CN110793531A - Road matching method and device and readable storage medium - Google Patents
Road matching method and device and readable storage medium Download PDFInfo
- Publication number
- CN110793531A CN110793531A CN201910866064.2A CN201910866064A CN110793531A CN 110793531 A CN110793531 A CN 110793531A CN 201910866064 A CN201910866064 A CN 201910866064A CN 110793531 A CN110793531 A CN 110793531A
- Authority
- CN
- China
- Prior art keywords
- road
- matched
- roads
- positions
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the application discloses a road matching method, a road matching device and a vehicle. Through the road matching process based on the historical movement distribution information of the road, the accuracy of road matching is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for road matching, and a readable storage medium.
Background
With the continuous development of the computer field, the positioning system has more and more extensive applications in automobile navigation and traffic management with the advantages of global, all-round and all-weather navigation positioning, timing and speed measurement. Such as planning travel routes and navigation, vehicle tracking, emergency assistance, and the like.
Currently, a road matching method based on position performs road matching by geometric relationship between local position points and a nearest road network. In the case where the position of the track is too sparse, for example, a partial position of the track is not acquired due to the device being blocked, or when the road condition is too complex, for example, a scene in which the parallel road has a closed road or the position of the parallel road drifts to the middle of the parallel road, it is difficult to match the accurate road through the local position. It can be seen that the road matching method depending on the position of the trajectory still needs to be improved in matching the road accuracy.
Disclosure of Invention
The embodiment of the application provides a method and a device for road matching and a readable storage medium, and the track to be matched can improve the accuracy of road matching based on the historical motion distribution information of a road.
In a first aspect, an embodiment of the present application provides a road matching method, including:
acquiring a track to be matched, wherein the track to be matched comprises P positions and motion information corresponding to the P positions respectively, and P is a positive integer greater than 2;
obtaining historical motion distribution information of N roads, wherein the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer;
and determining the road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions respectively.
As a possible implementation manner, the historical motion distribution information of each road includes Q regions on each road and statistical motion information corresponding to the Q regions, the statistical motion information corresponding to a first region is obtained by statistics according to the motion information of the historical track in the first region, the first region is any one of the Q regions, and Q is a positive integer greater than 2; the determining the road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information respectively corresponding to the P positions comprises:
determining P areas matched with the P positions from the Q areas on each road according to the positions of the Q areas on each road and the P positions;
and determining a road matched with the road to be matched in the N roads according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions.
As a possible implementation, the determining, according to the positions of the Q areas on each road and the P positions, P areas matching the P positions from the Q areas on each road includes:
determining the distance between each of the Q areas of each road and a first position, wherein the first position is any one of the P positions;
and determining the area matched with the first position in the Q areas on each road as the area corresponding to the minimum distance in the Q areas on each road.
As a possible implementation manner, the determining, according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, a road that is matched with the road to be matched in the N roads includes:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, wherein the first weight of each road is used for determining the probability of the track to be matched on each road;
and determining the road corresponding to the maximum first weight in the N roads as the road matched with the track to be matched in the N roads.
As a possible implementation manner, the determining, according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, a road that is matched with the road to be matched in the N roads includes:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions;
determining a second weight of each road according to the distance between the P positions and each road;
determining the total weight of each road according to the first weight of each road and the second weight of each road, wherein the total weight of each road is used for indicating the probability of the track to be matched on each road;
and determining the road corresponding to the maximum total weight in the N roads as the road matched with the track to be matched in the N roads.
As a possible implementation manner, before the obtaining of the historical motion distribution information of the N roads, the method further includes:
and determining a first area comprising the P positions according to the P positions of the track to be matched, wherein the N roads are roads in the first area.
As a possible implementation manner, the acquiring historical motion distribution information of the N roads includes:
acquiring current environmental parameters, wherein the environmental parameters comprise time and/or weather;
determining historical motion distribution information of the N roads corresponding to the current environmental parameter according to a corresponding relation between the environmental parameter and historical motion distribution information of the M roads, wherein the M roads comprise the N roads, and M is a positive integer not less than N.
In a second aspect, an embodiment of the present application discloses a road matching device, which includes:
the device comprises a first acquisition module, a second acquisition module and a matching module, wherein the first acquisition module is used for acquiring a track to be matched, the track to be matched comprises P positions and motion information corresponding to the P positions respectively, and P is a positive integer larger than 2;
the second acquisition module is used for acquiring historical motion distribution information of N roads, wherein the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer;
and the matching module is used for determining the road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions respectively.
As a possible implementation manner, the historical motion distribution information of each road includes Q regions on each road and statistical motion information corresponding to the Q regions, the statistical motion information corresponding to a first region is obtained by statistics according to the motion information of the historical track in the first region, the first region is any one of the Q regions, Q is a positive integer greater than 2, and the matching module includes:
the area determining unit is used for determining P areas matched with the P positions from the Q areas on each road according to the positions of the Q areas on each road and the P positions;
and the road determining unit is used for determining a road matched with the road to be matched in the N roads according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions.
As a possible implementation, the region determining unit is specifically configured to:
determining the distance between each of the Q areas of each road and a first position, wherein the first position is any one of the P positions;
and determining the area matched with the first position in the Q areas on each road as the area corresponding to the minimum distance in the Q areas on each road.
As a possible implementation, the road determination unit is specifically configured to:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, wherein the first weight of each road is used for determining the probability of the track to be matched on each road;
and determining the road corresponding to the maximum first weight in the N roads as the road matched with the track to be matched in the N roads.
As a possible implementation, the road determination unit is specifically configured to:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions;
determining a second weight of each road according to the distance between the P positions and each road;
determining the total weight of each road according to the first weight of each road and the second weight of each road, wherein the total weight of each road is used for indicating the probability of the track to be matched on each road;
and determining the road corresponding to the maximum total weight in the N roads as the road matched with the track to be matched in the N roads.
In a third aspect, an embodiment of the present application discloses a road matching device, which includes a processor and a memory, where the processor is connected to the memory, where the memory is used to store a program code, and the processor is used to call the program code to implement the method in the first aspect.
In a fourth aspect, an embodiment of the present application discloses a vehicle, which includes a processor and a memory, where the processor is connected to the memory, where the memory is used to store program codes, and the processor is used to call the program codes to implement the method in the first aspect.
In a fifth aspect, the present application discloses a computer-readable storage medium, which stores a computer program or computer instructions, and when the computer program or the computer instructions are executed, the method for road matching as disclosed in the first aspect or any embodiment of the first aspect is implemented.
In the embodiment of the application, a server acquires a track to be matched, wherein the track to be matched comprises P positions and motion information respectively corresponding to the P positions, and P is a positive integer greater than 2; obtaining historical motion distribution information of N roads, wherein the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer; and determining a road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions respectively. By implementing the embodiment of the application, the historical movement distribution information of the road is obtained by introducing the historical track of the road, when the current track to be matched of the vehicle or the terminal is obtained, the track to be matched can be matched according to the historical movement distribution information of the road, and then the road where the vehicle or the terminal is located is determined. The embodiment of the application can improve the accuracy of road matching based on the historical motion distribution information of the road, and particularly solves the difficult problem of parallel road matching in road matching.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of a system architecture for road matching according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a road matching method according to an embodiment of the present disclosure;
FIG. 3 is a schematic flowchart of another road matching method provided in the embodiments of the present application;
fig. 4 is an exemplary method for determining N roads from M roads according to P positions in the embodiment of the present application;
FIG. 5 is a flowchart illustrating a statistical method for historical tracks according to an embodiment of the present disclosure;
fig. 6 is a method for dividing a road area according to an embodiment of the present application;
fig. 7 is a schematic flowchart of a method for obtaining historical motion distribution information of N roads according to an embodiment of the present disclosure;
fig. 8 is historical motion distribution information of a road corresponding to a part of environmental parameters, which is given in an exemplary manner in an embodiment of the present application;
FIG. 9 illustrates an exemplary manner of determining P regions on each road based on P locations of the tracks to be matched;
fig. 10 is a schematic structural diagram of a road matching device provided in an embodiment of the present application;
fig. 11 is a schematic structural diagram of another road matching device provided in the embodiment of the present application;
fig. 12 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be understood that the terminology used in the embodiments of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic diagram of a system architecture for road matching according to an embodiment of the present disclosure. The system 100 may include at least one vehicle 101, a road matching server 102, a map server 103, a terminal server 104, and the like.
The vehicle 101 may obtain its position and motion information (e.g., velocity, acceleration, etc.) corresponding to the position at a set frequency via a positioning system. Wherein the motion information corresponding to the position a of the vehicle 101 represents the speed and/or acceleration, etc. of the vehicle 101 at the position a. In another embodiment of the present application, the vehicle 101 may also acquire only the position and the time when the vehicle is at the position, and the road matching server 102 or the vehicle 101 may calculate the motion information corresponding to the position according to the acquired position and time. The positioning system may be any one of a GPS positioning system, a BPS positioning system, and a base station positioning system.
The road matching server 102 may receive historical movement traces transmitted by the plurality of vehicles 101, and the historical movement traces may include a plurality of positions and movement information corresponding to the plurality of positions. The road matching server 102 may count a plurality of historical movement tracks on each road to obtain historical movement distribution information of each road. The road matching server 102 may count the number of location points in one area on each road, an average value of speeds corresponding to the location points in the area (also referred to as an average speed), an average value of accelerations corresponding to the location points in the area (also referred to as an average acceleration), and the like.
The road matching server 102, after obtaining the historical motion distribution information of the plurality of roads, may determine the road on which the vehicle 101 is located based on the plurality of historical motion distribution information. The specific implementation may be that the vehicle acquires a track to be matched, where the track to be matched includes P positions and motion information corresponding to the P positions respectively; and determining a road matched with the road to be matched in the plurality of roads according to the historical motion distribution information of the plurality of roads and the motion information corresponding to the P positions respectively, namely the road where the vehicle 101 is located. The road matching server 102 may also transmit the matching result to the vehicle 101 so that the vehicle 101 may navigate or drive or the like based on the matching result.
In an application scenario, the multiple vehicles 101 may send the track to be matched to the road matching server 102 in real time, and the road matching server 102 may count the traffic flow of each road or a certain area on the road in real time based on the matching result of the track to be matched of the multiple vehicles 101, for example, the density of the vehicles on each road, the average speed of the vehicles on each road, or the density of the vehicles in each area of each road and the average speed of the vehicles in the area. Further, the road matching server 102 may transmit the traffic volume of each road to the map server 103.
The map server 103 may provide reference information for navigation of the vehicle 101/terminal 104 based on the received traffic volume of each road, for example, the map server 103 may determine the degree of congestion of the road based on the traffic volume of each road, and further, transmit indication information indicating the degree of congestion of the road to the vehicle 101/terminal 104, at which time, the vehicle 101/terminal 104 may display the indication information to prompt the user of the degree of congestion of the road, for example, the degree of congestion of each link on the road may be distinguished by colors.
The terminal 104 may be a device in communication connection with the vehicle 101, and the terminal 104 may transmit the track to be matched of the vehicle 101 to the road matching server 102, or may receive the matching result transmitted by the road matching server 102. The terminal 104 may also be a client of the map server 103, and the terminal 104 installs a map application, such as an Tencent map, and interacts with the map server 103 based on the map application to realize each function of the map application.
It should be noted that the road matching server 102 and the map server 103 may be the same device, the terminal 104 and the map server 103 are not essential devices for the system 100, and the system 100 may include other devices, which is not limited herein.
Referring to fig. 2 and fig. 3, fig. 2 and fig. 3 are schematic flow charts of a road matching method provided in an embodiment of the present application, where a first vehicle may be the vehicle 101 in fig. 1, and a server may be the road matching server in fig. 1, and the method may be executed by the first vehicle, a terminal, or a server, and the embodiment of the present application takes the server as an example for description. For example, the following steps S202-S208, S2061-S2062 may also be performed by the first vehicle or terminal, and the method may include, but is not limited to, the following steps:
s202, the server obtains a track to be matched of the first vehicle, wherein the track to be matched comprises P positions and motion information corresponding to the P positions respectively, and P is a positive integer larger than 2.
Wherein, position DiThe corresponding movement information indicates that the first vehicle is at position DiSpeed and/or acceleration, P positions including position Di。
S202 may include three implementations as follows:
the first implementation mode comprises the following steps:
the first vehicle can obtain the position of the first vehicle through a positioning system according to a set frequency, and obtain motion information (such as speed, acceleration and the like) corresponding to the position through a motion sensor on the first vehicle, so as to obtain a track to be matched. And then, the first vehicle sends the acquired track to be matched to the server. Optionally, the first vehicle may also perform communication connection with a terminal, such as a mobile phone, a tablet computer, and the like, for example, perform communication connection through bluetooth, and then the first vehicle may send the track to be matched to the terminal, and the terminal sends the track to be matched to the server. It should be understood that the motion sensors may include velocity sensors, accelerometers, gyroscopes, magnetic sensors, and the like.
The second implementation mode comprises the following steps:
it should be understood that the location and movement information of the terminal located on the first vehicle may represent the first vehicle location and movement information, respectively. At this time, the terminal can obtain the position of the terminal through the positioning system according to the set frequency, and obtain the motion information (such as speed, acceleration and the like) corresponding to the position through the motion sensor on the terminal to obtain the track to be matched, and further send the track to be matched to the server.
The third implementation mode comprises the following steps:
the first vehicle or the terminal may acquire the position of the first vehicle and the time when each position is acquired in real time. And then the first vehicle, the terminal or the server calculates the motion information of the first vehicle based on the position of the first vehicle and the corresponding time of the position, and further obtains the track to be matched of the first vehicle.
The method and the device for acquiring the track to be matched of the first vehicle are not limited to the three implementation manners, and other implementation manners for acquiring the track to be matched of the first vehicle may also be included in the embodiment of the present application, which are not described herein again.
S204, the server determines N roads according to the P positions, wherein N is a positive integer.
It should be understood that S204 is not a necessary step of the method, and the N roads may be all roads on the map or part of roads on the map.
In an implementation of the embodiment of the application, after the server acquires the track to be matched, the server may determine, according to P positions of the track to be matched, a first area including the P positions. The first region may be a circular, rectangular or other shaped region geometrically centered at one of the P positions or an average of the P positions. The radius of the circular area or the side length of the rectangular area may be determined according to the P positions to ensure that the P positions are all within the first area.
Referring to fig. 4, fig. 4 illustrates an exemplary method for determining N roads from M roads based on P locations. M is a positive integer not less than N. Fig. 4 illustrates that, taking six positions D1, D2, D3, D4, D5 and D6 included in the track to be matched as an example, an average position of seven positions included in the track to be matched can be calculated
Further, an average position is calculated
Respectively with the distance from six positions in the track to be matched to average the positions
The first region 401 is formed with a length larger than the maximum distance as a side length as a geometric center. Further, the N roads are all roads in the first area. As shown in fig. 4, three roads L1, L2, and L3 in the first area are obtained, that is, roads near the track to be matched. Further, the server can identify the track to be matched at three of L1, L2 and L3Which one of the roads is.
It should be noted that the first area including P positions and N roads may also be determined in other manners, and the first area may also be a diamond area or a first area with other shapes, which is not limited herein.
S206, the server obtains historical motion distribution information of the N roads, wherein the historical motion distribution information of each road is obtained through statistics according to the motion information of the historical track on each road.
In one implementation of the embodiment of the application, the server may pre-store historical motion distribution information of M roads, where M is a positive integer not less than N. After the N roads are determined, the server may screen historical motion distribution information corresponding to the N roads from pre-stored historical motion distribution information of the M roads.
It should be understood that when the method is executed by a first vehicle or terminal, the first vehicle or terminal may acquire historical motion distribution information of M roads or historical motion distribution information of N roads from a server.
As shown in fig. 5, fig. 5 is a schematic flowchart of a statistical method for historical tracks according to an embodiment of the present disclosure.
S502: the server obtains H historical tracks, wherein each historical track in the H historical tracks comprises at least two sampling points and motion information corresponding to each sampling point.
S504: and the server counts the motion information corresponding to the sampling points in each area on each road.
The server may map the spatial coordinates into a raster image, as shown in fig. 6, where fig. 6 is a method for dividing a road region according to an embodiment of the present application. The map is divided into a plurality of areas by latitude and longitude, a grid represents an area, and an area can be divided into a road. The server can count the number, average speed and/or average acceleration and the like of sampling points of the H historical tracks in each area. It should be understood that the area AiThe corresponding statistical motion information is in the area A according to the historical trackiIs obtained by statistics of motion informationI.e. is region AiCorresponding average speed and/or area AiCorresponding average acceleration, wherein, area AiCorresponding to an average speed of region AiAverage of the velocities corresponding to the inner sampling points, region AiCorresponding average acceleration is region AiThe average of the accelerations corresponding to the inner sample points. At this time, the value of the ith row and jth column of pixel points in the raster image includes the number of sampling points in the region corresponding to the ith row and jth column of pixel points, the average velocity and/or average acceleration corresponding to the region, and the like. The plurality of regions belonging to the road R and the statistical motion information corresponding to the plurality of regions respectively form historical motion distribution information of the road R.
Fig. 6 exemplarily shows roads R1, R2, and R3, and exemplarily shows regions belonging to roads R1, R2, and R3, respectively. It should be understood that the road to which the grid/region belongs may be agreed; the road to which the grid/region belongs may also be determined according to the area ratio of the region covered by the road in the region, and as shown in fig. 6, if the area ratio of the region covered by the road R3 is greater than 50%, the region is assigned to the road R3.
In another implementation of the embodiment of the present application, the historical track further includes environmental parameters, such as time and/or weather, when the vehicle acquires the historical track. The H historical tracks may be historical tracks under specific environmental parameters, for example, when all the H historical tracks are 8 hours to 8 hours and 30 minutes, and when the weather is clear, the vehicle is located by the locating system. At this time, the server may store the historical motion distribution information of the M roads corresponding to the plurality of environment parameters, respectively. The historical motion distribution information of the N roads adopted in the process of road matching of the first vehicle is the historical motion distribution information of the N roads corresponding to the current environment parameters where the first vehicle is located.
In another implementation of the embodiment of the application, the server may pre-store a corresponding relationship between the environmental parameters and historical motion distribution information of the M roads. As shown in fig. 7, fig. 7 is a schematic flowchart of a method for acquiring historical motion distribution information of N roads according to an embodiment of the present application.
S2061, the server obtains current environment parameters, and the current environment parameters comprise current time and/or current weather. Wherein the current environmental parameter is an environmental parameter when the vehicle acquires the location.
The server may obtain the current weather from a weather website, or may obtain the current environmental parameters in other manners.
S2062, determining historical motion distribution information of N roads corresponding to the current environmental parameter according to the corresponding relation between the environmental parameter and the historical motion distribution information of the M roads, wherein the M roads comprise the N roads, and M is a positive integer not less than N.
The historical motion distribution information of the M roads respectively corresponding to each parameter can be stored in a database of the server or can be acquired from other places. Referring to fig. 8, fig. 8 exemplarily shows historical motion distribution information of a road corresponding to a portion of the environmental parameter. As shown in fig. 8, historical track distribution information of a road corresponding to a current environmental parameter (current weather and/or current time) may be found according to the current environmental parameter. As shown in fig. 8, for example, if the current environment is a combination of T1 and G1, the historical track distribution information of the roads R1, R2, and … … RM is the same as the current environment T1,G1U1、 T1,G1U2…… T1,G1UM. It is understood that the historical track distribution information of each road includes statistical motion information corresponding to a plurality of areas belonging to the road.
It should be understood that the particular embodiments are not limited to the several environmental parameters shown in FIG. 8, nor to the storage form of the data shown in FIG. 8.
And S208, the server determines a road matched with the track to be matched in the N roads according to the historical motion distribution information of the N roads and the track to be matched.
S208 may include, but is not limited to, the following two implementations:
implementation mode 1
S1051, the server determines P areas matched with the P positions from the Q areas on each road according to the positions and the P positions of the Q areas on each road.
In some embodiments of the present application, the server may determine distances of the Q regions of each road from a first location, which is any one of the P locations; further, the server determines that the area matched with the first position in the Q areas on each road is the area corresponding to the minimum distance in the Q areas on each road; thus, P regions matching the P positions out of the Q regions on each road are obtained.
Referring to fig. 9, fig. 9 illustrates an exemplary manner of determining P regions on each road based on P positions of the tracks to be matched. As shown in the figure, the track to be matched includes six positions D1, D2, D3, D4, D5 and D6, an area (an area with a shaded portion on the road R1 in fig. 9) corresponding to each of the six positions of the track to be matched is determined on the road R1, and an area (an area with a shaded portion on the road R2 in fig. 9) corresponding to each of the six positions of the track to be matched is determined on the road R2.
Alternatively, the shape of the road and the manner of dividing the road area presented in fig. 9 are not limited, and the area may be divided according to the shape of the road, which is not limited herein.
S1052, the server determines the road matched with the road to be matched in the N roads according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions.
And the statistical motion information corresponding to each region of each road is obtained by statistics according to the motion information of each region of the historical track on the road.
Alternatively, the statistical motion information of each region may refer to the speed, acceleration, number of positions, and the like of the region, which is not limited herein.
In a first implementation of S1052: the server can determine a first weight of the road Ri according to the similarity between the statistical motion information corresponding to the P regions determined in the road Ri and the motion information corresponding to the P positions, wherein the first weight of the road Ri is used for determining the probability of the track to be matched on the road Ri. Furthermore, the server may determine a road corresponding to the largest first weight among the N roads as a road matched with the track to be matched among the N roads. The road Ri is one of the N roads, i is an index of the road in the N roads, and i is a positive integer not greater than N. At this time, the first weight of the road Ri may be the probability of the track to be matched on the road Ri, or indicate the probability of the track to be matched on the road Ri.
In a second implementation of S1052: the server can determine a first weight of the road Ri according to the similarity between the statistical motion information corresponding to the P areas determined in the road Ri and the motion information corresponding to the P positions; determining a second weight of the road Ri according to the distances between the P positions and the road Ri; further, determining the total weight of the road Ri according to the first weight of the road Ri and the second weight of the road Ri, wherein the total weight of the road Ri is used for indicating the probability of the track to be matched on the road Ri; and finally, determining the road corresponding to the maximum total weight in the N roads as the road matched with the track to be matched.
The total weight of the road Ri may be obtained by adding the first weight of the road Ri and the second weight of the road Ri, or by multiplying the first weight of the road Ri and the second weight of the road Ri, or by other operation methods, which is not limited herein.
In two implementations of S1052, the first weight W1 of the road RiRiOne of the calculation methods of (1) may be:
wherein k is an index of a position in P positions, k being a positive integer no greater than P; an area k on the road Ri is an area matched with the position k; n iskRepresenting the number of sampling points of the historical track in a region k in a road Ri; l isiRepresenting the total number of sampling points of the historical track on the Ri; v. ofkRepresenting the motion information, such as speed, corresponding to the kth position in the track to be matched;representing statistical motion information, such as average speed, corresponding to the region k on the road Ri. In another embodiment of the present application, W1RiThe number n of sampling points in the region k in the road Ri of the historical track can also be not considered in the calculation processkAnd the total number L of sampling points of the historical track on the Ri roadiAnd will not be described herein.
In a second implementation manner of S1052, the server may calculate distances between P positions and the road Ri, and further, a total distance between P positions and the road Ri (i.e. a sum of the distances between P positions and the road Ri), and a second weight W2 of the road RiRiHaving a negative correlation with the total distance of the P positions from the road Ri, i.e. the smaller the total distance of the P positions from the road Ri, the second weight W2 for the road RiRiThe larger the track is, the more the track to be matched is matched with the road Ri.
It should be understood that for a straight road, the distance from position k to road Ri is the distance from position k to the centerline of road Ri; for a curved road, the distance from the position k to the road Ri is the distance from the position k to the tangent line of the center line of the road Ri at the position S, which is the closest point on the center line of the road Ri to the position k.
It should be understood that, without being limited to the above method, in another embodiment of the present application, a matching degree of the track to be matched and each road may also be obtained by a geometric method, and the second weight of the road may be determined based on the matching degree. The matching degree between the track to be matched and each road can also be obtained by other methods, such as a topological method, a probabilistic method, a hidden markov method, and the like, which is not limited herein.
The geometric matching algorithm is to obtain a point or a road section closest to the position according to the distance between the position and each node in the road network or the projection distance between the position and the road section in the road network, so as to determine the road to which the position belongs according to the closest point or road section.
It should be understood that the topological algorithm performs weighted calculation according to the vehicle history data, the relationship between the tracks, the road topological characteristics and the like to obtain the total weight of the road section, and selects the road section with the largest weight as the matched road section. The probability method includes the steps that multiple sections are selected from a confidence region and added into a candidate road section set, then a score is calculated for each candidate road section, and finally the road section with the highest score is selected as a matching road section. The hidden markov method does first enable a set of candidate road segments for each position to be matched, each candidate road segment being represented as a hidden state in a markov chain and having an observed state probability. If a location point is found to be very close to a road segment, a higher probability value is assigned to the road segment. The edges of each pair of adjacent vertices in the Markov chain are connected and weights, and state transition probabilities, are calculated. Finally, the maximum likelihood path with the highest observed state probability and state transition probability is found on the Markov chain.
In the embodiment of the application, a server acquires a track to be matched, wherein the track to be matched comprises P positions and motion information respectively corresponding to the P positions, and P is a positive integer greater than 2; obtaining historical motion distribution information of N roads, wherein the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer; and determining a road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions respectively. By implementing the embodiment of the application, the historical movement distribution information of the road is obtained by introducing the historical track of the road, when the current track to be matched of the vehicle or the terminal is obtained, the track to be matched can be matched according to the historical movement distribution information of the road, and then the road where the vehicle or the terminal is located is determined. The embodiment of the application can improve the accuracy of road matching based on the historical motion distribution information of the road, and particularly solves the difficult problem of parallel road matching in road matching.
The following describes apparatuses and devices according to embodiments of the present application.
Referring to fig. 10, fig. 10 is a schematic structural diagram of a road matching device according to an embodiment of the present application. As shown in fig. 10, the road matching apparatus 1000 may be applied to the road matching server 102, the vehicle 101, or the terminal 104 in the above-described embodiments corresponding to fig. 2 or fig. 3, and the apparatus 1000 may include:
a first obtaining module 1001, configured to obtain a track to be matched, where the track to be matched includes P positions and motion information corresponding to the P positions, and P is a positive integer greater than 2;
a second obtaining module 1002, configured to obtain historical motion distribution information of N roads, where the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer;
a matching module 1003, configured to determine, according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions, a road that is matched with the road to be matched in the N roads.
In an implementation of the embodiment of the present application, the historical motion distribution information of each road includes Q regions on each road and statistical motion information corresponding to the Q regions, the statistical motion information corresponding to a first region is obtained by statistics of the motion information of the historical track in the first region, the first region is any one of the Q regions, Q is a positive integer greater than 2, and the matching module 1003 includes:
an area determining unit 1003a, configured to determine, according to the positions of the Q areas on each road and the P positions, P areas that match the P positions from the Q areas on each road;
a road determining unit 1003b, configured to determine, according to similarities between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, a road that is matched with the road to be matched in the N roads.
In an implementation of the embodiment of the present application, the area determining unit is specifically configured to:
determining the distance between each of the Q areas of each road and a first position, wherein the first position is any one of the P positions;
and determining the area matched with the first position in the Q areas on each road as the area corresponding to the minimum distance in the Q areas on each road.
In an implementation of the embodiment of the present application, the road determining unit is specifically configured to:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, wherein the first weight of each road is used for determining the probability of the track to be matched on each road;
and determining the road corresponding to the maximum first weight in the N roads as the road matched with the track to be matched in the N roads.
In an implementation of the embodiment of the present application, the road determining unit is specifically configured to:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions;
determining a second weight of each road according to the distance between the P positions and each road;
determining the total weight of each road according to the first weight of each road and the second weight of each road, wherein the total weight of each road is used for indicating the probability of the track to be matched on each road;
and determining the road corresponding to the maximum total weight in the N roads as the road matched with the track to be matched in the N roads.
In an implementation of the embodiment of the present application, the area determining unit 1003a is further configured to:
and determining a first area comprising the P positions according to the P positions of the track to be matched, wherein the N roads are roads in the first area.
In an implementation of the embodiment of the present application, the second obtaining module 1002 is specifically configured to:
acquiring current environmental parameters, wherein the environmental parameters comprise time and/or weather;
determining historical motion distribution information of the N roads corresponding to the current environmental parameter according to a corresponding relation between the environmental parameter and historical motion distribution information of the M roads, wherein the M roads comprise the N roads, and M is a positive integer not less than N.
It should be understood that, for specific functional implementation manners of the above-mentioned functional units, reference may be made to the related description in the corresponding embodiment of fig. 2 or fig. 3, and details are not described here again.
Fig. 11 is a schematic structural diagram of another road matching device 1100 provided in the embodiment of the present application. The road matching device 1100 may be specifically the road matching server 102, the vehicle 101, or the terminal 104 in fig. 1, and may include: a processor 1101, a bus 1102, a user interface 1103, a network interface 1104, and a memory 1105. Wherein a communication bus 1102 is used to enable connective communication between these components. The user interface 1103 may optionally include a display screen, a keyboard. The network interface 1104 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). As shown in fig. 11, the memory 1105, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a device control application program, which may be executed when the apparatus 1100 is run.
In the road matching device 1100 shown in fig. 11, the network interface 1104 may provide a network communication function; and the processor 1101 may be configured to invoke a device control application stored in the memory 1105 to implement:
acquiring a track to be matched of a vehicle or a terminal through a network interface 1104, wherein the track to be matched comprises P positions and motion information corresponding to the P positions respectively, and P is a positive integer greater than 2;
obtaining historical motion distribution information of N roads, wherein the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer;
and determining the road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions respectively.
In an implementation of the embodiment of the present application, when the executed historical motion distribution information of each road includes Q regions on each road and statistical motion information corresponding to the Q regions, and the statistical motion information corresponding to a first region is obtained according to the motion information statistics of the historical track in the first region, the processor 1101 is further configured to execute:
determining P areas matched with the P positions from the Q areas on each road according to the positions of the Q areas on each road and the P positions;
and determining a road matched with the road to be matched in the N roads according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions.
In one implementation of the embodiment of the present application, the processor 1101 is further configured to:
determining the distance between each of the Q areas of each road and a first position, wherein the first position is any one of the P positions;
and determining the area matched with the first position in the Q areas on each road as the area corresponding to the minimum distance in the Q areas on each road.
In one implementation of the embodiment of the present application, the processor 1101 is further configured to:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, wherein the first weight of each road is used for determining the probability of the track to be matched on each road;
and determining the road corresponding to the maximum first weight in the N roads as the road matched with the track to be matched in the N roads.
In one implementation of the embodiment of the present application, the processor 1101 is further configured to:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions;
determining a second weight of each road according to the distance between the P positions and each road;
determining the total weight of each road according to the first weight of each road and the second weight of each road, wherein the total weight of each road is used for indicating the probability of the track to be matched on each road;
and determining the road corresponding to the maximum total weight in the N roads as the road matched with the track to be matched in the N roads.
It should be noted that the first obtaining module 1001 and the second obtaining module 1002 in fig. 10 may be implemented by the network interface 1104 in fig. 11, and the matching module 1003 and the sub-module area determining unit 1003a and the road determining unit 1003b in fig. 10 may be implemented by the processor 1104 in fig. 11.
It should be understood that the road matching device 1100 described in the embodiment of the present application may perform the description of the road matching method in the embodiment corresponding to any one of fig. 2 and fig. 3, and will not be described herein again. In addition, the beneficial effects of the same method are not described in detail.
Referring to fig. 12, fig. 12 is a schematic structural diagram of a vehicle 1200 according to an embodiment of the present disclosure. As shown in fig. 12, the vehicle 1200 may include: the processor 1201, the network interface 1204 and the memory 1205, and the road matching device 1022 may further include: a user interface 1203, and at least one communication bus 1202. Wherein a communication bus 1202 is used to enable connective communication between these components. The user interface 1203 may include a Display screen (Display) and a Keyboard (Keyboard), and optionally, the user interface 1203 may also include a standard wired interface and a standard wireless interface. The network interface 1204 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1204 may be a high-speed RAM memory or a non-volatile memory (e.g., at least one disk memory). The memory 1205 may also optionally be at least one storage device located remotely from the processor 1201 described previously. As shown in fig. 12, a memory 1201, which is a type of computer-readable storage medium, may include therein an operating system, a network communication module, a user interface module, and a device control application program.
In the road matching device 1200 shown in fig. 12, the network interface 1204 may provide a network communication function; and user interface 1203 is primarily an interface for providing input to a user; and the processor 1201 may be configured to invoke the device control application stored in the memory 1205 to perform:
acquiring a track to be matched, wherein the track to be matched comprises P positions and motion information corresponding to the P positions respectively, and P is a positive integer greater than 2;
obtaining historical motion distribution information of N roads through a network interface 1204, wherein the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer;
and determining the road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions respectively.
In a possible implementation, the processor 1201 may obtain the track to be matched by obtaining the position of the track to be matched by the positioning module 1206 at a set frequency, and obtaining motion information (such as speed, acceleration, and the like) corresponding to the position by the motion sensor 1207 on the vehicle, so as to obtain the track to be matched. The motion sensors 1207 may include velocity sensors, accelerometers, gyroscopes, magnetic sensors, and the like.
It should be noted that the first obtaining module 1001 in fig. 10 may be obtained by the positioning module 1206 and the motion sensor 1207, the second obtaining module 1002 may be implemented by the network interface 1104 in fig. 12, and the matching module 1003 and the sub-module area determining unit 1003a and the road determining unit 1003b in fig. 10 may be implemented by the processor 1104 in fig. 12.
It should be understood that the vehicle 1200 described in this embodiment of the present application may perform the description of the road matching method in the embodiment corresponding to any one of fig. 2 and fig. 3, and will not be described herein again. In addition, the beneficial effects of the same method are not described in detail.
Further, here, it is to be noted that: an embodiment of the present application further provides a computer storage medium, where the computer storage medium stores the aforementioned computer program executed by the road matching apparatus 1000 or 1100 and the vehicle 1200, and the computer program includes program instructions, and when the processor executes the program instructions, the method executed by the server in the embodiment corresponding to fig. 2 or fig. 3 can be executed, which will not be described again here.
Further, here, it is to be noted that: an embodiment of the present application further provides a computer storage medium, where the computer program executed by the road matching apparatus 1200 mentioned above is stored in the computer storage medium, and the computer program includes program instructions, and when the processor executes the program instructions, the method executed by the vehicle and the terminal in the embodiment corresponding to fig. 2 or fig. 3 can be executed, which will not be described again here.
In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in the embodiments of the computer storage medium to which the present invention relates, reference is made to the description of the method embodiments of the present invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A road matching method, comprising:
acquiring a track to be matched, wherein the track to be matched comprises P positions and motion information corresponding to the P positions respectively, and P is a positive integer greater than 2;
obtaining historical motion distribution information of N roads, wherein the historical motion distribution information of each road is obtained by statistics according to motion information of a historical track on each road, and N is a positive integer;
and determining the road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information corresponding to the P positions respectively.
2. The method according to claim 1, wherein the historical motion distribution information of each road includes Q regions on each road and statistical motion information corresponding to the Q regions, the statistical motion information corresponding to a first region is obtained by statistics according to the motion information of the historical track in the first region, the first region is any one of the Q regions, and Q is a positive integer greater than 2; the determining the road matched with the road to be matched in the N roads according to the historical motion distribution information of the N roads and the motion information respectively corresponding to the P positions comprises:
determining P areas matched with the P positions from the Q areas on each road according to the positions of the Q areas on each road and the P positions;
and determining a road matched with the road to be matched in the N roads according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions.
3. The method according to claim 2, wherein the determining P regions from the Q regions on each road that match the P positions according to the positions of the Q regions on each road and the P positions comprises:
determining the distance between each of the Q areas of each road and a first position, wherein the first position is any one of the P positions;
and determining the area matched with the first position in the Q areas on each road as the area corresponding to the minimum distance in the Q areas on each road.
4. The method according to claim 2, wherein the determining, according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, a road that matches the road to be matched in the N roads includes:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, wherein the first weight of each road is used for determining the probability of the track to be matched on each road;
and determining the road corresponding to the maximum first weight in the N roads as the road matched with the track to be matched in the N roads.
5. The method according to claim 2, wherein the determining, according to the similarity between the statistical motion information corresponding to the P regions determined in each road and the motion information corresponding to the P positions, a road that matches the road to be matched in the N roads includes:
determining a first weight of each road according to the similarity between the statistical motion information corresponding to the P areas determined in each road and the motion information corresponding to the P positions;
determining a second weight of each road according to the distance between the P positions and each road;
determining the total weight of each road according to the first weight of each road and the second weight of each road, wherein the total weight of each road is used for indicating the probability of the track to be matched on each road;
and determining the road corresponding to the maximum total weight in the N roads as the road matched with the track to be matched in the N roads.
6. The method according to any one of claims 1-5, wherein before obtaining the historical motion distribution information of the N roads, the method further comprises:
and determining a first area comprising the P positions according to the P positions of the track to be matched, wherein the N roads are roads in the first area.
7. The method according to any one of claims 1 to 5, wherein the obtaining of the historical motion distribution information of the N roads comprises:
acquiring current environmental parameters, wherein the environmental parameters comprise time and/or weather;
determining historical motion distribution information of the N roads corresponding to the current environmental parameter according to a corresponding relation between the environmental parameter and historical motion distribution information of the M roads, wherein the M roads comprise the N roads, and M is a positive integer not less than N.
8. A road matching device, characterized in that it comprises means for implementing the method according to any of claims 1-7.
9. A road matching device comprising a processor and a memory, said processor being coupled to said memory, wherein said memory is adapted to store program code and said processor is adapted to invoke said program code to implement the method of any of claims 1-8.
10. A computer-readable storage medium, in which a computer program or computer instructions are stored which, when executed, implement the method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910866064.2A CN110793531B (en) | 2019-09-12 | 2019-09-12 | Road matching method and device and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910866064.2A CN110793531B (en) | 2019-09-12 | 2019-09-12 | Road matching method and device and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110793531A true CN110793531A (en) | 2020-02-14 |
| CN110793531B CN110793531B (en) | 2022-08-02 |
Family
ID=69427476
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910866064.2A Active CN110793531B (en) | 2019-09-12 | 2019-09-12 | Road matching method and device and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110793531B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111595353A (en) * | 2020-04-26 | 2020-08-28 | 北京大学 | Real-time map matching method based on GPU and Spark mixed parallel computing architecture |
| CN111623787A (en) * | 2020-03-16 | 2020-09-04 | 蘑菇车联信息科技有限公司 | Distributed data acquisition method and device based on Internet vehicle machine |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100114484A1 (en) * | 2007-03-27 | 2010-05-06 | Nec Corporation | Map matching system, map matching method and program |
| CN104596526A (en) * | 2014-04-17 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Positioning point matching method and positioning point matching apparatus |
| CN105241464A (en) * | 2015-10-16 | 2016-01-13 | 江苏省电力公司苏州供电公司 | Fast matching method of crane track map |
| CN106023587A (en) * | 2016-05-25 | 2016-10-12 | 电子科技大学 | Track data road network precise matching method based on multi-information fusion |
| CN107796407A (en) * | 2016-09-06 | 2018-03-13 | 高德软件有限公司 | A kind of map-matching method and device |
-
2019
- 2019-09-12 CN CN201910866064.2A patent/CN110793531B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100114484A1 (en) * | 2007-03-27 | 2010-05-06 | Nec Corporation | Map matching system, map matching method and program |
| CN104596526A (en) * | 2014-04-17 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Positioning point matching method and positioning point matching apparatus |
| CN105241464A (en) * | 2015-10-16 | 2016-01-13 | 江苏省电力公司苏州供电公司 | Fast matching method of crane track map |
| CN106023587A (en) * | 2016-05-25 | 2016-10-12 | 电子科技大学 | Track data road network precise matching method based on multi-information fusion |
| CN107796407A (en) * | 2016-09-06 | 2018-03-13 | 高德软件有限公司 | A kind of map-matching method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111623787A (en) * | 2020-03-16 | 2020-09-04 | 蘑菇车联信息科技有限公司 | Distributed data acquisition method and device based on Internet vehicle machine |
| CN111595353A (en) * | 2020-04-26 | 2020-08-28 | 北京大学 | Real-time map matching method based on GPU and Spark mixed parallel computing architecture |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110793531B (en) | 2022-08-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11562168B2 (en) | Clustering for K-anonymity in location trajectory data | |
| EP3673407B1 (en) | Automatic occlusion detection in road network data | |
| Mohamed et al. | Accurate real-time map matching for challenging environments | |
| US10373077B2 (en) | Accurately determining real time parameters describing vehicle motion based on multiple data sources | |
| US10546043B1 (en) | Triangulation for K-anonymity in location trajectory data | |
| US20200018607A1 (en) | Map matched aggregation for k-anonymity in trajectory data | |
| CN105841707B (en) | Navigation system using map mechanism and method of operation thereof | |
| US20210256838A1 (en) | Method, apparatus, and system for automatic verification of road closure reports | |
| US11187542B2 (en) | Trajectory time reversal | |
| US11237007B2 (en) | Dangerous lane strands | |
| US11042648B2 (en) | Quantification of privacy risk in location trajectories | |
| US11049390B2 (en) | Method, apparatus, and system for combining discontinuous road closures detected in a road network | |
| US11704897B2 (en) | Lane count estimation | |
| US10401181B2 (en) | Detection of travel mode associated with computing devices | |
| US10598499B2 (en) | Method and device for accelerated map-matching | |
| US11348452B2 (en) | Method, apparatus, and system for automatic closure verification using multiple possible vehicle paths | |
| EP3823321A1 (en) | Method, apparatus, and system for detecting joint motion | |
| CN113950611B (en) | Method and data processing system for predicting road properties | |
| CN110793531B (en) | Road matching method and device and readable storage medium | |
| Alrassy et al. | OBD-data-assisted cost-based map-matching algorithm for low-sampled telematics data in urban environments | |
| KR20210079237A (en) | How to provide additional commands for difficult maneuvers during navigation | |
| KR20180034645A (en) | Method and control unit for building a database and predicting the path of a vehicle | |
| US20220207993A1 (en) | Method, apparatus, and system for verifying a lane closure using probe data | |
| Sharma et al. | Accurate real-time location map matching algorithm for large scale trajectory data | |
| CN117706478B (en) | Positioning drift identification method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 40021923 Country of ref document: HK |
|
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |