KR20240064140A - Method and apparatus for finding matching routes between different map databases using the minimum distance maximum values - Google Patents

Abstract

A method of obtaining a second path of a second map database that matches a first path of a first map database between different map databases composed of nodes and links is disclosed. The method includes: acquiring coordinate information of a start node of the first path and coordinate information of an end node of the first path; Obtaining a temporary start node and a temporary end node on a second map database based on the obtained coordinate information; Obtaining a bounding box based on the temporary start node, the temporary end node, and a padding distance; determining n1 candidate start nodes within the bounding box based on the distance from the temporary start node and determining n2 candidate end nodes within the bounding box based on the distance from the temporary end node; determining a plurality of candidate paths based on distance information of each of the paths connecting each of the m1 candidate start nodes among the n1 candidate start nodes and each of the m2 candidate end nodes among the n2 candidate end nodes; calculating a minimum distance maximum value for the first path for each of the candidate paths; and acquiring a second route based on the calculated minimum distance and maximum value for the first route. Including, and the maximum value of the minimum distance is:
calculating a minimum value among distance values between nodes included in a candidate path and nodes included in the first path; and calculating the maximum value among the minimum distance values of each of the nodes included in the candidate path (where the calculated maximum value is the maximum minimum distance value for the first path); It can be calculated by a method including.

Description

Method and device for finding matching routes between different map databases using minimum distance maximum value {METHOD AND APPARATUS FOR FINDING MATCHING ROUTES BETWEEN DIFFERENT MAP DATABASES USING THE MINIMUM DISTANCE MAXIMUM VALUES}

The present disclosure is for a method for finding a matching route between different map databases using the minimum distance maximum value.

The country provides domestic road traffic status by matching it with map data. However, when providing services using other map data formats, there is a problem that domestic road traffic conditions cannot be reflected. In particular, when using map data provided overseas, it does not match accurately with map data provided by the country. As a result, companies that provide services using overseas map data have difficulty providing domestic road traffic status. there was.

To solve this problem, methods for finding exactly matching routes between different map data are being studied.

Registered Patent No. 1538042 provides a method for converting paths between different data formats (2D and 3D).

This disclosure was created in response to the above-described background technology, and seeks to provide a method of finding a path in a second map database that matches a path in a first map database between different map databases.

In order to solve the above-described problem, in a method of obtaining a second path of a second map database that matches the first path, which is a path of a first map database, between different map databases composed of nodes and links, , acquiring coordinate information of the start node of the first path and coordinate information of the end node of the first path; Obtaining a temporary start node and a temporary end node on a second map database based on the obtained coordinate information; Obtaining a bounding box based on the temporary start node, the temporary end node, and a padding distance; determining n1 candidate start nodes within the bounding box based on the distance from the temporary start node and determining n2 candidate end nodes within the bounding box based on the distance from the temporary end node; determining a plurality of candidate paths based on distance information of each of the paths connecting each of the m1 candidate start nodes among the n1 candidate start nodes and each of the m2 candidate end nodes among the n2 candidate end nodes; calculating a minimum distance maximum value for the first path for each of the candidate paths; and acquiring a second route based on the calculated minimum distance and maximum value for the first route. The maximum value of the minimum distance includes: calculating the minimum value among the distance values between the nodes included in the candidate path and the nodes included in the first path; and calculating the maximum value among the minimum distance values of each of the nodes included in the candidate path (where the calculated maximum value is the maximum minimum distance value for the first path); It is possible to provide a method of obtaining a second path of a second database that matches the first path, which is the path of the first database, calculated by a method including.

In addition, the step of determining the plurality of candidate paths includes: obtaining K candidate paths by calculating the distances of each of the paths connecting the candidate start node and the candidate end node and selecting K in descending order of the calculated distances. By performing the candidate path search method on each of m1 candidate start nodes and m2 candidate end nodes, K*m1*m2 candidate paths can be obtained.

Additionally, n1 and n2 may be the same number, and m1 and m2 may be the same number.

Additionally, n1, n2, m1, and m2 may all be the same number.

Additionally, according to claim 1, in the step of acquiring the second path, a candidate path having the smallest minimum distance maximum value with respect to the first path among candidate paths may be acquired as the second path.

Additionally, determining n1 candidate start nodes within the bounding box based on the distance from the temporary start node and determining n2 candidate end nodes within the bounding box based on the distance from the temporary end node. Determines n1 candidate start nodes by selecting n1 nodes in the order of shortest straight-line distance to the temporary start node within the bounding box, and n2 candidate start nodes in order of shortline distance to the temporary end node within the bounding box. By selecting nodes, n2 candidate starting nodes can be determined.

Additionally, the coordinate information may be information about latitude and longitude coordinates.

A computer program stored in a computer-readable storage medium for solving the above-described problem, wherein the computer program configures one or more processors to: a first path, which is a path of a first map database, between different map databases composed of nodes and links; It includes commands for obtaining a second route of a matching second map database, wherein the commands include coordinate information of a start node of the first route and coordinates of an end node of the first route. Obtaining information; Obtaining a temporary start node and a temporary end node on a second map database based on the obtained coordinate information; Obtaining a bounding box based on the temporary start node, the temporary end node, and a padding distance; determining n1 candidate start nodes within the bounding box based on the distance from the temporary start node and determining n2 candidate end nodes within the bounding box based on the distance from the temporary end node; determining a plurality of candidate paths based on distance information of each of the paths connecting each of the m1 candidate start nodes among the n1 candidate start nodes and each of the m2 candidate end nodes among the n2 candidate end nodes; calculating the minimum distance maximum value for the first path for each of the candidate paths; and acquiring a second route based on the calculated minimum distance and maximum value for the first route. The maximum value of the minimum distance includes: calculating the minimum value among the distance values between the nodes included in the candidate path and the nodes included in the first path; and calculating the maximum value among the minimum distance values of each of the nodes included in the candidate path (where the calculated maximum value is the minimum distance maximum value for the first path). A computer readable method calculated by a method comprising: A computer program stored on a storage medium may be provided.

The present disclosure can obtain a path in a second map database that exactly matches the path in the first map database.

1 is a diagram for explaining a map database according to an embodiment of the present disclosure.
FIG. 2 is a diagram for explaining a computing device 100 according to an embodiment of the present disclosure.
3 to 7 are diagrams for explaining a method by which the computing device 100 acquires candidate paths for acquiring a second path, according to an embodiment of the present disclosure.
FIG. 8 is a diagram illustrating a method for acquiring a second path according to an embodiment of the present disclosure.
Figure 9 is a diagram for explaining a method of obtaining a second path according to another embodiment of the present disclosure.
Figure 10 is a diagram for explaining a method of determining a bounding box for a candidate path according to an embodiment of the present disclosure.
11 and 13 are diagrams for explaining a method of obtaining a second path based on the minimum distance maximum value, according to another embodiment of the present disclosure.
FIG. 14 is a diagram illustrating a method of obtaining a second path based on the number of pixels, according to another embodiment of the present disclosure.
15 and 16 are diagrams for explaining a method of obtaining a second path based on an area between a first path and a candidate path, according to another embodiment of the present disclosure.
FIG. 17 is a diagram illustrating a method by which the computing device 100 predicts the current speed of a target route, according to an embodiment of the present disclosure.
Figure 18 is a diagram for explaining at least one path connected to the target path.
Figure 19 is a diagram for explaining a case where there are multiple paths connected to a target path.
Figure 20 is a general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components may transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “if it contains only A,” “if it contains only B,” or “if it is a combination of A and B.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

1 is a diagram for explaining a map database according to an embodiment of the present disclosure.

According to one embodiment of the present disclosure, a map database may be composed of nodes and links.

Here, the node may represent a place where a change in speed occurs as the vehicle drives on the road. Specifically, a node may represent an intersection, an overpass start and end point, a road start and end point, an underpass start and end point, a tunnel start and end point, an administrative boundary, etc., but is not limited thereto.

Additionally, a link may mean a line connecting a node, which is a point at which a speed change occurs, and may represent a road in reality. Specifically, the link may represent a road, bridge, overpass, underpass, tunnel, etc., but is not limited thereto.

A map database can be constructed differently depending on the constructor. For example, even if a map database is constructed for the same location in Korea, the map data constructed in Korea and the map data constructed in an overseas country may be different.

The Korean government provides traffic volume data by matching it with government-constructed map data. As a result, when providing services using domestic map data constructed by other entities, there is a disadvantage in utilizing traffic volume data, so it is necessary to match map data constructed domestically with map data constructed in foreign countries. There is.

(a) of FIG. 1 shows one path (eg, a first path) consisting of nodes and links in a first map database, according to an embodiment of the present disclosure. The second (b) is data representing the vicinity of the first route in the second map database. Hereinafter, a method of finding a route matching the first route in the second map database is disclosed.

FIG. 2 is a diagram for explaining a computing device 100 according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 2 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include different components for performing the computing environment of the computing device 100, and only some of the disclosed components may configure the computing device 100.

The computing device 100 may include a processor 110, a memory 130, and a network unit 150.

The processor 110 may be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit) may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for learning neural networks, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. Calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, CPU and GPGPU can work together to process learning of network functions and data classification using network functions. Additionally, in one embodiment of the present disclosure, the processors of a plurality of computing devices can be used together to process learning of network functions and data classification using network functions. Additionally, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150.

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, hard disk type, multimedia card micro type, or card type memory (e.g. (e.g. SD or -Only Memory), and may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in connection with web storage that performs a storage function of the memory 130 on the Internet. The description of the memory described above is merely an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( A variety of wired communication systems can be used, such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN).

In addition, the network unit 150 presented in this specification includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and SC-FDMA ( A variety of wireless communication systems can be used, such as Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may be configured regardless of the communication mode, such as wired or wireless, and may be composed of various communication networks such as a personal area network (PAN) and a wide area network (WAN). You can. Additionally, the network may be the well-known World Wide Web (WWW), and may also use wireless transmission technology used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth.

The computing device 100 of the present disclosure can obtain a second path of the second map database that matches the first path, which is the path of the first map database, between different map databases composed of nodes and links. .

3 to 7 are diagrams for explaining a method by which the computing device 100 acquires candidate paths for acquiring a second path, according to an embodiment of the present disclosure.

In step S310, the computing device 100 may obtain coordinate information of the start node of the first path and coordinate information of the end node of the first path.

Referring to FIG. 4, the computing device 100 may acquire data about the route on the first map database. In this case, information about the start node (start point ID) of the route and the end node (end point ID) Information can also be obtained.

The information about the starting node may include coordinate information (e.g., latitude and/or longitude information, etc.) of the starting node, and the information about the ending node may include coordinate information (e.g., latitude and/or longitude information, etc.). information) may be included.

In step S320, the computing device 100 may obtain a temporary start node and a temporary end node on the second map database based on the acquired coordinate information.

For example, the computing device 100 may obtain a temporary start node (TSN) by projecting the coordinate information of the start node of the first path onto the second map database, and the end of the first path. A temporary end node (TEN) can be obtained by projecting the coordinate information of the node onto the second map database. In this case, the temporary start node and temporary end node may be nodes on the second map database.

In step S330, the computing device 100 may obtain a bounding box based on a temporary start node, the temporary end node, and a padding distance.

The bounding box is an arbitrary area for finding the second path and can be implemented as a square.

Referring to FIG. 5A , the computing device 100 may obtain a bounding box by applying the location information of the temporary start node and the temporary end node to a predetermined algorithm. Specifically, the computing device 100 calculates a rectangular box using the coordinate information of the temporary start node and the temporary end node, and adds a distance equal to the padding distance (e.g., D1, D2, D3, D4) to it. By expanding it further, a bounding box for obtaining the second path can be obtained.

According to another embodiment of the present disclosure, when referring to FIG. 5B, the computing device 100 may obtain the bounding box by applying the first path to a predetermined algorithm. Specifically, the computing device 100 may determine a rectangular bounding box based on the first path (a). In addition, the computing device 100 pads the left and right sides of the bounding box as much as the horizontal length (d1) of the determined bounding box and up and down as much as the determined vertical length (d2) of the bounding box, thereby creating a second path. You can finally obtain the bounding box to obtain it.

In step S340, the computing device 100 determines n1 candidate start nodes within the bounding box based on the distance from the temporary start node, and n2 candidate start nodes within the bounding box based on the distance from the temporary end node. Candidate termination nodes can be determined.

For example, the computing device 100 may extract a set of nodes and links on the second map database included in the bounding box.

Additionally, referring to FIG. 6 , the computing device 100 may determine n1 candidate start nodes by selecting n1 (n1 is a natural number) among the nodes in the bounding box in order of distance closest to the temporary start node. In this case, the Euclidean distance can be used to calculate the distance, and the coordinates of the temporary start node can be converted to coordinates projected in meters (e.g., used after EPSG transformation).

Additionally, referring to FIG. 6 , the computing device 100 may determine n2 candidate end nodes by selecting n2 (n2 is a natural number) nodes in the bounding box in order of distance closest to the temporary end node. In this case, the Euclidean distance can be used to calculate the distance, and the coordinates of the temporary end node can be converted to coordinates projected in meters (m) (e.g., used after EPSG transformation). In this case, n1 and n2 may be the same, but are not limited thereto.

In step S350, the computing device 100 selects a plurality of candidates based on the distance information of each of the paths connecting each of the m1 candidate start nodes among the n1 candidate start nodes and each of the m2 candidate end nodes among the n2 candidate end nodes. Paths can be determined.

According to one embodiment of the present disclosure, the computing device 100 determines m1 candidate starting nodes among n1 candidate starting nodes. For example, the computing device 100 may determine m1 candidate start nodes by selecting m1 (m1 is a natural number) among n1 candidate start nodes in order of distance closest to the temporary start node.

Additionally, the computing device 100 determines m2 candidate end nodes among the n2 candidate end nodes. For example, the computing device 100 may determine m2 candidate start nodes by selecting m2 (m2 is a natural number) among n2 candidate end nodes in order of distance closest to the temporary end node. In this case, m1 and m2 may be the same. Additionally, n1, n2, m1, and m2 may all be the same, but are not limited thereto.

There are multiple paths from one of the m1 candidate start nodes to one of the m2 candidate end nodes. The computing device 100 may obtain K candidate paths among a plurality of paths using a first candidate path search method.

Referring to FIG. 7, the computing device 100 calculates the distance of each of the paths connecting the candidate start node and the candidate end node and selects K in the order of the calculated distances to obtain K candidate paths. A candidate path search method can be used. In this case, the calculated distance may be the sum of the lengths of links included in the path. This is because a path is composed of a combination of several small links.

Since the computing device 100 has acquired m1 candidate start nodes and m2 candidate end nodes, by applying the first candidate path search method to the m1 candidate start nodes and m2 candidate end nodes, K*m1* m2 candidate paths can be obtained.

For example, assuming that m1 is 3 (S1, S2, S3) and m2 is 3 (E1, E2, E3), the computing device 100 selects K candidate paths connecting S1 and E1, S1 and K candidate paths connecting E2 can be obtained, and K candidate paths connecting S1 and E3 can be obtained. Additionally, the computing device 100 can obtain K candidate paths connecting S2 and E1, E2, and E3, and K candidate paths connecting S3 and E1, E2, and E3. As a result, the computing device 100 can obtain m1(3)*m2(3)*K candidate paths.

In this case, m1 and m2 may be the same, but are not limited thereto.

FIG. 8 is a diagram illustrating a method for acquiring a second path according to an embodiment of the present disclosure.

According to an exemplary embodiment of the present disclosure, the computing device 100 may obtain a second route by comparing the distance information of each of the candidate routes with the distance information of the first route.

The computing device 100 may calculate distance information for each of K*m1*m2 candidate paths. For example, the computing device 100 may calculate distance information about the candidate path by adding up the distances of all links included in the candidate path. Additionally, distance information can be calculated for each of K*m1*m2 candidate paths.

The computing device 100 may calculate distance information of the first path. For example, the computing device 100 may calculate distance information of the first path by adding up the distances of all links included in the first path.

According to an embodiment of the present disclosure, the computing device 100 may obtain a candidate path having distance information closest to the distance information of the first path as the second path. Specifically, the computing device 100 may calculate the difference between the distance information of each of the candidate paths and the distance information of the first path (S810), and select the candidate path with the smallest calculated difference as the second path. Can be acquired (S802).

Figure 9 is a diagram for explaining a method of obtaining a second path according to another embodiment of the present disclosure. Figure 10 is a diagram for explaining a method of determining a bounding box for a candidate path according to an embodiment of the present disclosure.

According to one embodiment of the present disclosure, the computing device 100 may determine a bounding box of a candidate path. For example, the computing device 100 may calculate the maximum latitude coordinate, maximum longitude coordinate, minimum latitude coordinate, and minimum longitude coordinate for the nodes included in the candidate path and determine the bounding box based on the calculated values. there is. This can be seen in FIG. 10 to illustrate this with drawings. The bounding box for the candidate path may be determined to have a rectangular shape, and the computing device 100 may calculate the area of the bounding box area for the candidate path using the width*height formula. In this case, the computing device 100 may calculate the area of the bounding box area for each of the K*m1*m2 candidate paths (S910).

In step S920, the computing device 100 may obtain the second route by comparing the area of the bounding box area of each of the candidate routes with the area of the bounding box area of the first route.

For example, the computing device 100 may obtain, as the second path, a candidate path having the area of the bounding box area closest to the area of the bounding box area of the first route among the candidate routes. Specifically, the computing device 100 may calculate a difference value between the area of the bounding box area of each of the candidate routes and the area of the bounding box area of the first route, and select the candidate route with the smallest difference value as the second route. It can be obtained with Additionally, the computing device 100 may calculate the ratio between the area of the bounding box area of each of the candidate paths and the area of the bounding box area of the first route, and select the candidate route whose ratio is closest to 1 as the second route. It can be obtained, but is not limited to this.

11 to 13 are diagrams for explaining a method of obtaining a second path based on the minimum distance maximum value, according to another embodiment of the present disclosure.

In step S1110, the computing device 100 may calculate the maximum minimum distance for the first path for each of the candidate paths.

The minimum distance maximum value means the maximum value of the values having the minimum distance among the distances from the node included in the candidate path to the nodes included in the first path.

Specifically, the computing device 100 may calculate the minimum value among the distance values between the nodes included in the candidate path and the nodes included in the first path, and the maximum value among the minimum distance values for each of the nodes included in the candidate path. By calculating the value, the minimum distance and maximum value for the candidate path can be calculated.

Referring to FIG. 12, it is assumed that the first path includes three nodes (S1, S2, and S3) and the candidate path includes four nodes (T1, T2, T3, and T4). The computing device 100 may calculate the distances (D(T1S1), D(T1S2), and D(T1S3)) from T1 to the nodes included in the first path, and calculate the distance with the minimum value among them. can do. In addition, the computing device 100 may calculate the distances (D(T2S1), D(T2S2), and D(T2S3)) from T2 to the nodes included in the first path, and the distance having the minimum value among these can be calculated. In addition, the computing device 100 calculates the distances (D(T3S1), D(T3S2), D(T3S3)) from T3 to the nodes included in the first path, and calculates the distance with the minimum value among them. You can. In addition, the computing device 100 calculates the distances (D(T4S1), D(T4S2), and D(T4S3)) from T4 to the nodes included in the first path, and calculates the distance with the minimum value among them. You can. The result is assumed to be as follows.

A.　　　　 D(T1S1), D(T1S2), D(T1S3) = 7,13,10 -> Minimum value is 7

B.　　　　 D(T2S1), D(T2S2, D(T2S3) = 3,11,7 -> Minimum value is 3

C.　　　　 D(T3S1), D(T3S2), D(T3S3) = 9,21,13 -> Minimum value is 9

D.　　　　 D(T4S1), D(T4S2, D(T4S3) = 6,9,7 -> Minimum value is 6

In this case, since the maximum value of the minimum values is 9, the maximum value of the minimum distance for the first path of the candidate path is 9.

According to another embodiment of the present disclosure, the computing device 100 may calculate the minimum distance maximum value for the first path of the candidate path as follows.

Referring to FIG. 13, it is assumed that the first path includes three nodes (S1, S2, and S3) and the candidate path includes four nodes (T1, T2, T3, and T4). The computing device 100 can calculate the distances (D(S1T1), D(S2T2), D(S1T3), and D(S1T4)) from S1 to the nodes included in the candidate path, the minimum value of which is The distance can be calculated. In addition, the computing device 100 can calculate the distances (D(S2T1), D(S2T2), D(S2T3), and D(S2T4)) from S2 to the nodes included in the candidate path, respectively. The distance with the minimum value can be calculated. In addition, the computing device 100 calculates the distances (D(S3T1), D(S3T2), D(S3T3), and D(S3T4)) from S3 to the nodes included in the candidate path, and has the minimum value among them. Distance can be calculated. The result is assumed to be as follows.

A.　　　　 D(S1T1), D(S1T2), D(S1T3), D(S1T4) = 7,3,9,6 -> Minimum value is 3

B.　　　　 D(S2T1), D(S2T2), D(S2T3), D(S2T4)= 13,11,21,9 -> Minimum value is 9

C.　　　　 D(T3S1), D(T3S2), D(T3S3), D(S2T4)= 10,7,13,7 -> Minimum value is 7

In this case, since the maximum value of the minimum values is 9, the maximum value of the minimum distance for the first path of the candidate path is 9.

According to an embodiment of the present disclosure, the computing device 100 may calculate the maximum minimum distance for each of K*m1*m2 candidate paths. Additionally, the computing device 100 may obtain the second route based on the calculated minimum distance and maximum value for the first route. Specifically, the computing device 100 may obtain the candidate path with the smallest minimum distance maximum value with respect to the first path among the candidate paths as the second path.

FIG. 14 is a diagram illustrating a method of obtaining a second path based on the number of pixels, according to another embodiment of the present disclosure.

In step S1410, the computing device 100 may convert the first path and each of the plurality of candidate paths into an image.

Additionally, the computing device 100 may calculate the number of path pixels in the image for each candidate path for each candidate path. Additionally, the number of pixels of the first path in the image can be calculated from the image for the first path.

Here, the number of path pixels refers to the number of pixels occupied by nodes and links included in the path in the image.

In step S1320, the computing device 100 selects the second path by comparing the number of pixels for the first path and the number of pixels for each of the candidate paths (e.g., k*m1*m2 candidate paths). It can be obtained. For example, the computing device 100 may obtain, as the second path, a candidate path whose ratio of the number of pixels of the candidate path to the number of pixels of the first path is closest to 1 among the candidate paths. Additionally, the computing device 100 may obtain, as the second path, a candidate path in which the difference between the number of pixels of the candidate path and the number of pixels of the first path is the smallest among the candidate paths.

15 and 16 are diagrams for explaining a method of obtaining a second path based on an area between a first path and a candidate path, according to another embodiment of the present disclosure.

In step s1510, the computing device 100 may calculate the number of pixels for the area formed by each of the first path and the candidate paths.

Figure 16 illustrates the area formed by the candidate path and the first path. For example, the computing device 100 may connect the start node (S1) of the first path and the start node (T1) of the candidate path with a straight line (a). Additionally, the computing device 100 may connect the end node (S3) of the first path and the end node (T4) of the candidate path with a straight line (b). In this case, the computing device 100 can derive an area (c) formed by the first path, a candidate path, and two straight lines (a) and (b), and calculate the number of pixels in the area. .

The computing device 100 may derive a first path and an area forming a region for each of a plurality of candidate paths (e.g., k*m1*m2 candidate paths), and determine a region within the region for each of the candidate paths. The number of pixels can be calculated. In this case, the pixels in the area inside the shape can be calculated through computer vision technology.

In step S1520, the computing device 100 may obtain a second path based on the calculated number of pixels.

For example, the computing device 100 may obtain a candidate path with the smallest calculated number of pixels as the second path.

FIG. 17 is a diagram illustrating a method by which the computing device 100 predicts the current speed of a target route, according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, when matching the first path of the first map database and the second path of the second map database, current speed information about the second path of the second map database can be obtained. This is because speed information is also provided to the routes in the first map database, and the corresponding speed information can be given to the routes in the second map database.

However, because not all routes in the second map database match the routes in the first map database, a method for predicting speed for a route without speed information is needed. Hereinafter, a method for predicting the current speed of the target path will be described.

In step S1710, the computing device 100 may obtain a ratio value between maximum speed information and current speed information for at least one route connected to the target route. In this case, the target path may mean a path on the second map database. Additionally, in step S1720, the computing device 100 may predict the current speed of the target route based on the ratio value between the obtained maximum speed information and current speed information. In this case, the current speed of the target path may mean the speed at which the vehicle is currently moving on the target path.

Figure 18 is a diagram for explaining at least one path connected to the target path. Additionally, Figure 19 is a diagram for explaining a case where there are multiple paths connected to the target path.

At least one path connected to the target path may mean the starting node of the target path, but is not limited to this and may include any path connected to at least one node included in the target path.

When referring to FIG. 17, the computing device 100 may obtain the maximum speed and current speed information of the route connected to the target route. Additionally, the computing device 100 may obtain a ratio value (for example, 80/100*100=80%) between the maximum speed of the node connected to the dangling path and the current speed information.

In this case, the maximum speed information may mean the maximum speed legally restricted to the corresponding route, and the current speed information may mean information about the speed at which the vehicle is currently moving on the relevant route.

Referring to FIG. 18 , when there are multiple paths connected to the target path, the computing device 100 may obtain maximum speed information and current speed information for each of the plurality of paths connected to the target path. Specifically, when there are three routes connected to the target route, speed information for the first route (maximum speed: 100 km/h, current speed: 80 km/h), speed information for the second route (maximum speed: 100 km/h) , current speed: 60 km/h), speed information for the third route (maximum speed: 100 km/h, current speed: 40 km/h) can be obtained. Additionally, it is possible to obtain a ratio value between maximum speed information and current speed information on each route (e.g., first route: 80/100*100 = 80%, second route: 60/100*100 = 60%, 3rd path: 40/100*100 = 40%).

The computing device 100 may predict the current speed of the target path by applying the ratio value between the obtained maximum speed information and current speed information to the maximum speed information of the target path. When referring to FIG. 18, the computing device 100 may obtain the current speed of the target route as 24 km/h (30*80/100 = 24).

According to an embodiment of the present disclosure, when there is a plurality of at least one path connected to the target path, the current speed of the target path is based on the average value of the ratio between the maximum speed information and the current speed information of each of the plurality of connected paths. Speed can be predicted. Referring to FIG. 19, the average ratio between maximum speed information and current speed information for each of the plurality of connected paths is (80+60+40)/3=60. By applying this value to the maximum speed information of the target path, the computing device 100 can predict the current speed of the target path to be 18 km/h (30*60/100).

According to another embodiment of the present disclosure, when there is no path connected to the target path, the computing device 100 may predict 80% of the maximum speed of the target path as the current speed of the target path. In this case, the computing device 100 can change 80% of the numbers according to the user's input.

If there is no current speed information for at least one path connected to the target path, the computing device 100 predicts the current speed for each of the at least one path connected to the target path, and then calculates the expected speed of the target path. It is predictable. For example, when referring to FIG. 19, if the current speed information for the first path does not exist, the computing device 100 predicts the current speed for the first path using the above-described method and then selects the target path. The current speed can be predicted.

Figure 20 is a general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has generally been described above as being capable of being implemented by a computing device, those skilled in the art will understand that the present disclosure can be implemented in combination with computer-executable instructions and/or other program modules that can be executed on one or more computers and/or in hardware and software. It will be well known that it can be implemented as a combination.

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure are applicable to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. It will be appreciated that each of these may be implemented in other computer system configurations, including those capable of operating in conjunction with one or more associated devices.

The described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media. Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes - a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM for reading the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1102, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those skilled in the art will also recognize removable optical media such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other types of computer-readable media, such as the like, may also be used in the example operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in the drive and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also connected to system bus 1108 through an interface, such as a video adapter 1146. In addition to monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102. For simplicity, only memory storage device 1150 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 1102 is connected to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed thereon for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158 or be connected to a communicating computing device on the WAN 1154 or to establish communications over the WAN 1154, such as over the Internet. Have other means. Modem 1158, which may be internal or external and a wired or wireless device, is coupled to system bus 1108 via serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.

Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) enables connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a cell tower. Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (8)

In the method of obtaining a second path of a second map database that matches the first path, which is a path of the first map database, between different map databases composed of nodes and links,
Obtaining coordinate information of a start node of the first path and coordinate information of an end node of the first path;
Obtaining a temporary start node and a temporary end node on a second map database based on the obtained coordinate information;
Obtaining a bounding box based on the temporary start node, the temporary end node, and a padding distance;
determining n1 candidate start nodes within the bounding box based on the distance from the temporary start node and determining n2 candidate end nodes within the bounding box based on the distance from the temporary end node;
determining a plurality of candidate paths based on distance information of each of the paths connecting each of the m1 candidate start nodes among the n1 candidate start nodes and each of the m2 candidate end nodes among the n2 candidate end nodes;
calculating a minimum distance maximum value for the first path for each of the candidate paths; and
Obtaining a second route based on the calculated minimum distance and maximum value for the first route;
Including,
The maximum value of the above minimum distance is:
calculating a minimum value among distance values between nodes included in a candidate path and nodes included in the first path; and
calculating the maximum value among the minimum distance values of each of the nodes included in the candidate path (where the calculated maximum value is the maximum minimum distance value for the first path);
Calculated by a method including,
A method of obtaining a second path in a second database that matches a first path that is a path in a first database. The method of claim 1, wherein determining the plurality of candidate paths comprises:
The first candidate path search method, which obtains K candidate paths by calculating the distances of each of the paths connecting the candidate start node and the candidate end node and selecting K in descending order of the calculated distance, is performed using m1 candidate start nodes and m2 candidate paths. By performing this on each candidate end node, K*m1*m2 candidate paths are obtained.
A method of obtaining a second path in a second database that matches a first path that is a path in a first database. The method of claim 2, wherein n1 and n2 are the same number, and m1 and m2 are the same number.
A method of obtaining a second path in a second database that matches a first path that is a path in a first database. The method of claim 2, wherein n1, n2, m1 and m2 are all the same number,
A method of obtaining a second path in a second database that matches a first path that is a path in a first database. The method of claim 1, wherein obtaining the second route comprises:
Obtaining the candidate path with the smallest minimum distance maximum value with respect to the first path among the candidate paths as the second path,
A method of obtaining a second path in a second database that matches a first path that is a path in a first database. The method of claim 1, wherein n1 candidate start nodes are determined within the bounding box based on the distance from the temporary start node, and n2 candidate end nodes are determined within the bounding box based on the distance from the temporary end node. The step to decide is,
Within the bounding box, n1 candidate start nodes are determined by selecting n1 nodes in the order of the shortest straight-line distance from the temporary start node, and n2 nodes are arranged in the shortest straight-line distance from the temporary end node within the bounding box. Determine n2 candidate starting nodes by selecting ,
A method of obtaining a second path in a second database that matches a first path that is a path in a first database. The method of claim 1, wherein the coordinate information is information about latitude and longitude coordinates,
A method of obtaining a second path in a second database that matches a first path that is a path in a first database. A computer program stored in a computer-readable storage medium, wherein the computer program generates a second map database that matches a first path, which is a path of a first map database, between different map databases composed of nodes and links by one or more processors. Includes instructions for obtaining a second path of, wherein the instructions include:
Obtaining coordinate information of a start node of the first path and coordinate information of an end node of the first path;
Obtaining a temporary start node and a temporary end node on a second map database based on the obtained coordinate information;
Obtaining a bounding box based on the temporary start node, the temporary end node, and a padding distance;
determining n1 candidate start nodes within the bounding box based on the distance from the temporary start node and determining n2 candidate end nodes within the bounding box based on the distance from the temporary end node;
determining a plurality of candidate paths based on distance information of each of the paths connecting each of the m1 candidate start nodes among the n1 candidate start nodes and each of the m2 candidate end nodes among the n2 candidate end nodes;
calculating a minimum distance maximum value for the first path for each of the candidate paths; and
Obtaining a second route based on the calculated minimum distance and maximum value for the first route;
Including,
The maximum value of the above minimum distance is:
calculating a minimum value among distance values between nodes included in a candidate path and nodes included in the first path; and
calculating the maximum value among the minimum distance values of each of the nodes included in the candidate path (where the calculated maximum value is the maximum minimum distance value for the first path);
Calculated by a method including,
A computer program stored on a computer-readable storage medium.