CN113155141A - Map generation method and device, electronic equipment and storage medium - Google Patents
Map generation method and device, electronic equipment and storage medium Download PDFInfo
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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Abstract
The disclosure discloses a map generation method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent transportation, in particular to the technical field of map data fusion. The specific implementation scheme is as follows: acquiring a first map data set and a second map data set, wherein the first map data set comprises first track point coordinates corresponding to each lane, and the second map data set comprises second track point coordinates corresponding to each road and road information; determining a road matched with each lane according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road; and generating a fused map data set based on the coordinates of each first track point corresponding to each lane and road information corresponding to the road matched with each lane. By the scheme, the fusion map data set containing the lane and the road information of the road can be obtained, and map data are enriched.
Description
Technical Field
The present disclosure relates to the field of intelligent transportation technologies, and in particular, to a map data fusion technology, and a method and an apparatus for generating a map, an electronic device, and a storage medium.
Background
With the support of intelligent transportation from map data, maps need to connect all road elements sufficiently in order for each traveler to obtain a concomitant travel service.
However, at present, high-precision maps used in intelligent transportation contain a small amount of map data, and cannot provide rich map element information.
Disclosure of Invention
The disclosure provides a map generation method and device, electronic equipment and a storage medium.
According to an aspect of the present disclosure, there is provided a map generation method including:
acquiring a first map data set and a second map data set, wherein the first map data set comprises coordinates of each first track point corresponding to each lane, and the second map data set comprises coordinates of each second track point corresponding to each road and road information;
determining a road matched with each lane according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road;
and generating a fused map data set based on the coordinates of each first track point corresponding to each lane and road information corresponding to the road matched with each lane.
According to another aspect of the present disclosure, there is provided a map generation apparatus including:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a first map data set and a second map data set, the first map data set comprises each first track point coordinate corresponding to each lane, and the second map data set comprises each second track point coordinate corresponding to each road and road information;
the matching module is used for determining a road matched with each lane according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road;
and the generating module is used for generating a fused map data set based on the coordinates of each first track point corresponding to each lane and road information corresponding to the road matched with each lane.
According to another aspect of the present disclosure, there is provided an electronic device including:
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 a method of generating a map as described in an embodiment of the above aspect.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a map generation method as described in an embodiment of the above aspect.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method of generating a map as described in an embodiment of an aspect above.
The map generation method, the map generation device, the electronic equipment and the storage medium have at least the following technical effects:
the map data set after fusion is generated by acquiring the first map data set and the second map data set, determining the road matched with each lane according to the matching degree between the first track point coordinates of each lane in the first map data set and the second track point coordinates of each road in the second map data set, and further based on the first track point coordinates corresponding to each lane and the road information corresponding to the road matched with each lane, thereby obtaining the fusion map data set which contains the lane and the road information of the road, and enriching the map data.
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 flowchart illustrating a method of generating a map according to a first embodiment of the present disclosure;
fig. 2 is a flowchart illustrating a method of generating a map according to a second embodiment of the present disclosure;
FIG. 3 is a diagram illustrating an example of a position relationship between each first track point corresponding to a lane and a candidate road;
FIG. 4 is a schematic diagram of a map generation apparatus according to a third embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a map generation apparatus according to a fourth embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a map generation apparatus according to a fifth embodiment of the present disclosure;
fig. 7 is a block diagram of an electronic device to implement the map generation method of the 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.
At present, a high-precision map used in intelligent transportation contains less map data, only can provide information at lane level but cannot provide rich map element information, and a large amount of road information (such as speed limit information, road names, interest points and the like) needs to be collected to manufacture the high-precision map, so that the workload of manufacturing the high-precision map is very large, the online period of the high-precision map is long, and the manufacturing efficiency is low.
Compared with a high-precision map, the traditional navigation map can provide road information at a road level, and in order to expand map information and enrich map data application, the high-precision map road network can be bound to a common navigation map road network and data fusion is carried out.
In the related art, the binding and fusion of the map data are mainly realized through two modes. Firstly, data fusion is carried out manually, and the method is low in efficiency; and secondly, data fusion is carried out in a manual and machine mode, the high-precision map and the navigation map are bound manually, and then the data fusion is realized through an algorithm.
In order to solve the above problems, the present disclosure provides a map generation method, an apparatus, an electronic device, and a storage medium, wherein a first map data set and a second map data set are obtained, a road matching each lane is determined according to a matching degree between each first track point coordinate of each lane in the first map data set and each second track point coordinate of each road in the second map data set, and a fused map data set is generated based on each first track point coordinate corresponding to each lane and road information corresponding to a road matching each lane, thereby obtaining a fused map data set including both lanes and road information of the road, enriching map data, and obtaining road information corresponding to the road by matching the lanes and the roads and performing map data fusion with the lane information without manually collecting the road information, and the lanes and the roads do not need to be bound manually, so that the generation difficulty of the high-precision map is reduced, the automatic matching of the map network and the automatic processing of map data fusion are realized, and the map generation efficiency is improved.
The following describes a method and an apparatus for generating a map, an electronic device, and a storage medium provided in an embodiment of the disclosure in detail with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a method for generating a map according to a first embodiment of the present disclosure, where as shown in fig. 1, the method for generating a map may include the following steps:
The road information may include, but is not limited to, a road name, speed limit information, a Point of interest (POI), and the like.
In the embodiment of the present disclosure, the first map data set and the second map data set may be maps containing different map information, for example, the first map data set may be a high-precision map providing lane level information, and the second map data set may be a navigation map providing road level information.
And 102, determining the road matched with each lane according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road.
In the embodiment of the present disclosure, for each lane in the first map data set, it needs to be matched with a road in the second map data set, and a road matched with each lane is obtained according to a matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road.
As an example, the matching degree can be represented by a distance between the first track point and the corresponding second track point, and the closer the distance, the higher the matching degree between the first track point and the corresponding second track point. The distance between the first track point and the second track point can be obtained by calculating the Euclidean distance according to the coordinate of the first track point and the coordinate of the corresponding second track point. And for any lane in the first map data set, calculating the distance between each first track point coordinate corresponding to the lane and each second track point coordinate corresponding to each road, and then determining the road matched with the lane according to the calculated distance. For example, a distance threshold value may be preset, the number of target second track points, in the second track point coordinates corresponding to each road, where the distance between the first track point coordinates corresponding to the lane is not less than the preset distance threshold value is counted, and the road containing the largest number of target second track points is determined as the road matched with the lane.
As another example, the matching degree may be represented by transition probability and emission probability from each first track point corresponding to the lane to each road, and according to the transition probability and the emission probability from each first track point corresponding to the lane to each road, the matching degree between the lane and each road is determined, and then according to the matching degree, the road matched with the lane is determined. It should be noted that this manner will be described in detail in the following embodiments, and will not be described herein.
And 103, generating a fused map data set based on the coordinates of the first track points corresponding to each lane and the road information corresponding to the road matched with each lane.
In the embodiment of the disclosure, after the roads matched with the lanes in the first map data set are determined, the road information of the roads matched with the lanes can be acquired from the second map data set, and then, based on the coordinates of the first track points corresponding to each lane and the road information corresponding to the roads matched with each lane, the coordinates of the first track points corresponding to the lanes and the road information corresponding to the roads matched with each lane are stored in an associated manner, so that the fused map data set is generated. Therefore, richer map information can be obtained based on the application of the fused map data set, and the map information not only comprises lane-level information, but also comprises road names, POIs, speed limit information and the like.
The map generation method of the disclosed embodiment includes obtaining a first map data set including first track point coordinates corresponding to each lane, and a second map data set including second track point coordinates corresponding to each road and road information, determining a road matching each lane according to a matching degree between the first track point coordinates corresponding to each lane and the second track point coordinates corresponding to each road, generating a fused map data set based on the first track point coordinates corresponding to each lane and the road information corresponding to the road matching each lane, thereby obtaining a fused map data set including both the lane and the road information of the road, enriching the map data, and obtaining the road information corresponding to the road and the lane information for map data fusion by matching the lane and the road, the method has the advantages that the road information does not need to be collected manually, the lane and the road do not need to be bound manually, the generation difficulty of the high-precision map is reduced, automatic matching of the map network and automatic processing of map data fusion are realized, and the map generation efficiency is improved.
In a possible implementation manner of the embodiment of the present disclosure, the matching degree may be represented by transition probabilities and emission probabilities from each first trace point corresponding to a lane to each road, and the road matched with each lane is determined according to the transition probability matrix and the emission probability matrix corresponding to each road by calculating the transition probability matrix and the emission probability matrix of each road. This process is described in detail below with reference to fig. 2.
Fig. 2 is a flowchart of a method for generating a map according to a second embodiment of the present disclosure, and as shown in fig. 2, on the basis of the embodiment shown in fig. 1, step 102 may include the following steps:
In the embodiment of the present disclosure, for any lane in the first map data set, multiple candidate roads corresponding to the lane may be acquired from the second map data set, and second track point coordinates corresponding to each candidate road may be acquired.
When a plurality of candidate roads corresponding to any lane are acquired, the candidate roads may be acquired in different manners, which will be described below by way of example.
As an example, the second track point coordinates of each road may be compared with the first track point coordinates corresponding to any lane, the number of second track points whose second track point coordinates corresponding to each road are the same as the first track point coordinates corresponding to any lane is counted, the roads are arranged in the descending order of the number, and the first n roads are selected as candidate roads. Wherein n is a positive integer, and the value of n can be preset.
As an example, the distance between each second track point coordinate of each road and each first track point coordinate corresponding to any lane may be calculated, the ratio of the number of second track points whose distance reaches a preset distance threshold to the total number of second track points corresponding to the road is calculated for each road, and the road whose ratio reaches a preset value is determined as a candidate road.
As an example, a plurality of candidate roads in the vicinity of any of the lanes may be searched from the second map data set based on the spatial index. The candidate roads are obtained through the spatial index, and the searching efficiency of the candidate roads can be effectively improved.
It should be noted that the above manner of acquiring multiple candidate roads is only used as an example to explain the present disclosure, and should not be taken as a limitation to the present disclosure, and other schemes capable of acquiring candidate roads besides the above manner should also belong to the content of the present disclosure.
In the embodiment of the present disclosure, after obtaining a plurality of candidate roads corresponding to any lane from the second map data set, the coordinates of each second track point corresponding to each candidate road may be further obtained from the second map data set.
In the embodiment of the present disclosure, for each candidate road, a transition probability matrix and an emission probability matrix corresponding to the candidate road may be calculated according to each first track point coordinate corresponding to any lane and each second track point coordinate corresponding to the candidate road. Each element in the transition probability matrix represents the transition probability from each first track point corresponding to the lane to the candidate road; and each element in the emission probability matrix represents the emission probability from each first track point corresponding to the lane to the candidate road. That is, the number of elements of the transition probability matrix and the emission probability matrix is determined by the number of the first track points corresponding to any lane.
In a possible implementation manner of the embodiment of the present disclosure, when calculating the transition probability matrix corresponding to each candidate road, first, according to coordinates of each first track point on any lane, a first track length between every two adjacent first track points is calculated, then, according to coordinates of each second track point corresponding to each candidate road, coordinates of each projection point on each candidate road, which respectively corresponds to each first track point on any lane, are determined, according to coordinates of each projection point on each candidate road, a second track length between every two adjacent projection points on each candidate road is determined, and then, according to a ratio between each first track length corresponding to any lane and each second track length corresponding to each candidate road, the transition probability matrix corresponding to each candidate road is determined. When the emission probability matrix corresponding to each candidate road is calculated, the distance between each first track point on any lane and the corresponding projection point may be determined, the gaussian distribution corresponding to each candidate road may be determined according to the distance between each first track point on any lane and the corresponding projection point, and the emission probability matrix corresponding to each candidate road may be determined according to the gaussian distribution corresponding to each candidate road.
The coordinates of each projection point corresponding to each first track point on any lane on each candidate road can be determined according to the coordinates of each second track point corresponding to each candidate road. For each first track point on any lane, a perpendicular line is drawn from the first track point to a candidate road, the intersection point of the perpendicular line and the candidate road is the projection point of the first track point on the candidate road, if the projection point overlaps with a certain second track point of the candidate road, the coordinate of the second track point is the coordinate of the projection point, if the projection point falls between two second track points, the coordinate of the projection point can be determined according to the coordinates of the two second track points, for example, the average value of the coordinates of the two second track points can be determined as the coordinate of the projection point, or the coordinate of the second track point with a short distance can be determined as the coordinate of the projection point, and so on.
It can be understood that, each element in the transition probability matrix corresponding to each candidate road determined is a ratio between the length of the first track point and the length of the corresponding second track point. For example, fig. 3 is an exemplary diagram of a position relationship between each first track point corresponding to a lane and a candidate road. As shown in fig. 3, if the point a is a projection point corresponding to the first track point 01, and the point B is a projection point corresponding to the first track point 02, then the ratio between the length of the first track point between 01 and 02 and the length of the second track point between a and B is an element in the transition probability matrix corresponding to the candidate road R1.
It should be noted that the transition probability can also be expressed by curvature, angle, etc., and the embodiment of the disclosure only uses the ratio between the track lengths as the transition probability to illustrate the disclosure, but not to limit the disclosure.
In the embodiment of the disclosure, by calculating the first track length between every two adjacent first track points and calculating the second track length between every two adjacent projection points on each candidate road, determining a transition probability matrix corresponding to each candidate road according to the ratio of each first track length corresponding to any lane to each second track length corresponding to each candidate road, determining the corresponding Gaussian distribution of each candidate road according to the distance between each first track point and the corresponding projection point on any lane, further determining the emission probability matrix corresponding to each candidate road according to the Gaussian distribution corresponding to each candidate road, thus, the similarity between the lane and the candidate road is reflected by the transition probability matrix, the proximity between the lane and the candidate road is reflected by the emission probability matrix, and conditions are provided for determining the road matched with the lane according to the transition probability matrix and the emission probability matrix.
And step 203, determining a target road matched with any lane from each candidate road according to the transition probability matrix and the emission probability matrix corresponding to each candidate road.
As a possible implementation manner, determining a target road matched with any lane from each candidate road according to a transition probability matrix and an emission probability matrix corresponding to each candidate road includes: acquiring a first transition probability and a first emission probability corresponding to each first track point from the transition probability matrix and the emission probability matrix of each candidate road; determining a first similarity value corresponding to each first track point according to the product of the first transfer probability and the first transmission probability corresponding to each first track point; determining a second similarity value corresponding to each candidate road according to each first similarity value corresponding to each first track point in each candidate road; and determining the candidate road corresponding to the maximum second similarity value as the target road.
When the second similarity value corresponding to each candidate road is obtained, the first similarity values of the first track points corresponding to the same candidate road can be added to obtain the second similarity value of the candidate road; alternatively, the largest first similarity value among the first similarity values of the first track points corresponding to the same candidate road may be determined as the second similarity value of the candidate road, which is not limited by the present disclosure.
The method comprises the steps of obtaining a first transition probability and a first transmission probability corresponding to each first track point from a transition probability matrix and a transmission probability matrix of each candidate road, determining a first similarity value corresponding to each first track point according to the product of the first transition probability and the first transmission probability corresponding to each first track point, determining a second similarity value corresponding to each candidate road according to each first similarity value corresponding to each first track point in each candidate road, and determining the candidate road corresponding to the largest second similarity value as a target road.
As a possible implementation manner, determining a target road matched with any lane from each candidate road according to a transition probability matrix and an emission probability matrix corresponding to each candidate road includes: determining a first candidate road corresponding to the maximum transition probability according to each transition probability in the transition probability matrix of each candidate road; determining a second candidate road corresponding to the maximum emission probability according to each emission probability in the emission probability matrix of each candidate road; in the case where the first candidate road is the same as the second candidate road, the first candidate road is determined as the target road.
The transition probability represents the similarity between a lane and a road, the emission probability represents the proximity between the lane and the road, a first candidate road determined according to the maximum transition probability is the road most similar to the lane, a second candidate road determined according to the maximum emission probability is the road most close to the lane, and when the first candidate road and the second candidate road are the same road, the road is the target road most matched with the lane, so that the automatic binding of the lane and the road is realized, and the accuracy of matching between the lane and the road is improved.
Further, in a possible implementation manner of the embodiment of the present disclosure, in a case that the first candidate road is different from the second candidate road, a third similarity value corresponding to each first trajectory point is determined, where the third similarity value is a product of a transition probability and an emission probability corresponding to each first trajectory point, which are obtained from the transition probability matrix and the emission probability matrix of the first candidate road; determining a fourth similarity value corresponding to each first track point, wherein the fourth similarity value is the product of the transition probability and the emission probability corresponding to each first track point, which are obtained from the transition probability matrix and the emission probability matrix of the second candidate road; determining a maximum similarity value from each third similarity value and each fourth similarity value; and determining the candidate road corresponding to the maximum similarity value as the target road.
When the first candidate road and the second candidate road are not the same road, calculating the product of the transition probability and the emission probability corresponding to each first track point of the first candidate road to obtain a plurality of third similarity values, calculating the product of the transition probability and the emission probability corresponding to each first track point of the second candidate road to obtain a plurality of fourth similarity values, further comparing each third similarity value with each fourth similarity value, selecting the maximum similarity value from the fourth similarity values, and determining the candidate road corresponding to the maximum similarity value as the target road. Therefore, only two candidate roads need to be subjected to similarity value calculation, the calculation amount is reduced, and the speed and the efficiency of matching the lane and the road are improved.
According to the map generation method, the multiple candidate roads corresponding to any lane and the second track point coordinates corresponding to each candidate road are obtained from the second map data set, the transition probability matrix and the emission probability matrix corresponding to each candidate road are calculated according to the first track point coordinates corresponding to any lane and the second track point coordinates corresponding to each candidate road, and then the target road matched with any lane is determined from each candidate road according to the transition probability matrix and the emission probability matrix corresponding to each candidate road.
Because product lines, coordinate systems and the like adopted by different map data sets are usually inconsistent, in order to facilitate road network binding and improve the accuracy of road network binding, in a possible implementation manner of the embodiment of the disclosure, before determining roads matched with each lane, the first map data set and the second map data set can be migrated to the same coordinate system according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road. By unifying the first map data set and the second map data set into a coordinate system, convenience is provided for calling data when a subsequent lane is matched with a road, and the accuracy of matching the lane with the road is improved.
In order to realize the above embodiments, the present disclosure further provides a map generation apparatus. Fig. 4 is a schematic structural diagram of a map generation apparatus provided according to a third embodiment of the present disclosure, and as shown in fig. 4, the map generation apparatus 40 includes: an acquisition module 410, a matching module 420, and a generation module 430.
The obtaining module 410 is configured to obtain a first map data set and a second map data set, where the first map data set includes first track point coordinates corresponding to each lane, and the second map data set includes second track point coordinates corresponding to each road and road information.
In a possible implementation manner of the embodiment of the present disclosure, the first map data set is a high-precision map, and the second map data set is a navigation map.
And the matching module 420 is configured to determine a road matched with each lane according to a matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road.
And the generating module 430 is configured to generate a fused map data set based on the coordinates of each first track point corresponding to each lane and road information corresponding to a road matched with each lane.
Further, in a possible implementation manner of the embodiment of the present disclosure, as shown in fig. 5, on the basis of the embodiment shown in fig. 4, the matching module 420 includes:
the obtaining unit 421 is configured to obtain, from the second map data set, multiple candidate roads corresponding to any one of the lanes and second track point coordinates corresponding to each of the candidate roads.
And the calculating unit 422 is configured to calculate a transition probability matrix and an emission probability matrix corresponding to each candidate road according to each first track point coordinate corresponding to any lane and each second track point coordinate corresponding to each candidate road.
In a possible implementation manner of the embodiment of the present disclosure, the calculating unit 422 is specifically configured to: calculating a first track length between every two adjacent first track points according to the coordinates of the first track points on any lane; determining coordinates of projection points on each candidate road, which correspond to the first track points on any lane respectively, according to the coordinates of the second track points corresponding to each candidate road; determining a second track length between every two adjacent projection points on each candidate road according to the coordinates of each projection point on each candidate road; determining a transition probability matrix corresponding to each candidate road according to the ratio of each first track length corresponding to any lane to each second track length corresponding to each candidate road; determining the distance between each first track point and the corresponding projection point on any lane; determining the Gaussian distribution corresponding to each candidate road according to the distance between each first track point on any lane and the corresponding projection point; and determining the emission probability matrix corresponding to each candidate road according to the Gaussian distribution corresponding to each candidate road.
And a matching unit 423, configured to determine, according to the transition probability matrix and the emission probability matrix corresponding to each candidate road, a target road matched with the any lane from the candidate roads.
In a possible implementation manner of the embodiment of the present disclosure, the matching unit 423 is specifically configured to: acquiring a first transition probability and a first emission probability corresponding to each first track point from the transition probability matrix and the emission probability matrix of each candidate road; determining a first similarity value corresponding to each first track point according to the product of the first transfer probability and the first transmission probability corresponding to each first track point; determining a second similarity value corresponding to each candidate road according to each first similarity value corresponding to each first track point in each candidate road; and determining the candidate road corresponding to the maximum second similarity value as the target road.
In a possible implementation manner of the embodiment of the present disclosure, the matching unit 423 is specifically configured to: determining a first candidate road corresponding to the maximum transition probability according to each transition probability in the transition probability matrix of each candidate road; determining a second candidate road corresponding to the maximum emission probability according to each emission probability in the emission probability matrix of each candidate road; determining the first candidate road as the target road if the first candidate road is the same as the second candidate road.
Further, in a possible implementation manner of the embodiment of the present disclosure, the matching unit 423 is specifically further configured to: under the condition that the first candidate road is different from the second candidate road, determining a third similarity value corresponding to each first track point, wherein the third similarity value is the product of the transition probability and the emission probability corresponding to each first track point, which are obtained from the transition probability matrix and the emission probability matrix of the first candidate road; determining a fourth similarity value corresponding to each first track point, wherein the fourth similarity value is the product of the transition probability and the emission probability corresponding to each first track point, which are obtained from the transition probability matrix and the emission probability matrix of the second candidate road; determining a maximum similarity value from each third similarity value and each fourth similarity value; and determining the candidate road corresponding to the maximum similarity value as the target road.
In a possible implementation manner of the embodiment of the present disclosure, as shown in fig. 6, on the basis of the embodiment shown in fig. 4, the generating device 40 of the map further includes:
a preprocessing module 400, configured to migrate the first map data set and the second map data set to a same coordinate system.
It should be noted that the foregoing explanation of the embodiment of the map generation method is also applicable to the map generation apparatus of this embodiment, and the implementation principle thereof is similar and will not be described herein again.
The map generation device of the disclosed embodiment determines roads matched with each lane according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road and road information by acquiring a first map data set containing each first track point coordinate corresponding to each lane and a second map data set containing each second track point coordinate corresponding to each road and road information, generates a fused map data set based on each first track point coordinate corresponding to each lane and road information corresponding to each lane, thereby obtaining a fused map data set containing both lanes and road information of roads, enriching map data, and acquiring road information corresponding to roads and lane information for map data fusion by matching lanes and roads, the method has the advantages that the road information does not need to be collected manually, the lane and the road do not need to be bound manually, the generation difficulty of the high-precision map is reduced, automatic matching of the map network and automatic processing of map data fusion are realized, and the map generation efficiency is improved.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 7 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments 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. 7, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes in accordance with a computer program stored in a Read-Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM703 are connected to each other by a bus 704. An Input/Output (I/O) interface 705 is also connected to the bus 704.
A number of components in the electronic device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, Integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated circuits (ASICs), Application Specific Standard Products (ASSPs), System On 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 map generation 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 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 Cathode Ray Tube (CRT) or Liquid Crystal Display (LCD) 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), the internet, and blockchain networks.
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 may be a cloud Server, which is 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 a conventional physical host and a VPS (Virtual Private Server). The server may also be a server of a distributed system, or a server incorporating a blockchain.
In order to implement the above embodiments, the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the map generation method as described in the foregoing embodiments.
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 (19)
1. A map generation method comprises the following steps:
acquiring a first map data set and a second map data set, wherein the first map data set comprises coordinates of each first track point corresponding to each lane, and the second map data set comprises coordinates of each second track point corresponding to each road and road information;
determining a road matched with each lane according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road;
and generating a fused map data set based on the coordinates of each first track point corresponding to each lane and road information corresponding to the road matched with each lane.
2. The method of claim 1, wherein the determining the road matched with each lane according to the matching degree between the first track point coordinates corresponding to each lane and the second track point coordinates corresponding to each road comprises:
acquiring a plurality of candidate roads corresponding to any lane and second track point coordinates corresponding to each candidate road from the second map data set;
calculating a transition probability matrix and an emission probability matrix corresponding to each candidate road according to each first track point coordinate corresponding to any lane and each second track point coordinate corresponding to each candidate road;
and determining a target road matched with any lane from the candidate roads according to the transition probability matrix and the emission probability matrix corresponding to each candidate road.
3. The method according to claim 2, wherein the calculating the transition probability matrix and the emission probability matrix corresponding to each of the candidate roads according to the first track point coordinates corresponding to any one of the lanes and the second track point coordinates corresponding to each of the candidate roads includes:
calculating a first track length between every two adjacent first track points according to the coordinates of the first track points on any lane;
determining coordinates of projection points on each candidate road, which correspond to the first track points on any lane respectively, according to the coordinates of the second track points corresponding to each candidate road;
determining a second track length between every two adjacent projection points on each candidate road according to the coordinates of each projection point on each candidate road;
determining a transition probability matrix corresponding to each candidate road according to the ratio of each first track length corresponding to any lane to each second track length corresponding to each candidate road;
determining the distance between each first track point and the corresponding projection point on any lane;
determining the Gaussian distribution corresponding to each candidate road according to the distance between each first track point on any lane and the corresponding projection point;
and determining the emission probability matrix corresponding to each candidate road according to the Gaussian distribution corresponding to each candidate road.
4. The method of claim 3, wherein the determining the target road matching with the any lane from the candidate roads according to the transition probability matrix and the transmission probability matrix corresponding to each candidate road comprises:
acquiring a first transition probability and a first emission probability corresponding to each first track point from the transition probability matrix and the emission probability matrix of each candidate road;
determining a first similarity value corresponding to each first track point according to the product of the first transfer probability and the first transmission probability corresponding to each first track point;
determining a second similarity value corresponding to each candidate road according to each first similarity value corresponding to each first track point in each candidate road;
and determining the candidate road corresponding to the maximum second similarity value as the target road.
5. The method of claim 3, wherein the determining the target road matching with the any lane from the candidate roads according to the transition probability matrix and the transmission probability matrix corresponding to each candidate road comprises:
determining a first candidate road corresponding to the maximum transition probability according to each transition probability in the transition probability matrix of each candidate road;
determining a second candidate road corresponding to the maximum emission probability according to each emission probability in the emission probability matrix of each candidate road;
determining the first candidate road as the target road if the first candidate road is the same as the second candidate road.
6. The method of claim 5, further comprising:
under the condition that the first candidate road is different from the second candidate road, determining a third similarity value corresponding to each first track point, wherein the third similarity value is the product of the transition probability and the emission probability corresponding to each first track point, which are obtained from the transition probability matrix and the emission probability matrix of the first candidate road;
determining a fourth similarity value corresponding to each first track point, wherein the fourth similarity value is the product of the transition probability and the emission probability corresponding to each first track point, which are obtained from the transition probability matrix and the emission probability matrix of the second candidate road;
determining a maximum similarity value from each third similarity value and each fourth similarity value;
and determining the candidate road corresponding to the maximum similarity value as the target road.
7. The method according to any one of claims 1 to 6, wherein before determining the road matching each of the lanes according to the matching degree between the first track point coordinates corresponding to each of the lanes and the second track point coordinates corresponding to each of the roads, the method further comprises:
migrating the first map data set and the second map data set to the same coordinate system.
8. The method of any of claims 1-6, wherein the first set of map data is a high precision map and the second set of map data is a navigational map.
9. A map generation apparatus comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a first map data set and a second map data set, the first map data set comprises each first track point coordinate corresponding to each lane, and the second map data set comprises each second track point coordinate corresponding to each road and road information;
the matching module is used for determining a road matched with each lane according to the matching degree between each first track point coordinate corresponding to each lane and each second track point coordinate corresponding to each road;
and the generating module is used for generating a fused map data set based on the coordinates of each first track point corresponding to each lane and road information corresponding to the road matched with each lane.
10. The apparatus of claim 9, wherein the matching module comprises:
the acquisition unit is used for acquiring a plurality of candidate roads corresponding to any lane and each second track point coordinate corresponding to each candidate road from the second map data set;
the calculation unit is used for calculating a transition probability matrix and an emission probability matrix corresponding to each candidate road according to each first track point coordinate corresponding to any lane and each second track point coordinate corresponding to each candidate road;
and the matching unit is used for determining a target road matched with any lane from the candidate roads according to the transition probability matrix and the emission probability matrix corresponding to each candidate road.
11. The apparatus of claim 10, wherein the computing unit is specifically configured to:
calculating a first track length between every two adjacent first track points according to the coordinates of the first track points on any lane;
determining coordinates of projection points on each candidate road, which correspond to the first track points on any lane respectively, according to the coordinates of the second track points corresponding to each candidate road;
determining a second track length between every two adjacent projection points on each candidate road according to the coordinates of each projection point on each candidate road;
determining a transition probability matrix corresponding to each candidate road according to the ratio of each first track length corresponding to any lane to each second track length corresponding to each candidate road;
determining the distance between each first track point and the corresponding projection point on any lane;
determining the Gaussian distribution corresponding to each candidate road according to the distance between each first track point on any lane and the corresponding projection point;
and determining the emission probability matrix corresponding to each candidate road according to the Gaussian distribution corresponding to each candidate road.
12. The apparatus of claim 11, wherein the matching unit is specifically configured to:
acquiring a first transition probability and a first emission probability corresponding to each first track point from the transition probability matrix and the emission probability matrix of each candidate road;
determining a first similarity value corresponding to each first track point according to the product of the first transfer probability and the first transmission probability corresponding to each first track point;
determining a second similarity value corresponding to each candidate road according to each first similarity value corresponding to each first track point in each candidate road;
and determining the candidate road corresponding to the maximum second similarity value as the target road.
13. The apparatus of claim 11, wherein the matching unit is specifically configured to:
determining a first candidate road corresponding to the maximum transition probability according to each transition probability in the transition probability matrix of each candidate road;
determining a second candidate road corresponding to the maximum emission probability according to each emission probability in the emission probability matrix of each candidate road;
determining the first candidate road as the target road if the first candidate road is the same as the second candidate road.
14. The apparatus of claim 13, wherein the matching unit is further specifically configured to:
under the condition that the first candidate road is different from the second candidate road, determining a third similarity value corresponding to each first track point, wherein the third similarity value is the product of the transition probability and the emission probability corresponding to each first track point, which are obtained from the transition probability matrix and the emission probability matrix of the first candidate road;
determining a fourth similarity value corresponding to each first track point, wherein the fourth similarity value is the product of the transition probability and the emission probability corresponding to each first track point, which are obtained from the transition probability matrix and the emission probability matrix of the second candidate road;
determining a maximum similarity value from each third similarity value and each fourth similarity value;
and determining the candidate road corresponding to the maximum similarity value as the target road.
15. The apparatus of any of claims 9-14, further comprising:
the preprocessing module is used for transferring the first map data set and the second map data set to the same coordinate system.
16. The apparatus of any one of claims 9-14, wherein the first set of map data is a high precision map and the second set of map data is a navigational map.
17. An electronic device, comprising:
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 a method of generating a map as claimed in any one of claims 1 to 8.
18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform a method of generating a map according to any one of claims 1-8.
19. A computer program product comprising a computer program which, when executed by a processor, implements a method of generating a map as claimed in any one of claims 1 to 8.
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| CN202110385330.7A CN113155141A (en) | 2021-04-09 | 2021-04-09 | Map generation method and device, electronic equipment and storage medium |
| PCT/CN2021/126211 WO2022213580A1 (en) | 2021-04-09 | 2021-10-25 | Map generation method and apparatus, and electronic device and storage medium |
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Application publication date: 20210723 |