CN117419714A - Method, device, equipment and storage medium for determining target road - Google Patents
Method, device, equipment and storage medium for determining target road Download PDFInfo
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The disclosure provides a method, a device, equipment and a storage medium for determining a target road, which relate to the technical field of artificial intelligence, in particular to the technical fields of intelligent traffic, map navigation and the like. The method for determining the target road comprises the following steps: clustering the candidate track points on the candidate track to obtain target track points; the candidate track is a historical track of the charging pile, wherein the candidate track is a terminal point; generating a target track based on the target track points; obtaining the similarity between the target track and the candidate road; and determining a target road in the candidate roads based on the similarity, wherein the target road is used for guiding the electric vehicle to travel to the charging pile. The present disclosure can accurately obtain a road reaching the charging stake.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to the technical fields of intelligent traffic, map navigation and the like, and can be applied to scenes such as image processing and the like, in particular to a method, a device, equipment and a storage medium for determining a target road.
Background
Along with the rapid development of electric automobiles, the dependence of driving users on charging piles is gradually enhanced, and the charging piles are mostly positioned at the road end of an internal road, so that road network loss is serious and the driving users are hard to find. For this reason, how to determine the way to reach the charging stake is a problem to be solved.
Disclosure of Invention
The disclosure provides a method, a device, equipment and a storage medium for determining a target road.
According to an aspect of the present disclosure, there is provided a method for determining a target road, including: clustering the candidate track points on the candidate track to obtain target track points; the candidate track is a historical track of the charging pile, wherein the candidate track is a terminal point; generating a target track based on the target track points; obtaining the similarity between the target track and the candidate road; and determining a target road in the candidate roads based on the similarity, wherein the target road is used for guiding the electric vehicle to travel to the charging pile.
According to another aspect of the present disclosure, there is provided a determination apparatus of a target road, including: the clustering module is used for carrying out clustering processing on the candidate track points on the candidate track so as to obtain target track points; the candidate track is a historical track of the charging pile, wherein the candidate track is a terminal point; the generation module is used for generating a target track according to the target track points; the acquisition module is used for acquiring the similarity between the target track and the candidate road; and the determining module is used for determining a target road in the candidate roads according to the similarity, wherein the target road is used for guiding the electric vehicle to travel to the charging pile.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above aspects.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method according to any one of the above aspects.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method according to any of the above aspects.
According to the technical scheme of the disclosure, the road reaching the charging pile can be accurately obtained.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
fig. 2 is a schematic diagram of an application scenario provided according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 4 is a schematic diagram of candidate trajectory points prior to clustering provided in accordance with an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of clustered target track points provided in accordance with an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of feature data extraction provided in accordance with an embodiment of the present disclosure;
FIG. 7 is a schematic diagram of a vectorization process provided in accordance with an embodiment of the present disclosure;
FIG. 8 is a schematic diagram according to a third embodiment of the present disclosure;
fig. 9 is a schematic diagram of an electronic device for implementing a method of determining a target link according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure. The embodiment provides a method for determining a target road, which comprises the following steps:
101. clustering the candidate track points on the candidate track to obtain target track points; and the candidate track is a historical track of the charging pile, and the end point of the candidate track is the historical track of the charging pile.
102. And generating a target track based on the target track points.
103. And obtaining the similarity between the target track and the candidate road.
104. And determining a target road in the candidate roads based on the similarity, wherein the target road is used for guiding the electric vehicle to travel to the charging pile.
The historical track of the electric vehicle may be collected in advance, and the historical track of which the end point is the charging pile may be used as a candidate track.
The track is composed of track points, and the data of each track point can comprise position information (such as longitude and latitude), time information and the like, and the track points are connected in sequence (such as longitude, latitude or time) to form the track.
For the same charging pile, the number of candidate tracks is usually multiple, and the number of corresponding candidate track points is large, so that the candidate track points can be clustered for improving the processing efficiency and accuracy.
By performing clustering processing on the candidate track points, at least one cluster can be obtained, one or more candidate track points can be selected as target track points for each cluster, for example, the center point of each cluster can be used as the target track point for each cluster.
After the target track points are obtained, the target track can be composed of the target track points.
The candidate road refers to a road (link) acquired in advance, for example, a road already recorded in map data.
Further, the amount of data of the road data recorded in the map data is large, and the road data in the map data may be screened, for example, a road in the same area as the target track or having a distance between the start points and/or a distance between the end points smaller than a preset value may be selected as a candidate road.
The shapes of the target track and the candidate road are curves, and the similarity between the target track and the candidate road can be calculated by adopting a similarity calculation mode between curves.
The candidate roads are usually plural, and the similarity between the target trajectory and each candidate road can be calculated.
And aiming at a certain candidate road, if the similarity between the target track and the candidate road is larger than a preset threshold value, taking the candidate road as the target road, namely, a road for guiding the electric vehicle to travel to the charging pile.
In this embodiment, the target track is obtained based on the candidate track, and the target road is determined based on the target track, and because the candidate track is the existing historical track of the charging pile, the historical track information can be fully utilized, so that the target road reaching the charging pile is determined, and in addition, the accuracy of the target road can be improved through clustering and similarity calculation.
In order to better understand the embodiments of the present disclosure, application scenarios to which the embodiments of the present disclosure are applicable are described.
As shown in fig. 2, a map-type Application (APP) may be installed on the terminal device 201, through which a navigation service may be provided to a user. The terminal device is, for example, a mobile device (such as a mobile phone), a vehicle-mounted terminal, a wearable device, etc., and fig. 2 illustrates the vehicle-mounted terminal as an example. The server 202 may determine the target link based on the target trajectory and the candidate links. The server may be a local server or a cloud server, and may be a single server or a cluster server. The terminal device and the server may communicate over a wired network and/or a wireless network.
After the server acquires the target track, the similarity between the target track and the pre-stored candidate road can be calculated, and the target road is determined based on the similarity; the target road refers to a road reaching the charging pile, for example, the charging pile is located inside the cell, and after entering from the cell entrance, the charging pile can be reached based on the target road. And after the target road is determined, displaying through the terminal equipment. The terminal equipment can display the target road on the map APP, and can play navigation information of the target road through a voice playing device such as a loudspeaker.
In combination with the above application scenario, the present disclosure further provides the following embodiments.
Fig. 3 is a schematic diagram of a second embodiment of the present disclosure, where a method for determining a target road is provided, as shown in fig. 3, the method includes:
301. clustering the candidate track points on the candidate track to obtain target track points; and the candidate track is a historical track of the charging pile, and the end point of the candidate track is the historical track of the charging pile.
The historical track of the electric vehicle may be collected in advance, and the historical track of which the end point is the charging pile may be used as a candidate track.
As shown in fig. 4, the number of candidate track points (represented by black points) on the candidate track may be large, and thus, the candidate track points may be clustered to obtain at least one cluster.
Specifically, a Density-based clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) may be employed for the clustering process.
Further, before the clustering process, the candidate track points can be subjected to data cleaning, coordinate conversion, sampling, compression and other processes. Wherein, the data cleaning may include: abnormal points, noise points and missing values in the candidate trajectory points are detected and processed. The outlier may be detected and filtered by a distance threshold or a speed threshold, etc. The noise points are smoothed using a smoothing algorithm (sliding window averaging). The missing values are filled in by interpolation methods such as linear interpolation or spline interpolation. Coordinate conversion converts, for example, the trajectory data from an original coordinate system to an ink card holder coordinate system for ease of calculation. Sampling and compression may include: and sampling and compressing the candidate track points according to the distance and the time interval to reduce the data volume and the calculation complexity.
302. And sequencing the target track points to obtain sequenced target track points.
303. And sequentially connecting the ordered target track points to generate the target track.
Wherein, for each cluster, the center point of the cluster can be used as the target track point.
After the target track points are obtained, sorting processing can be performed on the target track points based on the time information of the target track points. For example, as shown in fig. 5, the sorted target track points are denoted by reference numerals 1 to 5.
Then, the ordered target track points may be sequentially connected to obtain a target track, where the target track is indicated by a dashed line in fig. 5.
For example, as shown in fig. 5, the target track points are indicated by 1 to 5.
In this embodiment, the target track is obtained by sorting the target track points and sequentially connecting the sorted target track points, so that the accuracy of the target track can be improved, and the accuracy of the target road can be further improved.
304. And carrying out vectorization processing on the target track to obtain a track vector.
305. And carrying out vectorization processing on the candidate roads to obtain road vectors.
306. And carrying out similarity calculation on the track vector and the road vector to obtain the similarity between the target track and the candidate road.
In this embodiment, the similarity between the target track and the candidate road is simply and efficiently obtained by performing vectorization processing on the target track and the candidate road and calculating the similarity based on the track vector and the road vector, thereby improving the determination efficiency of the target road.
The track characteristic data of the target track can be extracted for the target track; and carrying out vectorization processing on the track characteristic data by adopting a pre-trained first word vector model so as to obtain the track vector.
Extracting road feature data of a candidate road aiming at the candidate road; and carrying out vectorization processing on the road characteristic data by adopting a pre-trained second word vector model so as to obtain the road vector.
As shown in fig. 6, the track feature data may include: track point speed and time on the target track, projection point coordinates of the target track to the candidate road, projection distance of the projection point to the candidate road and the like; the road characteristic data may include: link length, traffic flow of the road, road class, road type, etc.
After the feature data is obtained, a word vector model may be used to convert the feature data into vectors.
The word vector model is, for example, a word2vec model, which may be pre-trained with existing trajectories and user behavior in the database as training samples, with reference to the context.
The word vector model may include an input layer, a mapping layer, and an output layer, which may be converted into a corresponding vector for each point.
For example, as shown in fig. 7, for the target track, the track feature data may be converted into a track vector (Traj 2 Vec), where Ti represents the target track, and the first word vector model corresponding to the target track includes: track input layer, track mapping layer and track output layer, the dimensions of track vector are represented by n, then track vector is represented by [ Ti-1, ti-2,., ti- (n-1), ti-n ]; for the candidate road, the road feature data may be converted into a road vector (Link 2 Vec), where Li represents the candidate road, and the second word vector model corresponding to the candidate road includes: the dimensions of the road vector are denoted by m, and the road vector is denoted by [ Li-1, li-2, ], li- (m-1), li-m ].
In this embodiment, by extracting the track feature data and processing the track feature data by using the word vector model to obtain the track vector, accurate track feature data can be selected for the target track, and the accuracy of the track vector is improved by using the excellent performance of the word vector model, so that the accuracy of the target road is improved.
In this embodiment, the road feature data is extracted, and the word vector model is used to process the road feature data to obtain the road vector, so that accurate road feature data can be selected for the candidate road, and the accuracy of the road vector is improved by using the excellent performance of the word vector model, so that the accuracy of the target road is improved.
After the track vector and the road vector are obtained, the similarity between the vectors can be calculated, for example, the similarity is cosine similarity, and the calculation formula is as follows:
cosine_similarity＝(T i ·L i )/(||T i ||*||L i ||)；
wherein cosine_similarity is cosine similarity, T i ·L i Is T i And L i Is a dot product (inner product); t i I is T i Is (length); l i I is L i Is a norm (length) of (a).
307. And determining a target road in the candidate roads based on the similarity, wherein the target road is used for guiding the electric vehicle to travel to the charging pile.
Wherein, for a certain candidate road L i If the target track T i And the candidate road L i The similarity between the two candidate roads is larger than a preset threshold value, and the candidate road L is determined i As a target road, i.e., a road for guiding the electric vehicle to travel to the charging pile.
Fig. 8 is a schematic diagram according to a third embodiment of the present disclosure. The present embodiment provides a target road determining apparatus, as shown in fig. 8, the apparatus 800 includes: a clustering module 801, a generating module 802, an obtaining module 803 and a determining module 804.
The clustering module 801 is configured to perform clustering processing on candidate track points on the candidate track, so as to obtain target track points; the candidate track is a historical track of the charging pile, wherein the candidate track is a terminal point; the generating module 802 is configured to generate a target track according to the target track point; the obtaining module 803 is configured to obtain a similarity between the target track and the candidate road; the determining module 804 is configured to determine a target road among the candidate roads according to the similarity, where the target road is used to guide the electric vehicle to travel to the charging pile.
In this embodiment, the target track is obtained based on the candidate track, and the target road is determined based on the target track, and because the candidate track is the existing historical track of the charging pile, the historical track information can be fully utilized, so that the target road reaching the charging pile is determined, and in addition, the accuracy of the target road can be improved through clustering and similarity calculation.
In some embodiments, the generating module 802 is further configured to: sorting the target track points to obtain sorted target track points; and sequentially connecting the ordered target track points to generate the target track.
In this embodiment, the target track is obtained by sorting the target track points and sequentially connecting the sorted target track points, so that the accuracy of the target track can be improved, and the accuracy of the target road can be further improved.
In some embodiments, the obtaining module 803 is further configured to: vectorizing the target track to obtain a track vector; vectorizing the candidate roads to obtain road vectors; and carrying out similarity calculation on the track vector and the road vector to obtain the similarity between the target track and the candidate road.
In this embodiment, the similarity between the target track and the candidate road is simply and efficiently obtained by performing vectorization processing on the target track and the candidate road and calculating the similarity based on the track vector and the road vector, thereby improving the determination efficiency of the target road.
In some embodiments, the obtaining module 803 is further configured to: extracting track characteristic data of the target track; and carrying out vectorization processing on the track characteristic data by adopting a pre-trained first word vector model so as to obtain the track vector.
In this embodiment, by extracting the track feature data and processing the track feature data by using the word vector model to obtain the track vector, accurate track feature data can be selected for the target track, and the accuracy of the track vector is improved by using the excellent performance of the word vector model, so that the accuracy of the target road is improved.
In some embodiments, the obtaining module 803 is further configured to: extracting road feature data of the candidate road; and carrying out vectorization processing on the road characteristic data by adopting a pre-trained second word vector model so as to obtain the road vector.
In this embodiment, the road feature data is extracted, and the word vector model is used to process the road feature data to obtain the road vector, so that accurate road feature data can be selected for the candidate road, and the accuracy of the road vector is improved by using the excellent performance of the word vector model, so that the accuracy of the target road is improved.
It is to be understood that in the embodiments of the disclosure, the same or similar content in different embodiments may be referred to each other.
It can be understood that "first", "second", etc. in the embodiments of the present disclosure are only used for distinguishing, and do not indicate the importance level, the time sequence, etc.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 9 shows a schematic block diagram of an example electronic device 900 that may be used to implement embodiments of the present disclosure. Electronic device 900 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the electronic device 900 includes a computing unit 901 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 902 or a computer program loaded from a storage unit 909 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.
A number of components in the electronic device 900 are connected to the I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, or the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, an optical disk, or the like; and a communication unit 909 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunications networks.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 901 performs the respective methods and processes described above, for example, a determination method of a target link. For example, in some embodiments, the method of determining a target link may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the above-described determination method of the target link may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the method of determining the target link by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (13)
1. A method of determining a target link, comprising:
clustering the candidate track points on the candidate track to obtain target track points; the candidate track is a historical track of the charging pile, wherein the candidate track is a terminal point;
generating a target track based on the target track points;
obtaining the similarity between the target track and the candidate road;
and determining a target road in the candidate roads based on the similarity, wherein the target road is used for guiding the electric vehicle to travel to the charging pile.
2. The method of claim 1, wherein the generating a target trajectory based on the target trajectory points comprises:
sorting the target track points to obtain sorted target track points;
and sequentially connecting the ordered target track points to generate the target track.
3. The method of claim 1, wherein the obtaining the similarity between the target trajectory and a candidate road comprises:
vectorizing the target track to obtain a track vector;
vectorizing the candidate roads to obtain road vectors;
and carrying out similarity calculation on the track vector and the road vector to obtain the similarity between the target track and the candidate road.
4. A method according to claim 3, wherein said vectorizing said target trajectory to obtain a trajectory vector comprises:
extracting track characteristic data of the target track;
and carrying out vectorization processing on the track characteristic data by adopting a pre-trained first word vector model so as to obtain the track vector.
5. A method according to claim 3, wherein said vectorizing said target trajectory to obtain a trajectory vector comprises:
extracting road feature data of the candidate road;
and carrying out vectorization processing on the road characteristic data by adopting a pre-trained second word vector model so as to obtain the road vector.
6. A target road determining apparatus, comprising:
the clustering module is used for carrying out clustering processing on the candidate track points on the candidate track so as to obtain target track points; the candidate track is a historical track of the charging pile, wherein the candidate track is a terminal point;
the generation module is used for generating a target track according to the target track points;
the acquisition module is used for acquiring the similarity between the target track and the candidate road;
and the determining module is used for determining a target road in the candidate roads according to the similarity, wherein the target road is used for guiding the electric vehicle to travel to the charging pile.
7. The apparatus of claim 6, wherein the generation module is further to:
sorting the target track points to obtain sorted target track points;
and sequentially connecting the ordered target track points to generate the target track.
8. The apparatus of claim 6, wherein the acquisition module is further to:
vectorizing the target track to obtain a track vector;
vectorizing the candidate roads to obtain road vectors;
and carrying out similarity calculation on the track vector and the road vector to obtain the similarity between the target track and the candidate road.
9. The apparatus of claim 8, wherein the acquisition module is further to:
extracting track characteristic data of the target track;
and carrying out vectorization processing on the track characteristic data by adopting a pre-trained first word vector model so as to obtain the track vector.
10. The apparatus of claim 8, wherein the acquisition module is further to:
extracting road feature data of the candidate road;
and carrying out vectorization processing on the road characteristic data by adopting a pre-trained second word vector model so as to obtain the road vector.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-5.
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| CN202311141118.1A CN117419714A (en) | 2023-09-05 | 2023-09-05 | Method, device, equipment and storage medium for determining target road |
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| CN202311141118.1A CN117419714A (en) | 2023-09-05 | 2023-09-05 | Method, device, equipment and storage medium for determining target road |
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