two phases may be included, where in the first phase, attribute data of the respective road information of the first traffic network and the second traffic network may be modeled first, and corresponds to S101 in the embodiment shown in fig. 1; in the second stage, consistency between the first road information in the first traffic network and the second road information in the second traffic network is detected based on the attribute data of the road information in the first traffic network and the second traffic network obtained through modeling, which corresponds to S102 in the embodiment shown in fig. 1, so as to implement consistency detection between the first road information in the first traffic network and the second road information in the second traffic network.

For the first stage, when modeling the attribute data of the road information of the first traffic network and the second traffic network, considering that the modeling methods of the attribute data of the road information of the first traffic network and the attribute data of the road information of the second traffic network are similar, to avoid redundancy, hereinafter, the detailed description will be given on how to model the road information of the traffic network by taking the road information of any one of the road information of the first traffic network and the road information of the second traffic network as an example, and the following second embodiment may be referred to.

Example two

Before describing in detail how road information of a traffic network is modeled, the road information of the traffic network is explained. For example, the road information of the traffic network may include at least one of a branch, a lane, a road segment, a trunk line or a region, and may also include other road information, and the embodiment of the present disclosure is only described as an example that the road information may include at least one of a branch, a lane, a road segment, a trunk line or a region, but does not represent that the embodiment of the present disclosure is limited thereto.

A branch, i.e. branch, may be understood as a set of multiple entrances and/or multiple lanes at an exit in a physical direction within an intersection. Referring to fig. 2, fig. 2 is a schematic view of a traffic network according to an embodiment of the disclosure, fig. 2 includes an intersection, and 8 branches, where the 8 branches are a south-entering branch, a south-exiting branch, a north-entering branch, a north-exiting branch, an east-entering branch, an east-exiting branch, a west-entering branch, and a west-exiting branch.

Referring to the intersection shown in fig. 2, the intersection is a regular rectangular intersection, for example, the attribute data of the intersection may include an identifier and a spatial coordinate of a node of the intersection, and the node may be a central point of the intersection; the attribute data of the intersection can also comprise identifications and space coordinates of a plurality of nodes of the intersection; wherein, the node can be the identifier and the space coordinate of the node corresponding to 8 branches included in the intersection, and the 8 nodes are respectively: node 1, node 2, node 3, node 4, node 5, node 6, node 7, and node 8.

A lane, i.e. lane, may be understood as a lane corresponding to the entrance and/or exit of a physical direction within the intersection. For example, as shown in fig. 2, the south-entering branch includes two lanes, which are a straight lane and a right-turn lane.

A road segment, i.e. a roadsegment, can be understood as a set of directed road segments or lines communicating between two intersections, wherein a line is a set of points.

A trunk line, also called a roadline, may be understood as a collection of road segments between multiple intersections, from a starting intersection to an ending intersection, passing through an ordered intersection in the middle.

A region, i.e. an area, can be understood as a closable planar topology consisting of a set of a series of boundary points.

Intersection flow, i.e., flow, can be understood as having a branch identification, and a left, straight, right turn enumeration composite identification and topology of the actual traffic of that branch.

Having introduced the concept of a branch, a lane, a section, a trunk or a region in detail, below, it will be described in detail how to model attribute data of road information of a traffic network. It is understood that, in the embodiment of the present disclosure, in modeling the attribute data of the road information of the traffic network, since the attribute data of the corresponding intersection and the attribute data of the road segment closest to the intersection are generally consistent for different versions of the traffic network under normal circumstances, the attribute data of the road information of the traffic network, that is, the attribute data of the branch, the lane, the road segment, the trunk, and the region, can be modeled based on the attribute data of the intersection and/or the attribute data of the road segment closest to the intersection.

For example, when the road information includes a branch, the attribute data of the branch may be modeled based on the attribute data of the road segment closest to the intersection, and thus the attribute data of the branch may multiplex the attribute data of the road segment, that is, include the attribute data of the road segment closest to the target intersection, which is the intersection closest to the branch in the physical direction along the branch, in view of one road segment corresponding to one branch. For example, referring to fig. 2, the attribute data of the southbound branch may include the attribute data of the road segment composed of the node 1 and the node 9, taking the southbound branch as an example.

It should be noted that, when modeling attribute data of a branch, for some special cases, there may be links other than branch road segments at an intersection to which the branch belongs, and therefore, in order to accurately describe the branch, attribute data of a road segment and an identifier of a side node of the road segment near the intersection may be determined together as attribute data of the branch, and may be specifically set according to actual needs.

For example, when the road information includes a lane, in view of the lane being a lane in a branch, when modeling attribute data of the lane, the attribute data of the lane may include attribute data of a target branch to which the lane belongs, a lane identification of the lane in the target branch, and steering information of the lane. For example, the lane markings may be numbered in the order of the vehicle traveling direction from left to right, and the number may be determined as the lane marking. Illustratively, the steering information may be left turns, straight lines, or right turns.

For example, as shown in fig. 2, taking the left lane in the southbound branch as an example, the attribute data of the left lane may include attribute data of the southbound branch, lane identification of the left lane, and straight information of the left lane.

For example, when the road information includes a road segment, the attribute data of the road segment may be modeled based on the attribute data of the intersection, and the attribute data of the road segment may include attribute data of two intersections corresponding to the head and the tail of the road segment. For example, assuming that a road segment is a set of directed road segments communicated between intersection 1 and intersection 2, the attribute data of the road segment may include attribute data of intersection 1 and attribute data of intersection 2.

For example, when the road information includes a trunk line, the attribute data of the trunk line may be modeled based on the attribute data of the intersections, and may be described by an ordered set of identifications of the first intersection, the last intersection, and the middle intersection, and/or an ordered set of section identifications, that is, the attribute data of the trunk line may include attribute data of two intersections corresponding to the head and the tail of the trunk line, and attribute data of all intersections through which the trunk line passes, and/or attribute data of the section. Taking the example that the attribute data of the trunk line can include attribute data of two intersections corresponding to the head and the tail of the trunk line and attribute data of all intersections through which the trunk line passes, assuming that the trunk line is a road segment set of ordered intersections between a starting intersection, an intersection 1 to a terminating intersection and an intersection 4, the attribute data of the trunk line can include attribute data of the intersection 1, attribute data of the intersection 4 and attribute data of an intersection 2 and an intersection 3 through which the trunk line passes.

For example, when the road information includes an area, when the attribute data of the area is modeled, the attribute data of the area includes attribute data of all intersections within the area, attribute data of links, and attribute data of trunks. For example, the attribute data of the area may further include attribute data of all branches in the area, and the embodiment of the present disclosure is described by taking the example that the attribute data of the area includes attribute data of all intersections in the area, attribute data of road segments, and attribute data of main lines, but the embodiment of the present disclosure is not limited thereto.

With the above description, based on the attribute data of the intersection and/or the attribute data of the road segment closest to the intersection, the attribute data of the road information can be obtained by modeling respectively, that is, the first stage of performing the consistency detection on the traffic road network is completed. Next, with respect to the second phase, how to detect the consistency between the first road information in the first traffic network and the second road information in the second traffic network based on the attribute data of the road information in the first traffic network and the second traffic network obtained by modeling will be described in detail. The first road information is road information in a first traffic network, and the second road information is road information in a second traffic network.

Based on any of the above embodiments, in order to facilitate understanding of how to detect consistency between first road information in a first traffic network and second road information in a second traffic network based on first attribute data and second attribute data in the embodiments of the present disclosure, a detailed description will be given below by using an embodiment three shown in fig. 3.

EXAMPLE III

Fig. 3 is a flowchart illustrating a method for detecting consistency of first road information and second road information according to a third embodiment of the present disclosure, where the method for detecting consistency of first road information and second road information may also be performed by software and/or a hardware device, for example, the hardware device may be a terminal or a server. For example, referring to fig. 3, the method for detecting the consistency of the first road information and the second road information may include:

s301, judging whether the first attribute data and the second attribute data comprise attribute data of road information.

Taking the example that the road information in the traffic road network includes a branch, a lane, a link, a trunk, and an area, when detecting the consistency of the first road information and the second road information based on the attribute information of each of the branch, the lane, the link, the trunk, and the area, the consistency of the first road information and the second road information may be detected in the order of the branch, the lane, the link, the trunk, and the area.

It should be noted that, in addition to detecting the consistency between the first road information and the second road information based on the attribute information of each of the branch, the lane, the link, the trunk, and the area, the consistency between the first road information and the second road information may be detected in combination with the attribute information of the intersection. For example, when the consistency of the first road information and the second road information is detected in combination with the attribute information of the intersection, the consistency of the first road information and the second road information may be detected in the order of the branch, the lane, the intersection, the link, the trunk, and the area. The reason why the consistency between the first road information and the second road information is not described here in conjunction with the attribute information of the intersection is that, considering that the attribute data of the corresponding intersections are usually consistent for different versions of traffic networks in a normal case, the consistency between the first road information and the second road information is only detected based on the attribute information of each of the branch, the lane, the link, the trunk, and the area, which is taken as an example, but the present disclosure is not limited thereto.

It is to be understood that, in the embodiment of the present disclosure, when the consistency between the first road information and the second road information is detected based on the respective attribute information of the branch, the lane, the link, the trunk line, and the region, in view of the similarity of the corresponding detection methods, for avoiding repeated description, the consistency between the first road information and the second road information is detected based on the attribute data of the branch, which is not limited to this embodiment.

Taking the road information as a branch as an example, it may be determined whether the first attribute data of the first traffic network includes the attribute data of the branch, and the first attribute data of the second traffic network includes the attribute data of the branch, and the following S302 is executed according to the determination result:

and S302, detecting the consistency of the first road information and the second road information according to the judgment result.

For example, when the second traffic network is the updated traffic network of the first traffic network, when the consistency between the first road information and the second road information is detected according to the determination result, three possible situations may be included:

in a possible case, if the first attribute data includes attribute data of the road information and the second attribute data does not include attribute data of the road information, it is determined that the second road information is the road information deleted when the first traffic network is updated.

Taking the road information as an example, if the first attribute data includes attribute data of the branch, and the second attribute data does not include attribute data of the branch, which indicates that the branch was deleted when the first traffic network was updated, the branch is determined to be the branch deleted when the first traffic network was updated. For example, the attribute data for the delete type and its corresponding branch may be stored in the diff field.

In another possible case, if the first attribute data does not include the attribute data of the road information, and the second attribute data includes the attribute data of the road information, it is determined that the second road information is the road information added when the first traffic network is updated.

Taking the road information as an example, if the first attribute data does not include the attribute data of the branch, and the second attribute data includes the attribute data of the branch, which indicates that the branch is added when updating the first traffic network, the branch is determined to be the branch deleted when updating the first traffic network. For example, the attribute data for the add type and its corresponding branch may be stored in the diff field.

In still another possible case, if the attribute data of the road information is included in both the first attribute data and the second attribute data, it is determined whether the first road information and the second road information coincide with each other.

In this possible case, for example, if the first attribute data and the second attribute data both include attribute data of the road information, it is determined whether the identifier of the first road information and the identifier of the second road information are consistent; and if the identifier of the first road information is consistent with the identifier of the second road information, determining that the first road information is consistent with the second road information.

For example, if the identifier of the first road information and the identifier of the second road information are not consistent, the mapping relationship between the identifier of the first road information and the identifier of the second road information is determined, and the mapping relationship is added to the second road information.

Taking the road information as an example, if the first attribute data and the second attribute data both include attribute data of the branch, which indicates that both the first traffic network and the second traffic network include the branch, where the branch in the first traffic network may be marked as a first branch, and the branch in the second traffic network may be marked as a second branch, it may be further determined whether the identifier of the first branch in the first traffic network and the identifier of the second branch in the second traffic network are consistent; if the identification of the first branch is consistent with the identification of the second branch, which indicates that the identifications corresponding to the branches of the same physical space are the same, determining that the first branch is consistent with the second branch; on the contrary, if the identifier of the first branch is not consistent with the identifier of the second branch, which indicates that the identifiers corresponding to the branches in the same physical space are different, the mapping relationship between the identifier of the first branch and the identifier of the second branch can be further determined, and the mapping relationship is added to the second branch, so that the problem of different identifiers corresponding to the same branch can be further solved.

Further, taking the road information as a lane as an example, how to detect the consistency between the lane in the first traffic network and the lane in the second traffic network based on the attribute data of the lane will be described. For example, it may be determined whether the first attribute data of the first traffic network includes attribute data of the lane or not, and whether the first attribute data of the second traffic network includes attribute data of the lane or not, and the lane in the first traffic network and the lane in the second traffic network may be detected according to the determination result.

For example, in one case, if the first attribute data includes attribute data of a lane and the second attribute data does not include attribute data of the lane, which indicates that the lane is deleted when the first traffic network is updated, the lane is determined to be the lane deleted when the first traffic network is updated.

In another case, if the first attribute data does not include attribute data of a lane, and the second attribute data includes attribute data of the lane, which indicates that the lane is deleted when the first traffic network is updated, the lane is determined to be the lane deleted when the first traffic network is updated.

In another case, if the first attribute data and the second attribute data both include attribute data of lanes, which indicates that the first traffic network and the second traffic network both include the lane, where the lane in the first traffic network may be marked as a first lane, and the lane in the second traffic network may be marked as a second lane, it may be further determined whether the identifier of the first lane in the first traffic network and the identifier of the second lane in the second traffic network are consistent; if the mark of the first lane is consistent with the mark of the second lane, the marks corresponding to the lanes in the same physical space are the same, and the first lane is determined to be consistent with the second lane; on the contrary, if the identifier of the first lane is not consistent with the identifier of the second lane, which indicates that the identifiers corresponding to the lanes in the same physical space are different, the mapping relationship between the identifier of the first lane and the identifier of the second lane can be further determined, and the mapping relationship is added in the second lane, so that the problem that the identifiers corresponding to the same lane are different can be further solved.

In the embodiment of the present disclosure, the detection of the consistency between the first road information and the second road information based on the attribute information of each of the branch and the lane is taken as an example. It can be understood that, the method for detecting the consistency of the first road information and the second road information based on the attribute information of the intersection, the road segment, the trunk line, and the region is similar to the method for detecting the consistency of the first road information and the second road information based on the attribute information of the branch and the lane, and reference may be made to the above-mentioned related description for detecting the consistency of the first road information and the second road information based on the attribute information of the branch and the lane, and here, the embodiment of the present disclosure is not described again.

It can be seen that, in the embodiment of the present disclosure, when consistency between first road information in a first traffic network and second road information in a second traffic network is detected, it may be determined whether attribute data of the road information is included in the first attribute data and the second attribute data, and consistency between the first road information and the second road information is detected according to a determination result, so that consistency between the first road information in the first traffic network and the second road information in the second traffic network is detected based on the first attribute data and the second attribute data described by the attribute data of the intersection and/or the attribute data of the road segment, and the calculation complexity is low, and it may be efficiently detected whether traffic networks of different versions are consistent, thereby implementing consistency detection on traffic networks of different versions.

Example four

Fig. 4 is a schematic structural diagram of a detection device 40 of a traffic network according to a fourth embodiment of the present disclosure, for example, please refer to fig. 4, the detection device 40 of the traffic network may include:

an obtaining unit 401, configured to obtain first attribute data of a first traffic network and second attribute data of a second traffic network, respectively; the traffic network comprises intersections and road information, and the attribute data of the road information is described based on the attribute data of the intersections and/or the attribute data of the road segments closest to the intersections.

A detecting unit 402, configured to detect consistency between first road information in the first traffic network and second road information in the second traffic network based on the first attribute data and the second attribute data.

Optionally, the road information comprises at least one of a branch, a lane, a section, a trunk, or a region; the detection unit 402 includes a first detection module and a second detection module.

The first detection module is used for judging whether the first attribute data and the second attribute data comprise attribute data of road information or not;

and the second detection module is used for detecting the consistency of the first road information and the second road information according to the judgment result.

Optionally, the second traffic network is an updated traffic network of the first traffic network; the second detection module comprises a first detection submodule, a second detection submodule and a third detection submodule.

And the first detection submodule is used for determining that the second road information is the road information deleted when the first traffic network is updated if the first attribute data comprises the attribute data of the road information and the second attribute data does not comprise the attribute data of the road information.

And the second detection submodule is used for determining that the second road information is the newly added road information when the first traffic network is updated if the first attribute data does not include the attribute data of the road information and the second attribute data includes the attribute data of the road information.

And the third detection submodule is used for determining whether the first road information and the second road information are consistent or not if the first attribute data and the second attribute data both comprise attribute data of the road information.

Optionally, the third detection submodule is specifically configured to, if the first attribute data and the second attribute data both include attribute data of road information, determine whether an identifier of the first road information is consistent with an identifier of the second road information; and if the identifier of the first road information is consistent with the identifier of the second road information, determining that the first road information is consistent with the second road information.

Optionally, the second detection module further includes a fourth detection submodule and a fifth detection submodule.

And the fourth detection submodule is used for determining the mapping relation between the identifier of the first road information and the identifier of the second road information if the identifier of the first road information is inconsistent with the identifier of the second road information.

And the fifth detection submodule is used for adding the mapping relation in the second road information.

Optionally, the road information includes a branch, the attribute data of the branch includes attribute data of a road segment closest to the target intersection, and the target intersection is an intersection closest to the branch in a physical direction along the branch.

Optionally, the road information further includes a lane, and the attribute data of the lane includes attribute data of a target branch to which the lane belongs, a lane identification of the lane in the target branch, and steering information of the lane.

Optionally, the road information further includes a road segment, and the attribute data of the road segment includes attribute data of two intersections corresponding to the head and the tail of the road segment.

Optionally, the road information further includes a trunk line, and the attribute data of the trunk line includes attribute data of two intersections corresponding to the head and the tail of the trunk line, and attribute data of all intersections through which the trunk line passes.

Optionally, the road information further includes a region, and the attribute data of the region includes attribute data of all intersections, attribute data of road segments, and attribute data of trunk lines in the region.

The detection apparatus 40 for a traffic network according to the embodiment of the present disclosure may implement the technical solution of the detection method for a traffic network shown in any one of the above embodiments, and its implementation principle and beneficial effects are similar to those of the detection method for a traffic network, and reference may be made to the implementation principle and beneficial effects of the detection method for a traffic network, which are not described herein again.

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

According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.

Fig. 5 is a schematic block diagram of an electronic device 50 provided by an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 5, the apparatus 50 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 50 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

A number of components in device 50 are connected to I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 50 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 executes the respective methods and processes described above, such as the detection method of the traffic network. For example, in some embodiments, the method of detecting traffic networks may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 50 via ROM 502 and/or communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the detection method of a traffic network described above may be performed. Alternatively, in other embodiments, the calculation unit 501 may be configured by any other suitable means (e.g. by means of firmware) to perform the detection method of the traffic network.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

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

Claims

1. A method for detecting a traffic network comprises the following steps:

respectively acquiring first attribute data of a first traffic network and second attribute data of a second traffic network; the traffic network comprises intersections and road information, and the attribute data of the road information is described based on the attribute data of the intersections and/or the attribute data of the road segments closest to the intersections;

2. The method of claim 1, wherein the road information comprises at least one of a branch, a lane, a segment, a trunk, or a region;

the detecting consistency of first road information in the first traffic network and second road information in the second traffic network based on the first attribute data and the second attribute data includes:

judging whether attribute data of the road information is included in the first attribute data and the second attribute data;

and detecting the consistency of the first road information and the second road information according to the judgment result.

3. The method of claim 2, wherein the second traffic network is an updated traffic network of the first traffic network;

the detecting the consistency of the first road information and the second road information according to the judgment result includes:

if the first attribute data comprises attribute data of the road information and the second attribute data does not comprise attribute data of the road information, determining that the second road information is the road information deleted when the first traffic network is updated;

if the first attribute data does not include the attribute data of the road information and the second attribute data includes the attribute data of the road information, determining that the second road information is the newly added road information when the first traffic network is updated;

and if the first attribute data and the second attribute data both comprise the attribute data of the road information, determining whether the first road information and the second road information are consistent.

4. The method of claim 3, wherein determining whether the first road information and the second road information are consistent if the first attribute data and the second attribute data both include attribute data of the road information comprises:

if the first attribute data and the second attribute data both include the attribute data of the road information, judging whether the identifier of the first road information and the identifier of the second road information are consistent;

and if the identifier of the first road information is consistent with the identifier of the second road information, determining that the first road information is consistent with the second road information.

5. The method of claim 4, further comprising:

if the identifier of the first road information is inconsistent with the identifier of the second road information, determining a mapping relation between the identifier of the first road information and the identifier of the second road information;

adding the mapping relationship to the second road information.

6. The method according to any one of claims 1-5, said road information comprising a branch, the attribute data of said branch comprising attribute data of a road segment closest to a target intersection, said target intersection being the intersection closest to said branch in the physical direction of said branch.

7. The method of claim 6, the road information further comprising a lane, the attribute data of the lane comprising attribute data of a target branch to which the lane belongs, a lane identification of the lane in the target branch, and steering information of the lane.

8. The method according to any one of claims 1 to 7, wherein the road information further comprises a road segment, and the attribute data of the road segment comprises attribute data of two intersections corresponding to the head and the tail of the road segment.

9. The method according to any one of claims 1 to 8, wherein the road information further includes a trunk line, and the attribute data of the trunk line includes attribute data of two intersections corresponding to the trunk line end to end and attribute data of all intersections through which the trunk line passes.

10. The method according to any one of claims 1 to 9, the road information further comprising an area, the attribute data of the area including attribute data of all intersections within the area, attribute data of road segments, and attribute data of trunks.

11. A traffic network detection device comprising:

the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for respectively acquiring first attribute data of a first traffic network and second attribute data of a second traffic network; the traffic network comprises intersections and road information, and the attribute data of the road information is described based on the attribute data of the intersections and/or the attribute data of the road segments closest to the intersections;

12. The apparatus of claim 11, wherein the road information comprises at least one of a branch, a lane, a segment, a trunk, or a region; the detection unit comprises a first detection module and a second detection module;

the first detection module is configured to determine whether attribute data of the road information is included in the first attribute data and the second attribute data;

13. The apparatus of claim 12, wherein said second traffic network is an updated traffic network of said first traffic network; the second detection module comprises a first detection submodule, a second detection submodule and a third detection submodule;

the first detection submodule is configured to determine that the second road information is the road information deleted when the first traffic network is updated if the first attribute data includes attribute data of the road information and the second attribute data does not include attribute data of the road information;

the second detection submodule is configured to determine that the second road information is newly added road information when the first traffic network is updated if the first attribute data does not include attribute data of the road information and the second attribute data includes attribute data of the road information;

the third detection submodule is configured to determine whether the first road information and the second road information are consistent if the first attribute data and the second attribute data both include attribute data of the road information.

14. The apparatus of claim 13, wherein the first and second electrodes are disposed in a substantially cylindrical configuration,

the third detection submodule is specifically configured to, if the first attribute data and the second attribute data both include attribute data of the road information, determine whether an identifier of the first road information and an identifier of the second road information are consistent; and if the identifier of the first road information is consistent with the identifier of the second road information, determining that the first road information is consistent with the second road information.

15. The apparatus of claim 14, the second detection module further comprising a fourth detection submodule and a fifth detection submodule;

the fourth detection submodule is configured to determine, if the identifier of the first road information is not consistent with the identifier of the second road information, a mapping relationship between the identifier of the first road information and the identifier of the second road information;

the fifth detection submodule is configured to add the mapping relationship to the second road information.

16. The apparatus according to any one of claims 11-15, said road information comprising a branch, said attribute data of the branch comprising attribute data of a road segment closest to a target intersection, said target intersection being the intersection closest to the branch in a physical direction of the branch.

17. The apparatus of claim 16, the road information further comprising a lane, the attribute data of the lane comprising attribute data of a target branch to which the lane belongs, a lane identification of the lane in the target branch, and steering information of the lane.

18. The apparatus according to any one of claims 11-17, wherein the road information further comprises a segment, and the attribute data of the segment comprises attribute data of two intersections corresponding to the segment from head to tail.

19. The apparatus according to any one of claims 11 to 18, wherein the road information further includes a trunk line, and the attribute data of the trunk line includes attribute data of two intersections to which the trunk line corresponds end to end, and attribute data of all intersections through which the trunk line passes.

20. The apparatus according to any one of claims 11 to 19, the road information further comprising an area, the attribute data of the area including attribute data of all intersections within the area, attribute data of segments, and attribute data of trunks.

21. An electronic device, comprising:

at least one processor; and

said memory storing instructions executable by said at least one processor to enable said at least one processor to perform a method of detection of traffic network according to any one of claims 1-10.

22. A non-transitory computer readable storage medium storing computer instructions for causing said computer to perform the method of detection of traffic network according to any one of claims 1-10.

23. A computer program product comprising a computer program which, when being executed by a processor, carries out the steps of the method for detection of a traffic network according to any one of claims 1-10.