A Method and Device for Converting the Latitude-Longitude and Station Number on Expressways

Abstract

The present invention relates to a method and device for converting longitude and latitude stake numbers on an expressway. The method includes the steps of: measuring the longitude and latitude of the center line of each lane to obtain multiple sets of longitude and latitude coordinates with unique numbers; merging the multiple sets of longitude and latitude coordinates with unique numbers into an ordered set of longitude and latitude coordinates of the lane center line by using a clustering algorithm with distance as the objective function; obtaining the corresponding relationship between the ordered set of longitude and latitude coordinates of the lane center line and the expressway mileage by using the differential principle and generating a relationship dictionary corresponding to mileage and coordinates; and realizing the conversion of longitude and latitude stake numbers based on the input longitude and latitude or stake number and the relationship dictionary. High-precision mutual conversion of the longitude and latitude and stake numbers of the entire expressway can be achieved through the lane longitude and latitude and the highway mileage.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed traffic informatization, and particularly relates to a method and device for converting longitude and latitude stake numbers on expressways. Background Art

[0002] With the increasing intelligence of expressways, the requirements for various data on expressways are becoming more and more refined and comprehensive. For example, devices such as lidar, speed radar, message boards, and cameras cover the highway with high density. The data collected by these facilities can be used to identify some simple events such as speeding and illegal parking. With the proposal of various intelligent service concepts such as vehicle-road collaboration and travel services, the required dimensions of these data are not only time but also space, that is, event + vehicle positioning on the expressway.

[0003] Currently, stake numbers are used to uniformly number the foundation piles before construction to represent the mileage position of vehicles on the expressway, but generally there are markings only at the positions of whole mileage numbers. Therefore, the current method for converting longitude and latitude stake numbers on expressways can only achieve the conversion of longitude and latitude stake numbers at the whole kilometer numbers per kilometer. Summary of the Invention

[0004] Embodiments of the present invention provide a method and device for converting longitude and latitude stake numbers on expressways, which can achieve high-precision mutual conversion of longitude and latitude and stake numbers for the whole process of expressways through lane longitude and latitude and highway mileage.

[0005] In a first aspect, embodiments of the present invention provide a method for converting longitude and latitude stake numbers on expressways, characterized in that the method includes the steps of:

[0006] Measuring the longitude and latitude of the center line of each lane to obtain multiple sets of longitude and latitude coordinates with unique numbers;

[0007] Merging the multiple sets of longitude and latitude coordinates with unique numbers into an ordered set of longitude and latitude coordinates of the lane center line through a clustering algorithm with distance as the objective function;

[0008] Adopting the differential principle to obtain the corresponding relationship between the ordered set of longitude and latitude coordinates of the lane center line and the highway mileage number and generating a relationship dictionary corresponding to mileage numbers and coordinates;

[0009] Implementing the conversion of longitude and latitude stake numbers based on the input longitude and latitude or stake number and the relationship dictionary.

[0010] In some embodiments, the method further includes the steps of:

[0011] Finding out the longitude and latitude coordinates of non-high-speed sections in the multiple sets of longitude and latitude coordinates with unique numbers;

[0012] When merging the multi-segment set of longitude and latitude coordinates with unique numbers, delete the longitude and latitude coordinates of the non-high-speed sections.

[0013] In some embodiments, it further includes the steps of:

[0014] Find the bifurcated lanes in the multi-segment set of longitude and latitude coordinates with unique numbers;

[0015] When merging the multi-segment set of longitude and latitude coordinates with unique numbers, merge each of the bifurcated lanes into a new virtual lane, and the virtual lane belongs to the ordered lanes.

[0016] In some embodiments, the merging each of the bifurcated lanes into a new virtual lane includes the steps of:

[0017] Add the data points of the coordinate sets after the lane bifurcation sequentially and then average them, and use the averaged result as the new virtual lane.

[0018] In some embodiments, the merging the multi-segment set of longitude and latitude coordinates with unique numbers into an ordered lane center line longitude and latitude coordinate set by a clustering algorithm with distance as the objective function includes the steps of:

[0019] Determine the number of clustering clusters according to the number of lanes in the ordered lanes;

[0020] Classify all coordinate segments in the longitude and latitude coordinate set into corresponding clustering clusters by a clustering algorithm with distance as the objective function;

[0021] Classifying all the coordinate segments into corresponding clustering clusters is taken as one clustering iteration and multiple clustering iterations are performed, and the clustering center point positions of each clustering cluster are updated after each clustering iteration until the clustering center point positions of each clustering cluster no longer change;

[0022] Determine the ordered lane center line longitude and latitude coordinate set based on the results after multiple clustering iterations.

[0023] In some embodiments, the classifying all coordinate segments in the longitude and latitude coordinate set into corresponding clustering clusters by a clustering algorithm with distance as the objective function includes the steps of:

[0024] Arbitrarily select a coordinate segment in the longitude and latitude coordinate set as the starting position of the clustering center point of each clustering cluster;

[0025] Calculate the distance from the center point of each coordinate segment in the longitude and latitude coordinate set to the starting positions of each clustering center point and classify each coordinate segment into the clustering cluster with the shortest distance.

[0026] In some embodiments, the objective function includes:

[0027]

[0028] Among them, dis is the distance between two longitude and latitude coordinate points, a is the difference after converting the longitudes of the two points into radians, b is the difference after converting the latitudes of the two points into radians, R1 is the longitude of the first point converted into radians, R2 is the longitude of the second point converted into radians, and earthR is the radius of the earth.

[0029] In some embodiments, updating the cluster center point positions of each cluster includes the steps of:

[0030] Making the cluster center point position of the new cluster be the center of the corresponding classification data set.

[0031] In some embodiments, obtaining the corresponding relationship between the ordered longitude and latitude coordinate set of the lane center line and the highway mileage number by using the differential principle and generating a relationship dictionary corresponding to the mileage number and the coordinates includes the steps of:

[0032] Dividing the mileage number by the number of points in the ordered longitude and latitude coordinate set of the lane center line to obtain the average mileage interval between coordinate points;

[0033] Determining the relationship dictionary corresponding to the mileage number and the coordinates based on the second formula, and the second formula includes: k j +x0*i = Set[i],

[0034] where x0 represents the average mileage interval, k j represents the lane number, and Set[i] represents the i-th coordinate point in the ordered lane coordinate set Set.

[0035] In a second aspect, an embodiment of the present invention provides a highway longitude and latitude stake number conversion device, which is characterized in that it includes the steps of:

[0036] A coordinate set acquisition module, which is used to measure the longitude and latitude of the center line of each lane to obtain a multi-segment longitude and latitude coordinate set with unique numbers;

[0037] A lane merging module, which is used to merge the multi-segment longitude and latitude coordinate sets with unique numbers into an ordered longitude and latitude coordinate set of the lane center line by using a clustering algorithm with distance as the objective function;

[0038] A relationship dictionary generation module, which is used to obtain the corresponding relationship between the ordered longitude and latitude coordinate set of the lane center line and the highway mileage number by using the differential principle and generate a relationship dictionary corresponding to the mileage number and the coordinates;

[0039] A conversion module, which is used to implement longitude and latitude stake number conversion based on the input longitude and latitude or stake number and the relationship dictionary.

[0040] An embodiment of the present invention provides a method and device for converting highway longitude and latitude stake numbers. First, a clustering algorithm is used to process a high-precision longitude and latitude coordinate set. By clustering the unordered longitude and latitude coordinate data, an ordered high-precision lane coordinate set is obtained. At the same time, the differential idea is also adopted to evenly divide the mileage by coordinates to obtain the corresponding relationship between coordinates and mileage, which can give full play to the dense characteristics of the longitude and latitude coordinate set, regard the curve as a straight line in the subtle parts, and then realize the one-to-one correspondence between the unscaled longitude and latitude set and the stake number, and realize the mutual conversion of the high-precision whole-process longitude and latitude and stake number of the highway. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a schematic flowchart of a method for converting highway longitude and latitude stake numbers provided by an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of the dotting situation of longitude and latitude coordinate points provided by an embodiment of the present invention;

[0044] Figure 3 It is a schematic structural diagram of a device for converting highway longitude and latitude stake numbers provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0046] As Figure 1 shown, an embodiment of the present invention provides a method for converting highway longitude and latitude stake numbers, including the steps of:

[0047] S100: Measure the longitude and latitude of the center line of each lane to obtain a set of longitude and latitude coordinates with unique numbers for multiple segments;

[0048] S200: By using a clustering algorithm with distance as the objective function, merge the set of longitude and latitude coordinates with unique numbers for multiple segments into an ordered set of lane center line longitude and latitude coordinates;

[0049] S300: Obtain the corresponding relationship between the ordered lane centerline longitude and latitude coordinate set and the highway mileage using the differential principle, and generate a relationship dictionary corresponding to the mileage and coordinates;

[0050] S400: Based on the input longitude and latitude or station number, realize the conversion between longitude and latitude and station number using the relationship dictionary.

[0051] It can be understood that the acquisition of the coordinate set is carried out in a certain order, for example, automatically by the machine when dotting.

[0052] The embodiment of the present invention provides a method and device for converting highway longitude and latitude and station number. The clustering algorithm is used to process the high-precision longitude and latitude coordinate set. By clustering the unordered longitude and latitude coordinate data, an ordered high-precision lane coordinate set is obtained; at the same time, the differential idea is also used to evenly divide the mileage by coordinates to obtain the corresponding relationship between coordinates and mileage, which can give full play to the dense characteristics of the longitude and latitude coordinate set, regard the curve as straight in the subtle places, and then realize the one-to-one correspondence between the unscaled longitude and latitude set and the station number, and realize the mutual conversion of the high-precision highway full-course longitude and latitude and station number.

[0053] In some embodiments, when S100 obtains the longitude and latitude coordinate set of the lane centerline, the collected coordinate format is a segmented longitude and latitude set, and the coordinates within each segment of the coordinate set are ordered, but the overall set is disordered. Compared with the method of collecting data according to the position marks of the whole mileage in the related art, the method of directly collecting the lane centerline coordinate set in this embodiment can achieve a high-precision effect if a small interval is selected, such as 0.5 meters.

[0054] In some embodiments, before S200 merges the multi-segment longitude and latitude coordinate sets with unique numbers into an ordered lane centerline longitude and latitude coordinate set, it further includes the steps:

[0055] S201: Find the longitude and latitude coordinates of non-highway sections in the multi-segment longitude and latitude coordinate sets with unique numbers;

[0056] S202: Delete the longitude and latitude coordinates of the non-highway sections when merging the multi-segment longitude and latitude coordinate sets with unique numbers.

[0057] This embodiment takes into account the complexity of the road. The measurement results may include the longitude and latitude coordinates of many non-highway sections. These coordinate sets can be found through QGIS, and when merging the highway longitude and latitude coordinates (that is, merging into an ordered lane centerline longitude and latitude coordinate set), this part of the coordinate sets of non-highway sections is deleted.

[0058] In some embodiments, before S200 merges the multi-segment longitude and latitude coordinate sets with unique numbers into an ordered lane centerline longitude and latitude coordinate set, it further includes the steps:

[0059] S203: Find the forked lanes in the multi-segment latitude and longitude coordinate sets with unique numbers;

[0060] S204: When merging the multi-segment latitude and longitude coordinate sets with unique numbers, merge each forked lane into a new virtual lane, and the virtual lane belongs to the ordered lanes.

[0061] In this embodiment, considering that on highways, there are many lane additions designed to reduce congestion, one lane may be connected to multiple lanes ahead, resulting in lane forking. This part of the latitude and longitude data can also be clearly viewed in QGIS. For this part of the data, for the unity of the number of lanes, the forked lanes can be averaged and merged into one lane for the latitude and longitude sets.

[0062] Preferably, when merging each forked lane into a new virtual lane in S204, the data points of the coordinate sets after lane forking can be added sequentially and then averaged, and the averaged result is used as the new virtual lane.

[0063] In some embodiments, S200 includes the steps:

[0064] S210: Determine the number of clustering clusters according to the number of lanes in the ordered lanes;

[0065] S220: Classify all coordinate segments in the latitude and longitude coordinate set into corresponding clustering clusters through a clustering algorithm with distance as the objective function;

[0066] S230: Classify all the coordinate segments into corresponding clustering clusters as one clustering iteration and perform multiple clustering iterations, and update the clustering center point positions of each clustering cluster after each clustering iteration until the clustering center point positions of each clustering cluster no longer change;

[0067] S240: Determine the latitude and longitude coordinate set of the center line of the ordered lanes based on the results after multiple clustering iterations.

[0068] It should be noted that the number of clustering clusters in S210 is determined according to the actual number of lanes. If there are 8 actual lanes, there are 8 clustering clusters.

[0069] In some embodiments, S220 includes the steps:

[0070] S221: Arbitrarily select a coordinate segment in the latitude and longitude coordinate set as the starting position of the clustering center point of each clustering cluster;

[0071] S222: Calculate the distance from the center point of each coordinate segment in the latitude and longitude coordinate set to the starting positions of each clustering center point and classify each coordinate segment into the clustering cluster with the shortest distance.

[0072] Preferably, the objective function includes:

[0073]

[0074] Among them, dis is the distance between two longitude and latitude coordinate points, a is the difference between the longitudes of the two points converted to radians, b is the difference between the latitudes of the two points converted to radians, R1 is the longitude of the first point converted to radians, R2 is the longitude of the second point converted to radians, and earthR is the radius of the earth.

[0075] Preferably, when updating the cluster center point position of each cluster cluster, the cluster center point position of the new cluster cluster can be made the center of the corresponding classification data set. It can be understood that the elements in the data set are disordered, segmented longitude and latitude coordinate points, and each segment of coordinate points is ordered.

[0076] In some embodiments, S300 includes the steps of:

[0077] S310: Divide the mileage by the number of points in the ordered lane centerline longitude and latitude coordinate set to obtain an average mileage interval between coordinate points;

[0078] S320: Determine the relationship dictionary corresponding to the mileage and the coordinates based on a second formula, wherein the second formula includes: j +x0*i=Set[i],

[0079] Among them, x0 represents the average mileage interval, k j Represents the lane number, and Set[i] represents the i-th coordinate point in the ordered lane coordinate set Set.

[0080] It is understandable that, since the coordinate sets with smaller intervals are collected in S100, the curved sections within a short distance can be regarded as straight lines. At the same time, the speed and time interval of the marking points remain unchanged, which can be regarded as the same interval between the coordinate points. In this way, the average mileage interval between the coordinate points can be obtained by dividing the mileage by the number of points in the coordinate set. Each coordinate point and the number of intervals correspond one to one, and the corresponding relationship between longitude and latitude and mileage can be obtained. When the longitude and latitude or the stake number is input in S400, the corresponding conversion can be completed by finding the nearest point.

[0081] In a specific embodiment, the number of lanes is set to 8, that is, the number of clusters is 8. First, the center points of 8 segments of coordinates are randomly selected from the collected longitude and latitude coordinate sets as the starting positions of the cluster center points of each cluster;

[0082] Then calculate the distance from the center point of each segment of the longitude and latitude coordinates to the starting position of each cluster center point, and classify each segment of the coordinates into the cluster with the shortest distance, that is, x i∈S j (Min(dis(x i ,U j ))), where x i is the center point of the i-th segment in the coordinate set, S j is the j-th type of clustering cluster, U j is the clustering center point of S j . The value range of i is [0, the length of the coordinate set), and the value range of j is [1, the number of clustering clusters];

[0083] After all coordinate segments are classified, update the positions of the clustering center points of each clustering cluster so that the new clustering center points are the centers of each classified data set.

[0084] That is where a j is the j-th type of coordinate set after clustering, and x is the center point of each segment of the coordinate set in the j-th type of clustering cluster after classification. Assume that the clustering center point of the 1st type obtained in the i-th clustering iteration is S 1i (where 1 represents the lane number, representing the i-th clustering center point of the 1st lane), and the next obtained clustering center is S 1i+1 , and the center point change value C 1i is obtained, that is, C 1i =|S 1i -S 1i+1 |. Then, each time a clustering iteration is performed, update the clustering center point coordinates according to the new clustering cluster until the position of the clustering center point no longer changes, that is: The 8 clustering clusters obtained at this time are the final clustering results, which are the center line coordinate sets of 8 lanes.

[0085] Since the lane coordinate set at this time is disordered, for example, when the lane is in the east-west direction as a whole, the longitude decreases in turn, and it can be sorted according to the size of the longitude to obtain an ordered lane coordinate set Set.

[0086] Taking part of the coordinates of the 1st lane as an example, as shown in Table 1, ID is the number of each segment of the coordinate set collected, which is unique. The "Coordinate Point" column shows the left example (only 5 are listed) collected corresponding to the ID. The actual number of coordinate points collected for each segment may be several hundred.

[0087] Table 1

[0088]

[0089] For example Figure 2As shown, corresponding marking points are formed during the process of collecting longitude and latitude coordinate points. The marking point interval is approximately 0.5 meters, which can be regarded as a small interval. According to the idea of differentiation, the curve mileage of a short distance is regarded as a straight line. At the same time, the speed and time interval of the marking points remain unchanged, and the interval of coordinate points can be regarded as consistent. Therefore, dividing the mileage by the number of points in the coordinate set can obtain the average mileage interval x0 between coordinate points. Then, the corresponding relationship between the stake number and longitude and latitude is: k j +x0*i = Set[i]; Assume that the length of 25 coordinate points corresponding to the 1st lane (Y1) in the coordinate set (as shown in Table 1) is 15 meters. Then x0 = 15÷25 = 0.6. The corresponding relationship dictionary between the longitude and latitude and the stake number of the 1st lane is shown in Table 2. Through the corresponding relationship dictionary between the longitude and latitude and the stake number, the corresponding conversion can be performed by inputting the longitude and latitude or the stake number.

[0090] Table 2

[0091]

[0092] For example, when converting longitude and latitude to the stake number, the longitude and latitude coordinate point A (115.0345231, 30.31839189) of the 1st lane to be converted can be input. Find the corresponding coordinate point set of the 1st lane from the relationship dictionary and calculate the distance between each coordinate point and the target coordinate point (which can be calculated based on the objective function). The stake number corresponding to the longitude and latitude with the shortest distance is the target stake number. After calculation, the converted stake number is k0+6.0.

[0093] For example, when converting the stake number to longitude and latitude, the stake number k0+2.5 of the 1st lane going upstream to be converted can be input. Calculate the distance from each stake number in Table 2. The longitude and latitude corresponding to the stake number with the closest distance is the target longitude and latitude. After calculation, the closest stake number is k0+2.4, and the longitude and latitude is [115.0345304, 30.31842387]. It should be noted that in this embodiment, there are a total of 8 lanes. Lanes 1-4 are in one driving direction, and lanes 5-8 are in the other driving direction. The lane numbers of the 4 lanes are not 1234, but 1239. The lane numbers of lanes 5-8 are also 1239. For distinction, the 1st lane in the driving direction corresponding to 1-4 is the upstream 1st lane, and the 1st lane in the driving direction corresponding to 5-8 is the downstream 1st lane. It can be understood that since there is no k0+2.5 in Table 2 and the marking point interval in this embodiment is approximately 0.6 meters, it is not an integer multiple. The value with the closest distance can be taken as the matching point.

[0094] In the actual business applications of intelligent transportation, what the upper-layer applications usually care about is not the specific position of the vehicle in the lane, but which lane the vehicle is in, as well as information such as the vehicle's speed and lane change. Therefore, in the corresponding relationship between longitude and latitude and the stake number, only the midline of the lane is taken for the longitude and latitude. In the calculation method of converting longitude and latitude to the stake number, the point with the closest distance in the midline coordinate set is also taken as the mapping result.

[0095] As Figure 3 shown, the embodiment of the present invention further relates to a highway longitude and latitude stake number conversion device, which includes the steps of:

[0096] A coordinate set acquisition module, which is used to measure the longitude and latitude of the center line of each lane to obtain multiple segments of longitude and latitude coordinate sets with unique numbers;

[0097] A lane merging module, which is used to merge the multiple segments of longitude and latitude coordinate sets with unique numbers into an ordered lane center line longitude and latitude coordinate set through a clustering algorithm with distance as the objective function;

[0098] A relationship dictionary generation module, which is used to obtain the corresponding relationship between the ordered lane center line longitude and latitude coordinate set and the highway mileage number by using the differential principle and generate a relationship dictionary corresponding to the mileage number and coordinates;

[0099] A conversion module, which is used to realize the conversion of longitude and latitude stake numbers based on the input longitude and latitude or stake number and the relationship dictionary.

[0100] In some embodiments, the lane merging module is further used for:

[0101] Finding out the longitude and latitude coordinates of non-highway sections in the multiple segments of longitude and latitude coordinate sets with unique numbers;

[0102] Deleting the longitude and latitude coordinates of the non-highway sections when merging the multiple segments of longitude and latitude coordinate sets with unique numbers.

[0103] In some embodiments, the lane merging module is further used for:

[0104] Finding out the bifurcated lanes in the multiple segments of longitude and latitude coordinate sets with unique numbers;

[0105] When merging the multiple segments of longitude and latitude coordinate sets with unique numbers, merging each bifurcated lane into a new virtual lane, and the virtual lane belongs to the ordered lanes.

[0106] Preferably, when merging each bifurcated lane into a new virtual lane, the data points of the coordinate sets after lane bifurcation can be added sequentially and then averaged, and the averaged result is used as the new virtual lane.

[0107] In some embodiments, the lane merging module is further used for:

[0108] Determining the number of clustering clusters according to the number of lanes in the ordered lanes;

[0109] Classifying all coordinate segments in the longitude and latitude coordinate set into corresponding clustering clusters through a clustering algorithm with distance as the objective function;

[0110] Classify all the coordinate segments into corresponding clustering clusters as one clustering iteration, and perform multiple clustering iterations. After each clustering iteration, update the positions of the clustering centers of each clustering cluster until the positions of the clustering centers of each clustering cluster no longer change;

[0111] Determine the ordered set of longitude and latitude coordinates of the lane center line based on the results after multiple clustering iterations.

[0112] In some embodiments, the lane merging module is further configured to:

[0113] Arbitrarily select a coordinate segment in the set of longitude and latitude coordinates as the starting position of the clustering center of each clustering cluster;

[0114] Calculate the distance from the center point of each coordinate segment in the set of longitude and latitude coordinates to the starting positions of each clustering center, and classify each coordinate segment into the clustering cluster with the shortest distance.

[0115] Preferably, the objective function includes:

[0116]

[0117] where dis is the distance between two longitude and latitude coordinate points, a is the difference after converting the longitudes of the two points into radians, b is the difference after converting the latitudes of the two points into radians, R1 is the longitude of the first point converted into radians, R2 is the longitude of the second point converted into radians, and earthR is the radius of the earth.

[0118] Preferably, when updating the positions of the clustering centers of each clustering cluster, the position of the clustering center of the new clustering cluster can be set as the center of the corresponding classified data set.

[0119] In some embodiments, the relationship dictionary generation module is further configured to:

[0120] Divide the mileage by the number of points in the ordered set of longitude and latitude coordinates of the lane center line to obtain the average mileage interval between coordinate points;

[0121] Determine the relationship dictionary between the mileage and the coordinates based on the second formula, and the second formula includes: k j +x0*i = Set[i],

[0122] where x0 represents the average mileage interval, k j represents the lane number, and Set[i] represents the i-th coordinate point in the ordered set of lane coordinates Set.

[0123] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations. In the hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be executed by several physical components in cooperation. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or can be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable storage medium, which can include a computer-readable storage medium (or non-transitory medium) and a communication medium (or transitory medium).

[0124] It should be noted that in the present invention, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article, or device including the said element.

[0125] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for converting longitude and latitude station numbers of expressways, characterized in that, The method includes the steps of: Measuring the longitude and latitude of the center line of each lane to obtain multiple sets of longitude and latitude coordinates with unique numbers; Merging the multiple sets of longitude and latitude coordinates with unique numbers into an ordered set of longitude and latitude coordinates of the lane center line through a clustering algorithm with distance as the objective function; Adopting the differential principle to obtain the corresponding relationship between the ordered set of longitude and latitude coordinates of the lane center line and the highway mileage, and generating a relationship dictionary corresponding to mileage and coordinates; Realizing the conversion between longitude and latitude and mileage based on the input longitude and latitude or mileage and the relationship dictionary; The step of merging the multiple sets of longitude and latitude coordinates with unique numbers into an ordered set of longitude and latitude coordinates of the lane center line through a clustering algorithm with distance as the objective function includes the steps of: Determining the number of clustering clusters according to the number of lanes in the ordered lanes; Classifying all coordinate segments in the longitude and latitude coordinate set into corresponding clustering clusters through a clustering algorithm with distance as the objective function; Classifying all the coordinate segments into the corresponding clustering clusters as one clustering iteration, and performing multiple clustering iterations, and updating the clustering center point positions of each clustering cluster after each clustering iteration until the clustering center point positions of each clustering cluster no longer change; Determining the ordered set of longitude and latitude coordinates of the lane center line based on the results after multiple clustering iterations.

2. The method for converting highway longitude and latitude stake numbers according to claim 1, characterized in that, It further includes the steps of: Finding out the longitude and latitude coordinates of non-highway sections in the multiple sets of longitude and latitude coordinates with unique numbers; Deleting the longitude and latitude coordinates of the non-highway sections when merging the multiple sets of longitude and latitude coordinates with unique numbers.

3. The method for converting highway longitude and latitude stake numbers according to claim 1, characterized in that It further includes the steps of: Finding out the bifurcated lanes in the multiple sets of longitude and latitude coordinates with unique numbers; When merging the multiple sets of longitude and latitude coordinates with unique numbers, merging each bifurcated lane into a new virtual lane, and the virtual lane belongs to the ordered lanes.

4. The method for converting highway longitude and latitude stake numbers according to claim 3, characterized in that, The step of merging each bifurcated lane into a new virtual lane includes the steps of: Sequentially adding and averaging the data points of the coordinate set after lane bifurcation, and using the averaged result as the new virtual lane.

5. The method for converting highway longitude and latitude stake numbers according to claim 4, wherein The step of classifying all coordinate segments in the longitude and latitude coordinate set into corresponding clustering clusters through a clustering algorithm with distance as the objective function includes the steps of: Arbitrarily selecting a coordinate segment in the longitude and latitude coordinate set as the starting position of the clustering center point of each clustering cluster; Calculating the distance from the center point of each coordinate segment in the longitude and latitude coordinate set to the starting position of each clustering center point, and classifying each coordinate segment into the clustering cluster with the shortest distance.

6. The method for converting highway longitude and latitude stake numbers according to claim 5, wherein The objective function includes: Where dis is the distance between two longitude and latitude coordinate points, a is the difference after converting the longitudes of the two points into radians, b is the difference after converting the latitudes of the two points into radians, R1 is the longitude of the first point converted into radians, R2 is the longitude of the second point converted into radians, and earthR is the radius of the earth.

7. The method for converting highway longitude and latitude stake numbers according to claim 1, characterized in that, The step of updating the clustering center point positions of each clustering cluster includes the steps of: Making the clustering center point position of the new clustering cluster be the center of the corresponding classified data set.

8. The method for converting highway longitude and latitude stake numbers according to claim 1, characterized in that, The step of adopting the differential principle to obtain the corresponding relationship between the ordered set of longitude and latitude coordinates of the lane center line and the highway mileage, and generating a relationship dictionary corresponding to mileage and coordinates includes the steps of: Obtain the average mileage interval between coordinate points by dividing the mileage used by the number of points in the ordered set of lane centerline longitude and latitude coordinates; Determine the relationship dictionary between the mileage and coordinates based on the second formula, where the second formula includes: k j + x0 * i = Set[i], Among them, x0 represents the average mileage interval, and k j represents the lane number, and Set[i] represents the i-th coordinate point in the ordered lane coordinate set Set.

9. A highway longitude and latitude stake number conversion device for implementing the highway longitude and latitude stake number conversion method as described in claim 1, characterized in that, It includes the steps: A coordinate set acquisition module, which is used to measure the longitude and latitude of the center line of each lane to obtain multiple sets of longitude and latitude coordinates with unique numbers; A lane merging module, which is used to merge the multiple sets of longitude and latitude coordinates with unique numbers into an ordered set of lane centerline longitude and latitude coordinates by using a clustering algorithm with distance as the objective function; A relationship dictionary generation module, which is used to obtain the corresponding relationship between the ordered set of lane centerline longitude and latitude coordinates and the highway mileage by using the differential principle and generate a relationship dictionary corresponding to mileage and coordinates; A conversion module, which is used to implement longitude and latitude stake number conversion based on the input longitude and latitude or stake number and the relationship dictionary.

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