CN113008246B - Map matching method and device - Google Patents
Map matching method and device Download PDFInfo
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- CN113008246B CN113008246B CN201911307525.9A CN201911307525A CN113008246B CN 113008246 B CN113008246 B CN 113008246B CN 201911307525 A CN201911307525 A CN 201911307525A CN 113008246 B CN113008246 B CN 113008246B
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- 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
Abstract
The embodiment of the application discloses a map matching method and a map matching device. One embodiment of the above method includes: track point information generated by a target object in a driving process is obtained, wherein the track point information comprises the position of a track point and the speed corresponding to the track point; determining a plurality of road sections of the area to which the target object belongs according to the positions of the track points; acquiring a historical road condition information set, wherein the historical road condition information set comprises historical road condition information of a plurality of road sections; and determining the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set. The method and the device can more accurately determine the road section to which the user belongs, and improve the accuracy of map matching.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a map matching method and device.
Background
Map matching refers to a process of matching a coordinate sequence of a vehicle in travel to an appropriate road section in a road network. Typically, the inputs for map matching include a sequence of vehicle locations and a base road network. The vehicle position sequence is usually a GPS positioning point or a WIFI positioning point and comprises information such as longitude and latitude coordinates, speed and angles of the points; the basic road network generally reflects road traffic information, and mainly comprises road attributes, road node attributes, road geometric shapes, road intersection rules and the like.
The conventional map matching strategy can achieve a good effect in most scenes, but the matching accuracy rate still has a space for improving scenes such as parallel roads (main and auxiliary roads, overhead up and down) and the like. The mismatching of the parallel paths may cause the calculation of the road condition to be incorrect, the calculation of ETA (estimated arrival time by the user) to be incorrect, and the wrong judgment of the traffic condition of the road.
Disclosure of Invention
The embodiment of the application provides a map matching method and device.
In a first aspect, an embodiment of the present application provides a map matching method, including: acquiring track point information generated by a target object in a driving process, wherein the track point information comprises the position of a track point and the speed corresponding to the track point; determining a plurality of road sections of the area to which the target object belongs according to the positions of the track points; acquiring a historical road condition information set, wherein the historical road condition information set comprises historical road condition information of the plurality of road sections; and determining the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set.
In some embodiments, the acquiring the historical traffic information set includes: filtering abnormal road condition information in the historical road condition information of each road section, and determining a historical road condition information set; and/or merging the historical road condition information of each road section to determine a historical road condition information set.
In some embodiments, the historical traffic information includes at least three traffic levels; and the above method further comprises: and determining the historical road condition information with the hopping road condition grades of the adjacent time units as the abnormal road condition information.
In some embodiments, the above method further comprises: and determining the historical road condition information with the driving frequency lower than a preset threshold value in a preset time period as abnormal road condition information.
In some embodiments, the merging the historical traffic information of each road segment includes: merging historical road condition information of a plurality of connected road sections with the same road condition grade; and/or merging the historical road condition information of the road sections of the same road grade.
In some embodiments, the historical traffic information set includes historical driving speeds of the road segments at historical times; and determining the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set, wherein the determining comprises: establishing an index table according to historical road condition information of each road section at each historical moment; and determining the road section to which the target object belongs according to the speed corresponding to the track point and the index table.
In some embodiments, the determining the road segment to which the target object belongs according to the speed corresponding to the track point and the index table includes: for each track point, determining a road section corresponding to the track point according to the speed corresponding to the track point and the index table; and determining the road section to which the target object belongs according to the road sections corresponding to the continuous multiple track points.
In a second aspect, an embodiment of the present application provides a map matching apparatus, including: the information acquisition unit is configured to acquire track point information generated by the target object in the driving process, wherein the track point information comprises the position of a track point and the speed corresponding to the track point; a road section determining unit configured to determine a plurality of road sections of an area to which the target object belongs, based on the positions of the track points; a set acquisition unit configured to acquire a historical traffic information set including historical traffic information of the plurality of road segments; and the map matching unit is configured to determine the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set.
In some embodiments, the set obtaining unit is further configured to: filtering abnormal road condition information in the historical road condition information of each road section, and determining a historical road condition information set; and/or combining the historical road condition information of each road section to determine a historical road condition information set.
In some embodiments, the historical traffic information includes at least three traffic levels; and the apparatus further comprises an anomaly determination unit configured to: and determining the historical road condition information with the hopping road condition grades of the adjacent time units as the abnormal road condition information.
In some embodiments, the apparatus further comprises an anomaly determination unit configured to: and determining the historical road condition information with the driving frequency lower than a preset threshold value in a preset time period as abnormal road condition information.
In some embodiments, the set obtaining unit is further configured to: merging historical road condition information of a plurality of connected road sections with the same road condition grade; and/or merging the historical road condition information of the road sections of the same road grade.
In some embodiments, the historical traffic information set includes historical driving speeds of the road segments at historical times; and the map matching unit is further configured to: establishing an index table according to historical road condition information of each road section at each historical moment; and determining the road section to which the target object belongs according to the speed corresponding to the track point and the index table.
In some embodiments, the map matching unit is further configured to: for each track point, determining a road section corresponding to the track point according to the speed corresponding to the track point and the index table; and determining the road section to which the target object belongs according to the road sections corresponding to the continuous multiple track points.
In a third aspect, an embodiment of the present application provides a server, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any of the embodiments of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, which when executed by a processor implements the method as described in any one of the embodiments of the first aspect.
According to the map matching method and the map matching device provided by the embodiment of the application, the real-time driving information of the target object can be obtained firstly, and the real-time driving information comprises the real-time driving position and the real-time driving speed. And determining a plurality of road sections of the area to which the target object belongs according to the real-time driving position. Then, a historical traffic information set may be obtained, where the historical traffic information set includes historical traffic information for multiple road segments. And finally, determining the road section to which the target object belongs based on the real-time driving speed and the historical road condition information set. The method of the embodiment can more accurately determine the road section to which the user belongs, and improves the accuracy of map matching.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a map matching method according to the present application;
FIG. 3 is a flow diagram of another embodiment of a map matching method according to the present application;
FIG. 4a is a schematic diagram of an application scenario of a map matching method according to the present application;
FIG. 4b is a schematic diagram of another application scenario of the map matching method according to the present application;
FIG. 5 is a schematic block diagram of one embodiment of a map matching apparatus according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing a server according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the map matching method or map matching apparatus of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, a network 103, and a server 104. The network 103 serves as a medium for providing communication links between the terminal devices 101, 102 and the server 104. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 101, 102 to interact with the server 105 over the network 103 to receive or send messages or the like. Various communication client applications, such as a map application, a navigation application, a web browser application, etc., may be installed on the terminal devices 101 and 102.
The terminal apparatuses 101 and 102 may be hardware or software. When the terminal devices 101, 102 are hardware, they may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, in-vehicle terminals, and the like. When the terminal apparatuses 101 and 102 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 104 may be a server providing various services, such as a background map server for map matching the location of the terminal devices 101, 102. The background map server may analyze data such as track point information of the terminal devices 101 and 102, and feed back a processing result (for example, a road segment to which the background map server belongs) to the terminal devices 101 and 102.
The server 104 may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server 104 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the map matching method provided in the embodiment of the present application is generally executed by the server 104, and accordingly, the map matching apparatus is generally disposed in the server 104.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a map matching method according to the present application is shown. The map matching method of the embodiment comprises the following steps:
In this embodiment, an execution subject of the map matching method (for example, the server 104 shown in fig. 1) may acquire track point information generated by the target object during the driving process in a wired connection manner or a wireless connection manner. The target object may be a terminal held by the user, a vehicle driven by the user, an autonomous vehicle or other drivable device (such as an automated guided vehicle, etc.). The target object can generate a plurality of track points in the driving process, and the information of each track point comprises the position of the track point and the speed corresponding to the track point. The target object can send track point information to the execution main body in real time.
And step 202, determining a plurality of road sections of the area to which the target object belongs according to the positions of the track points.
After the execution main body obtains the track point information of the target object, a plurality of road sections of the area to which the target object belongs can be determined according to the positions of the track points. For example, the execution subject may use the position of the latest track point as the center of a circle and a surrounding 50 m area as the area to which the target object belongs. Alternatively, the execution subject may take the position of the latest trajectory point as the center and take an area within a preset range as the area to which the target object belongs. After determining the area to which the target object belongs, the executing entity may determine a plurality of road segments within the area to which the executing entity belongs. Here, the link may refer to a path that the user can travel in an area where the user is located. Typically, the length of the road segment is short, for example 200 meters.
The execution main body can also obtain a historical road condition information set. Here, the historical traffic information set includes historical traffic information of a plurality of road segments. It is understood that the plurality of road segments involved in the historical road condition information set includes the plurality of road segments determined in step 202. The historical road condition information may include historical time, historical driving speed at the historical time, and road condition grades.
In some optional implementation manners of this embodiment, the historical traffic information set may be obtained through the following steps not shown in fig. 2: and filtering abnormal road condition information in the historical road condition information of each road section, and determining a historical road condition information set. And/or combining the historical road condition information of each road section to determine a historical road condition information set.
In this implementation, the execution main body may filter abnormal traffic information in the historical traffic information of each road segment to obtain a historical traffic information set. Or, the execution main body may merge the historical traffic information of each road segment to obtain a historical traffic information set. It can be understood that, by filtering the abnormal traffic information, the accuracy of determining the road section to which the target object belongs by using the historical traffic information can be improved. By combining the historical road condition information, the calculation amount can be reduced, and the calculation efficiency is improved.
In some optional implementations of this embodiment, the historical traffic information may include at least three traffic classes. For example, four road condition levels may be included, namely "clear", "slow", "congested" and "heavily congested". The execution main body may use the history road condition information of the road condition level hopping of the adjacent time unit as the abnormal road condition information. For example, the traffic information is issued once per minute, and the time unit is minutes. The adjacent time units are the previous minute and the next minute. And if the road condition grade of the previous minute jumps from the road condition grade of the next minute, the historical road condition information is considered to be wrong. Here, the skip may refer to a skip from a certain road condition class to a road condition class separated from the road condition class by at least one class, for example, a skip from "clear" to "congested" or "heavily congested", or a skip from "heavily congested" to clear "or" slow ".
In some optional implementation manners of this embodiment, the execution main body may further use historical traffic information, in which the driving frequency is lower than a preset threshold value in a preset time period, as the abnormal traffic information.
In this implementation, the execution subject may remove the historical traffic information of the road section with a low frequency of travel. Generally, a road section with a low driving frequency is a remote path, and in order to improve the calculation efficiency, the historical road condition information of the road section can be directly deleted as the abnormal road condition information.
In some optional implementation manners of this embodiment, the execution principal may merge the historical road condition information of the eligible road segments. For example, the execution subject may merge the historical traffic information by the following steps not shown in fig. 2: and merging the historical road condition information of a plurality of connected road sections with the same road condition grade. And/or merging the historical road condition information of the road sections of the same road grade.
In this implementation, the execution subject may merge historical traffic information of a plurality of connected road segments of the same traffic class. For example, the road condition grades of the section a, the section B and the section C are the same, and the section a, the section B and the section C are connected. It should be noted that the connection relationship between the road segments can be obtained by a road network, and a specific road network can be obtained from map data. The execution principal may merge the historical road condition information for road segment a, road segment B, and road segment C. The execution subject can also merge the historical road condition information of the road sections of the same road grade. For example, the execution subject may merge historical traffic information of a highway, an overhead road, and a city backbone road.
Due to the fact that the lengths of the road sections are short, the historical road condition information of each road section at each historical moment can form massive data. The amount of calculation can be effectively reduced by filtering and/or combining the historical road condition information.
And 204, determining the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set.
After the execution main body obtains the historical road condition information set, the road section to which the target object belongs can be determined based on the speed corresponding to the track point. Specifically, the execution body may determine the road segment to which the target object belongs by using the position of the track point and the hidden markov model, in combination with the speed and the historical road condition information set corresponding to the track point. Hidden markov models are models used for map matching. Researchers generally consider map matching as a problem for sequence labeling, given a sequence of sites to determine the path segments they match, the most likely sequence is output by a double stochastic process. Hidden markov models contain five basic elements, defined as follows:
λ＝{S,V,A,B,π}。
wherein S represents a state variable set, V represents an observation variable set, A represents a state transition matrix, B represents observation probability distribution, and pi represents an initial state probability vector.
In the hidden markov model, generally, a method of calculating observation probability and state transition probability is used to find a sequence of hidden states which is most likely to generate a certain specific output sequence, and finally, a Viterbi algorithm is used to solve a probability maximum path, and the path is regarded as a user real path.
In this embodiment, the hidden markov model may be utilized, and meanwhile, the speed corresponding to the trajectory point and the historical road condition information set may be combined to determine the road segment to which the target object belongs. That is, the method of the present embodiment considers not only the projection distance and direction of the trajectory point but also the similarity of the velocity, compared to the conventional map matching method using only the hidden markov model. If the projection distance and the direction of the track point determine a plurality of candidate road sections, the similarity between the speed of the target object and the speed included in the road condition information of each candidate road section is used as one of the factors of the emission probability of the hidden Markov model, so that the map matching result is more accurate.
The map matching method provided by the above embodiment of the present application may first obtain real-time driving information of the target object, where the real-time driving information includes a real-time driving position and a real-time driving speed. And determining a plurality of road sections of the area to which the target object belongs according to the real-time driving position. Then, a historical traffic information set may be obtained, where the historical traffic information set includes historical traffic information for multiple road segments. And finally, determining the road section to which the target object belongs based on the real-time driving speed and the historical road condition information set. The method of the embodiment can more accurately determine the road section to which the user belongs, and improves the accuracy of map matching.
With continued reference to FIG. 3, a flow 300 of another embodiment of a map matching method according to the present application is shown. As shown in fig. 3, the map matching method of the present embodiment may include the following steps:
And step 302, determining a plurality of road sections of the area to which the target object belongs according to the positions of the track points.
The principle of step 301 to step 303 is similar to that of step 201 to step 203, and is not described herein again.
And step 304, establishing an index table according to the historical road condition information of each road section at each historical moment.
After the execution main body obtains the historical traffic information set, the historical traffic information set may include historical driving speeds of the road segments at the historical times. The execution subject can establish an index table according to the historical road condition information of each road section at each historical moment. Specifically, the executive agent may determine historical road condition information for each road segment at time 1, time 2, and time 3 … …. In order to make the index table more easily retrievable, the execution subject may also group the roads, for example, each road (e.g., a nameless road on a map) as a group, intersections composed of a plurality of connected road segments as a small group, and super roads as a small group.
And 305, for each track point, determining a road segment corresponding to the track point according to the speed corresponding to the track point and the index table.
After the execution main body determines the index table, the probability that the target object belongs to each road section can be determined according to the speed corresponding to the track point and the index table. Specifically, the execution subject may compare the acquisition time of the latest track point with each time in the index table, and determine the historical driving speed corresponding to each road segment at the time closest to the acquisition time. If the historical driving speed of a certain road section is the same as the speed of the latest track point or the difference value is within a preset range, the target object is considered to be highly likely to belong to the road section. The speed corresponding to each track point and the historical driving speed corresponding to each road section at each moment are input into the hidden Markov model, and the probability that each track point belongs to each road section can be obtained. The execution subject may use the road segment corresponding to the maximum probability value as the road segment corresponding to the track point.
In practical application, there may be a case where different track points correspond to different road segments or a case where an accurate result cannot be obtained according to a road segment corresponding to each track point, and in order to improve the accuracy of map matching, the execution subject may comprehensively consider road segments corresponding to a plurality of continuous track points.
And step 306, determining the road section to which the target object belongs according to the road sections corresponding to the continuous multiple track points.
The execution subject may determine the road segment to which the target object belongs according to the road segments corresponding to the N continuous track points. Here, N may be set according to an actual application scenario. For example, for two parallel roads (generally including a main road and a sub road, on an overhead road and under an overhead road), it may not be possible to determine whether the target object is driving on the main road or the sub road at all, depending on the driving speed of the track point and the historical road condition information of each road segment. The execution main body can enter the auxiliary road only according to the position of the 1 st track point, namely the position of the starting point of the target object, and the starting point has no main road entrance all the time. In this way, it can be determined that the target object is currently driving on the secondary road. This is the case where a priori knowledge of the trace points is used for map matching. In addition, the executive body can also utilize posterior knowledge of the track points for map matching. That is, the executing agent cannot determine whether the target object is traveling on the main road or the sub road at all. Map matching can be aided by the next points of track. For example, next, if the track point of the target object is located on a small road connected to the auxiliary road, the execution subject may determine that the target object is located on the auxiliary road for driving.
With continued reference to fig. 4a, fig. 4a is a schematic diagram of an application scenario of the map matching method according to the present embodiment. In the application scenario of fig. 4a, the user drives the vehicle to travel at an elevated ramp entrance, which includes multiple parallel roads, and the map matching accuracy using the positions of the track points and the road network is low due to the close proximity of the road sections. At the moment, the speeds corresponding to the track points acquired by the server are respectively 60km/h, 10km/h, 15km/h, 20km/h and 40 km/h. The execution subject can judge that the track of the vehicle driven by the user is the track from the overhead to the elevated under the ramp by combining the historical road condition information of each road section (the overhead main road, the overhead sub-main road and the overhead sub-auxiliary road).
With continuing reference to fig. 4b, fig. 4b is a schematic diagram of another application scenario of the map matching method according to the present embodiment. In the application scenario of fig. 4b, the user drives on the main road, but the main road is congested, and the driving speed is lower than that of the auxiliary road. At the moment, the user bypasses from the main road to the auxiliary road and bypasses back to the main road through the main and auxiliary road connecting road. The speeds of the server which are acquired by the vehicle at the corresponding track points are respectively 10km/h, 30km/h, 20km/h and 45 km/h. In practice, such a handover cannot necessarily be intuitively felt, since the accuracy of GPS positioning is typically 5-10 meters. If map matching is carried out by simply utilizing the position of the track point and the road network, the conclusion that the track runs on the main road all the time can be obtained. However, the track of the vehicle driven by the user can be obtained by combining the road condition information, namely that the vehicle is connected with the road through the main road and the auxiliary road, and bypasses from the main road to the auxiliary road and bypasses back to the main road.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of a map matching apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the map matching apparatus 500 of the present embodiment includes: an information acquisition unit 501, a link determination unit 502, a set acquisition unit 503, and a map matching unit 504.
An information acquisition unit 501 configured to acquire track point information generated by the target object during driving. The track point information includes the position of the track point and the speed corresponding to the track point.
A road segment determining unit 502 configured to determine a plurality of road segments of an area to which the target object belongs, according to the positions of the track points.
A set acquiring unit 503 configured to acquire the historical road condition information set. The historical traffic information set comprises historical traffic information of a plurality of road sections.
And the map matching unit 504 is configured to determine a road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set.
In some optional implementations of this embodiment, the set obtaining unit 503 is further configured to: filtering abnormal road condition information in the historical road condition information of each road section, and determining a historical road condition information set; and/or merging the historical road condition information of each road section to determine a historical road condition information set.
In some optional implementations of this embodiment, the historical traffic information includes at least three traffic classes. The apparatus 500 may further comprise an anomaly determination unit, not shown in fig. 5, configured to: and determining the historical road condition information with the hopping road condition grades of the adjacent time units as the abnormal road condition information.
In some optional implementations of this embodiment, the apparatus 500 may further include an abnormality determining unit, not shown in fig. 5, configured to: and determining the historical road condition information of which the driving frequency is lower than a preset threshold value in a preset time period as abnormal road condition information.
In some optional implementations of this embodiment, the set obtaining unit 503 is further configured to: merging historical road condition information of a plurality of connected road sections with the same road condition grade; and/or merging the historical road condition information of the road sections of the same road grade.
In some optional implementations of the embodiment, the historical traffic information set includes historical driving speeds of the road segments at historical times. The map matching unit 504 is further configured to: establishing an index table according to historical road condition information of each road section at each historical moment; and determining the road section to which the target object belongs according to the speed corresponding to the track point and the index table.
In some optional implementations of the present embodiment, the map matching unit 504 is further configured to: for each track point, determining a road section corresponding to the track point according to the speed corresponding to the track point and an index table; and determining the road section to which the target object belongs according to the road sections corresponding to the continuous multiple track points.
It should be understood that the units 501 to 504 described in the map matching apparatus 500 correspond to respective steps in the method described with reference to fig. 2. Thus, the operations and features described above for the map matching method are equally applicable to the apparatus 500 and the units included therein, and are not described in detail here.
Referring now to FIG. 6, a schematic diagram of an electronic device (e.g., the server of FIG. 1) 600 suitable for use in implementing embodiments of the present disclosure is shown. The server shown in fig. 6 is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or installed from the storage means 608, or installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: track point information generated by a target object in a driving process is obtained, wherein the track point information comprises the position of a track point and the speed corresponding to the track point; determining a plurality of road sections of the area to which the target object belongs according to the positions of the track points; acquiring a historical road condition information set, wherein the historical road condition information set comprises historical road condition information of a plurality of road sections; and determining the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an information acquisition unit, a link determination unit, a set acquisition unit, and a map matching unit. The names of the units do not form a limitation on the units themselves in some cases, and for example, the information acquisition unit may also be described as a "unit that acquires track point information generated by the target object during travel".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (14)
1. A map matching method, comprising:
track point information generated by a target object in a driving process is obtained, wherein the track point information comprises the position of a track point and the speed corresponding to the track point;
determining a plurality of road sections of the region to which the target object belongs according to the positions of the track points;
acquiring a historical road condition information set, wherein the historical road condition information set comprises historical road condition information of the plurality of road sections, and the historical road condition information comprises at least three road condition grades;
determining the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set;
the method further comprises the following steps:
determining historical road condition information with hopping road condition grades of adjacent time units as abnormal road condition information, wherein the hopping road condition grade refers to that a road condition grade jumps to a road condition grade separated from the road condition grade by at least one grade;
the acquiring of the historical traffic information set comprises:
and filtering abnormal road condition information in the historical road condition information of each road section, and determining a historical road condition information set.
2. The method of claim 1, wherein the obtaining the historical traffic information set comprises:
and merging the historical road condition information of each road section to determine a historical road condition information set.
3. The method of claim 2, wherein the method further comprises:
and determining the historical road condition information with the driving frequency lower than a preset threshold value in a preset time period as abnormal road condition information.
4. The method as claimed in claim 2, wherein the merging the historical traffic information of each road segment comprises:
merging historical road condition information of a plurality of connected road sections with the same road condition grade; and/or
And merging the historical road condition information of the road sections of the same road grade.
5. The method of claim 1, wherein the set of historical road condition information includes historical travel speeds for each road segment at each historical time; and
the determining the road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set includes:
establishing an index table according to historical road condition information of each road section at each historical moment;
and determining the road section to which the target object belongs according to the speed corresponding to the track point and the index table.
6. The method of claim 5, wherein the determining the road segment to which the target object belongs according to the speed corresponding to the track point and the index table comprises:
for each track point, determining a road section corresponding to the track point according to the speed corresponding to the track point and the index table;
and determining the road section to which the target object belongs according to the road sections corresponding to the continuous multiple track points.
7. A map matching apparatus comprising:
the information acquisition unit is configured to acquire track point information generated by a target object in a driving process, wherein the track point information comprises the position of a track point and the speed corresponding to the track point;
a road section determination unit configured to determine a plurality of road sections of an area to which the target object belongs, according to the positions of the track points;
a set acquisition unit configured to acquire a historical traffic information set including historical traffic information of the plurality of road segments, wherein the historical traffic information includes at least three traffic classes;
a map matching unit configured to determine a road section to which the target object belongs based on the speed corresponding to the track point and the historical road condition information set;
the apparatus further comprises an anomaly determination unit configured to:
determining that historical road condition information with hopping road condition grades of adjacent time units is abnormal road condition information, wherein the hopping road condition grade refers to that a road condition grade hops to a road condition grade separated from the road condition grade by at least one grade;
the set acquisition unit is further configured to:
and filtering abnormal road condition information in the historical road condition information of each road section, and determining a historical road condition information set.
8. The apparatus of claim 7, wherein the set acquisition unit is further configured to:
and merging the historical road condition information of each road section to determine a historical road condition information set.
9. The apparatus of claim 8, wherein the apparatus further comprises an anomaly determination unit configured to:
and determining the historical road condition information with the driving frequency lower than a preset threshold value in a preset time period as abnormal road condition information.
10. The apparatus of claim 8, wherein the set acquisition unit is further configured to:
merging historical road condition information of a plurality of connected road sections with the same road condition grade; and/or
And merging the historical road condition information of the road sections of the same road grade.
11. The apparatus of claim 7, wherein the set of historical road condition information comprises historical driving speeds of the road segments at historical times; and
the map matching unit is further configured to:
establishing an index table according to historical road condition information of each road section at each historical moment;
and determining the road section to which the target object belongs according to the speed corresponding to the track point and the index table.
12. The apparatus of claim 11, wherein the map matching unit is further configured to:
for each track point, determining a road section corresponding to the track point according to the speed corresponding to the track point and the index table;
and determining the road section to which the target object belongs according to the road sections corresponding to the continuous multiple track points.
13. A server, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
14. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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