CN114511044A - Unmanned vehicle passing control method and device - Google Patents
Unmanned vehicle passing control method and device Download PDFInfo
- Publication number
- CN114511044A CN114511044A CN202210401483.0A CN202210401483A CN114511044A CN 114511044 A CN114511044 A CN 114511044A CN 202210401483 A CN202210401483 A CN 202210401483A CN 114511044 A CN114511044 A CN 114511044A
- Authority
- CN
- China
- Prior art keywords
- station
- cluster
- unmanned vehicle
- cluster center
- pass
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Probability & Statistics with Applications (AREA)
- Atmospheric Sciences (AREA)
- Traffic Control Systems (AREA)
Abstract
The disclosure relates to the technical field of unmanned driving, and provides a method and a device for controlling unmanned vehicle passing. The method comprises the following steps: acquiring station data corresponding to a target area; processing the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area; judging whether the unmanned vehicle can pass through the cluster center station or not through a travelable judging module based on the coordinates of the cluster center station of the station cluster; under the condition that the unmanned vehicle can pass through the cluster center station, the cluster center station is used as a target station, and the unmanned vehicle is controlled to pass through the target station; under the condition that the unmanned vehicle cannot pass through the cluster center station, judging whether other stations except the cluster center station in the station cluster allow the unmanned vehicle to pass through or not from near to far by taking the cluster center station as a starting point through a travelable judging module; and when the other stations allow the unmanned vehicles to pass through, the other stations are used as target stations, and the unmanned vehicles are controlled to pass through the target stations.
Description
Technical Field
The disclosure relates to the technical field of unmanned driving, in particular to a method and a device for controlling the passing of an unmanned vehicle.
Background
Artificial intelligence is applied to the field of vehicle driving, unmanned technologies, unmanned vehicles and other technologies or concepts are increasingly explosive, and unmanned vehicles are used in various fields. When the unmanned vehicle needs to provide one service or multiple articles for many users, the optimal scheme is that the unmanned vehicle passes through a crowd-concentrated station to provide convenience for more users. However, when there are a plurality of crowd-concentrated stations in an area where the unmanned vehicle travels, how to pick out an optimal station from the plurality of crowd-concentrated stations so that the unmanned vehicle passes through the optimal station. The method commonly used at present is to process data of a plurality of crowd-concentrated stations in an area by using a clustering algorithm to obtain a station cluster, and a cluster center station of the station cluster is used as a station through which an unmanned vehicle passes (the cluster center station can be understood as a cluster center point of the station cluster). However, the cluster center point of the station cluster may not be the optimal station through which the unmanned vehicle should pass, and it can be said that there may be a case where the unmanned vehicle cannot pass through the cluster center station of the station cluster.
In the course of implementing the disclosed concept, the inventors found that there are at least the following technical problems in the related art: the existing method for determining the optimal station through which an unmanned vehicle passes has the problem of low accuracy.
Disclosure of Invention
In view of this, the present disclosure provides an unmanned vehicle passing control method, an apparatus, an electronic device, and a computer-readable storage medium, so as to solve the problem in the prior art that the accuracy is low in the current method for determining an optimal station through which an unmanned vehicle should pass.
In a first aspect of the disclosed embodiments, a method for controlling the passage of an unmanned vehicle is provided, which includes: acquiring site data corresponding to a target area, wherein the site data comprise coordinates of a plurality of crowd-concentrated sites; processing the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area; judging whether the unmanned vehicle can pass through the cluster center station or not through a travelable judging module based on the coordinates of the cluster center station of the station cluster; under the condition that the unmanned vehicle can pass through the cluster center station, the cluster center station is used as a target station, and the unmanned vehicle is controlled to pass through the target station; under the condition that the unmanned vehicle cannot pass through the cluster center station, judging whether other stations except the cluster center station in the station cluster allow the unmanned vehicle to pass through or not from near to far by taking the cluster center station as a starting point through a travelable judging module; and when the other stations allow the unmanned vehicles to pass through, the other stations are used as target stations, and the unmanned vehicles are controlled to pass through the target stations.
In a second aspect of the embodiments of the present disclosure, there is provided an unmanned vehicle passage control apparatus, including: the acquisition module is configured to acquire station data corresponding to a target area, wherein the station data comprises coordinates of a plurality of crowd-concentrated stations; the processing module is configured to process the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area; the first judgment module is configured to judge whether the unmanned vehicle can pass through the cluster center station through the travelable judgment module based on the coordinates of the cluster center station of the station cluster; the first control module is configured to take the cluster center station as a target station and control the unmanned vehicle to pass through the target station under the condition that the unmanned vehicle can pass through the cluster center station; the second judgment module is configured to judge whether the unmanned vehicle is allowed to pass through other stations except the cluster center station in the station cluster from near to far by taking the cluster center station as a starting point through the travelable judgment module under the condition that the unmanned vehicle cannot pass through the cluster center station; and the second control module is configured to take the other stations as target stations and control the unmanned vehicles to pass through the target stations when the other stations allow the unmanned vehicles to pass through.
In a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor, implements the steps of the above-mentioned method.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: acquiring site data corresponding to a target area, wherein the site data comprise coordinates of a plurality of crowd-concentrated sites; processing the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area; judging whether the unmanned vehicle can pass through the cluster center station or not through a travelable judging module based on the coordinates of the cluster center station of the station cluster; under the condition that the unmanned vehicle can pass through the cluster center station, the cluster center station is used as a target station, and the unmanned vehicle is controlled to pass through the target station; under the condition that the unmanned vehicle cannot pass through the cluster center station, judging whether other stations except the cluster center station in the station cluster allow the unmanned vehicle to pass through from near to far by taking the cluster center station as a starting point through a travelable judging module; and when the other stations allow the unmanned vehicles to pass through, the other stations are used as target stations, and the unmanned vehicles are controlled to pass through the target stations. By adopting the technical means, the problem of low accuracy of the existing method for determining the optimal station through which the unmanned vehicle should pass in the prior art can be solved, and the accuracy of determining the optimal station through which the unmanned vehicle should pass is improved.
Drawings
To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of an unmanned vehicle passing control method provided by an embodiment of the disclosure;
fig. 3 is a schematic structural diagram of an unmanned vehicle passing control device provided in an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
An unmanned vehicle passage control method and device according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a scene schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include terminal devices 1 and 3, unmanned vehicle 2, server 4, and network 5.
The devices 1 and 3 may be hardware or software. When the terminal devices 1 and 3 are hardware, they may be various electronic devices having a display screen and supporting communication with the server 4, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like; when the terminal devices 1 and 3 are software, they may be installed in the electronic device as above. The terminal devices 1 and 3 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited by the embodiments of the present disclosure. Further, various applications, such as a data processing application, an instant messaging tool, social platform software, a search-type application, a shopping-type application, and the like, may be installed on the terminal devices 1 and 3.
The server 4 may be a server that provides various services, for example, a backend server that receives a request sent by a terminal device that establishes a communication connection with the server, and the backend server may receive and analyze the request sent by the terminal device, and generate a processing result. The server 4 may be one server, may also be a server cluster composed of a plurality of servers, or may also be a cloud computing service center, which is not limited in this disclosure.
The server 4 may be hardware or software. When the server 4 is hardware, it may be various electronic devices that provide various services to the terminal devices 1 and 3, and the unmanned vehicle 2. When the server 4 is software, it may be a plurality of software or software modules that provide various services for the terminal devices 1 and 3 and the unmanned vehicle 2, or may be a single software or software module that provides various services for the terminal devices 1 and 3 and the unmanned vehicle 2, which is not limited by the embodiment of the present disclosure.
The network 5 may be a wired network connected by a coaxial cable, a twisted pair and an optical fiber, or may be a wireless network that can interconnect various Communication devices without wiring, for example, Bluetooth (Bluetooth), Near Field Communication (NFC), Infrared (Infrared), and the like, which is not limited in the embodiment of the present disclosure.
The user can establish a communication connection with the server 4 via the terminal devices 1 and 3, and the unmanned vehicle 2 via the network 5 to receive or transmit information or the like. It should be noted that the specific types, numbers and combinations of the terminal devices 1 and 3, the unmanned vehicles 2, the server 4 and the network 5 may be adjusted according to the actual requirements of the application scenarios, and the embodiment of the present disclosure does not limit this.
Fig. 2 is a schematic flow chart of an unmanned vehicle passage control method provided by the embodiment of the disclosure. The unmanned vehicle passage control method of fig. 2 may be performed by the terminal device of fig. 1, or an unmanned vehicle or a server. As shown in fig. 2, the unmanned vehicle passage control method includes:
s201, acquiring site data corresponding to a target area, wherein the site data comprises coordinates of a plurality of crowd-concentrated sites;
s202, processing the site data by using a clustering algorithm to obtain a site cluster corresponding to a target area;
s203, judging whether the unmanned vehicle can pass through the cluster center station or not through the travelable judging module based on the coordinates of the cluster center station of the station cluster;
s204, under the condition that the unmanned vehicle can pass through the cluster center station, the cluster center station is taken as a target station, and the unmanned vehicle is controlled to pass through the target station;
s205, under the condition that the unmanned vehicle can not pass through the cluster center station, judging whether other stations except the cluster center station in the station cluster allow the unmanned vehicle to pass through or not from near to far by taking the cluster center station as a starting point through the travelable judging module;
and S206, when the other stations allow the unmanned vehicle to pass through, taking the other stations as target stations and controlling the unmanned vehicle to pass through the target stations.
The embodiment of the disclosure can be applied to a scene that an unmanned vehicle needs to provide services or articles, such as a scene that the unmanned vehicle travels, a scene that the unmanned bus and the unmanned taxi provide riding services, a scene that the unmanned vehicle provides articles (the articles may be goods or commodities, such as a cruise vending machine, etc.), and the like. A crowd-dense site may be a site with high traffic. The clustering algorithm processes data to obtain clusters is a common technology, and redundant description is omitted here. The clustering algorithm may be a commonly used clustering algorithm such as kmeans clustering. The cluster center site can understand the cluster center point of the site cluster, and because the cluster center point is the average of the coordinates of all points within a cluster, the cluster center site of the site cluster may not allow unmanned vehicles to pass (e.g., the cluster center point is a garden). The stations in the station cluster all belong to crowd-intensive stations in the station data, and the station cluster is an expression form of the station data. A stop may be broadly understood as a location, such as a bus stop board, school doorway, etc. There may be more than one site in the cluster of sites other than the cluster center site.
For example, the following steps are carried out: a cluster center station of a station cluster corresponding to the target area is A, and in the station cluster, other stations which are far away from the cluster center station from near to far are station B, station C and station D in sequence. And judging whether the unmanned vehicle can not pass through the cluster center station A by the travelable judging module, and then judging whether the stations B, C and D except the cluster center station in the station cluster allow the unmanned vehicle to pass through from near to far by taking the cluster center station as a starting point. And finding that the unmanned vehicle can pass through the station C and the station D, wherein the station C is the other station which can be judged for the first time, so that the unmanned vehicle is controlled to pass through the station C.
According to the technical scheme provided by the embodiment of the disclosure, station data corresponding to a target area is obtained, wherein the station data comprises coordinates of a plurality of crowd-concentrated stations; processing the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area; judging whether the unmanned vehicle can pass through the cluster center station or not through a travelable judging module based on the coordinates of the cluster center station of the station cluster; under the condition that the unmanned vehicle can pass through the cluster center station, the cluster center station is used as a target station, and the unmanned vehicle is controlled to pass through the target station; under the condition that the unmanned vehicle cannot pass through the cluster center station, judging whether other stations except the cluster center station in the station cluster allow the unmanned vehicle to pass through or not from near to far by taking the cluster center station as a starting point through a travelable judging module; and when the other stations allow the unmanned vehicles to pass through, the other stations are used as target stations, and the unmanned vehicles are controlled to pass through the target stations. By adopting the technical means, the problem of low accuracy of the existing method for determining the optimal station through which the unmanned vehicle should pass in the prior art can be solved, and the accuracy of determining the optimal station through which the unmanned vehicle should pass is improved.
After step S205 is executed, that is, in a case where the unmanned vehicle cannot pass through the cluster center station, the determining, by the travelable determining module, whether or not the unmanned vehicle is allowed to pass through at a station other than the cluster center station in the station cluster from near to far using the cluster center station as a starting point includes: under the condition that other stations through which the unmanned vehicle can pass do not exist in the station cluster, judging that the unmanned vehicle cannot pass through the target area; acquiring position information of a plurality of other areas near the target area; determining other target areas from the plurality of other areas based on the position information of each other area and the distance between each other area and the target area; and controlling the unmanned vehicle to pass through other areas of the target.
The other target area is an area which is closest to the unmanned vehicle and can pass through. Whether other areas of the target can pass through and whether the specific passing station are consistent with the confirmation method of whether the target area can pass through and the specific passing station in the previous embodiment.
Before step S201 is executed, that is, before the station data corresponding to the target area is acquired, the method includes: acquiring an image of each station through an image acquisition device of each station in a target area, wherein the image of each station carries coordinates of each station; acquiring user request data corresponding to each site in a target area, wherein the user request data corresponding to each site carries coordinates of each site; and determining a plurality of crowd-concentrated sites from all the sites in the target area according to the image of each site in the target area and the user request data corresponding to each site so as to obtain the site data corresponding to the target area.
The image of each station is an image of the flow of people of each station, and the user request data corresponding to each station comprises the number of users making requests of each station, such as the number of users ordering at each station (which can be a demand sheet for a certain goods or service, such as a driving sheet). According to the image of each site in the target area and the user request data corresponding to each site, a plurality of densely populated sites, namely sites with large pedestrian volume and a large number of users sending requests, are determined from all the sites in the target area.
After step S201 is executed, that is, after the station data corresponding to the target area is acquired, the method further includes: setting a preset cluster number corresponding to a clustering algorithm; processing the site data by using a clustering algorithm to obtain a plurality of site clusters of a preset cluster corresponding to the target area; determining a target station from a plurality of cluster center stations of a preset cluster through a travelable judging module based on the coordinates of the cluster center stations of each station cluster; and controlling the unmanned vehicle to pass through the target station.
The first embodiment is to determine the target station based on one station cluster, and since the cluster center station is representative, the embodiments of the present disclosure determine the target station based on a predetermined cluster of a plurality of cluster center stations without managing other stations of each station cluster except the cluster center station. If the number of the preset clusters is large enough (whether the number of the preset clusters is large enough is relative to the area of the target area and the number of densely populated stations in the target area), the number of the station clusters obtained according to the station data is sufficient, and the cluster center stations of the station clusters with the preset number of clusters are sufficient to support the determination of the target station.
Based on the coordinates of the cluster center station of each station cluster, determining a target station from a plurality of cluster center stations of a preset cluster through a travelable judging module, wherein the method comprises the following steps: generating a first station set based on a plurality of cluster center stations of a preset cluster according to the principle that each cluster center station is far away from an unmanned vehicle from near; and determining the target station through the travelable judging module based on the first station set.
The preset cluster number is 3, and the 3 cluster center stations which are far away from the unmanned vehicle are a cluster center station E, a cluster center station F and a cluster center station G respectively. The sequence of the elements in the first site set is cluster center site E, cluster center site F and cluster center site G, and the target site is determined by the travelable judging module according to the sequence.
After step S202 is executed, that is, after the station data is processed by using the clustering algorithm to obtain a station cluster corresponding to the target area, the method further includes: generating a second station set based on the station cluster according to the principle that the station distance is from near to far, wherein a cluster center station is a first station in the second station set, and the station distance is the distance between the cluster center station and other stations; determining a target station through a drivable judging module based on the second station set; and controlling the unmanned vehicle to pass through the target station.
For example, a cluster center station of a station cluster corresponding to the target area is a, and in the station cluster, a station B, a station C, and a station D are sequentially located from near to far from the cluster center station. The sequence of the elements in the second site set is cluster center site A, site B, site C and site D, and the target site is determined by the travelable judging module according to the sequence.
In executing step S203, determining, by the travelable determination module, whether the unmanned vehicle can pass through the cluster center station based on the coordinates of the cluster center station of the station cluster, includes: acquiring road condition information of a cluster center station in real time based on coordinates of the cluster center station of the station cluster; and judging whether the unmanned vehicle can pass through the cluster center station or not through the travelable judging module based on the road condition information.
The road condition information of the cluster center station comprises: traffic information at a cluster center station, traffic information, obstacle information, presence or absence of a high-accuracy map, presence or absence of a passable route, and the like. The driving judging module is an intelligent judging module and can judge whether the station allows the unmanned vehicle to pass according to the road condition information of the station. For example, if the road condition information of the cluster center station indicates that the cluster center station does not have a corresponding high-precision map or a passable path (if a lawn is in front of the cluster center station, it indicates that the cluster center station is impassable), the travelable determination module determines that the unmanned vehicle cannot pass through the cluster center station.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 3 is a schematic diagram of an unmanned vehicle passing control device provided in an embodiment of the present disclosure. As shown in fig. 3, the unmanned vehicle passage control device includes:
an obtaining module 301, configured to obtain site data corresponding to a target area, where the site data includes coordinates of a plurality of crowd-concentrated sites;
the processing module 302 is configured to process the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area;
a first judgment module 303 configured to judge, by the travelable judgment module, whether the unmanned vehicle can pass through a cluster center station based on coordinates of the cluster center station of the station cluster;
a first control module 304 configured to control the unmanned vehicle to pass through the target station by taking the cluster center station as the target station in a case where the unmanned vehicle can pass through the cluster center station;
a second determination module 305 configured to determine whether the unmanned vehicle is allowed to pass through the other stations except the cluster center station in the station cluster from near to far by using the cluster center station as a starting point through the travelable determination module, in a case that the unmanned vehicle cannot pass through the cluster center station;
and a second control module 306 configured to control the unmanned vehicle to pass through the target station by taking the other station as the target station when the other station allows the unmanned vehicle to pass through.
The embodiment of the disclosure can be applied to a scene that an unmanned vehicle needs to provide services or articles, such as a scene that the unmanned vehicle travels, a scene that a bus taking service is provided by an unmanned bus and an unmanned taxi, a scene that the unmanned vehicle provides articles (the articles may be goods, commodities, and the like), and the like. A crowd-dense site may be a site with high traffic. The clustering algorithm processes data to obtain clusters is a common technology, and redundant description is omitted here. The clustering algorithm may be a commonly used clustering algorithm such as kmeans clustering. The cluster center site can understand the cluster center point of the site cluster, and because the cluster center point is the average of the coordinates of all points within a cluster, the cluster center site of the site cluster may not allow unmanned vehicles to pass (e.g., the cluster center point is a garden). . The stations in the station cluster all belong to crowd-intensive stations in the station data, and the station cluster is an expression form of the station data. A stop may be broadly understood as a location, such as a bus stop board, school doorway, etc.
For example, the following steps are carried out: a cluster center station of a station cluster corresponding to the target area is A, and in the station cluster, other stations which are far away from the cluster center station from near to far are station B, station C and station D in sequence. And judging whether the unmanned vehicle can not pass through the cluster center station A by the travelable judging module, and then judging whether the stations B, C and D except the cluster center station in the station cluster allow the unmanned vehicle to pass through from near to far by taking the cluster center station as a starting point. And finding that the unmanned vehicle can pass through the station C and the station D, wherein the station C is the other station which can be judged for the first time, so that the unmanned vehicle is controlled to pass through the station C.
According to the technical scheme provided by the embodiment of the disclosure, station data corresponding to a target area is obtained, wherein the station data comprises coordinates of a plurality of crowd-concentrated stations; processing the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area; judging whether the unmanned vehicle can pass through the cluster center station or not through a travelable judging module based on the coordinates of the cluster center station of the station cluster; under the condition that the unmanned vehicle can pass through the cluster center station, the cluster center station is used as a target station, and the unmanned vehicle is controlled to pass through the target station; under the condition that the unmanned vehicle cannot pass through the cluster center station, judging whether other stations except the cluster center station in the station cluster allow the unmanned vehicle to pass through or not from near to far by taking the cluster center station as a starting point through a travelable judging module; and when the other stations allow the unmanned vehicles to pass through, the other stations are used as target stations, and the unmanned vehicles are controlled to pass through the target stations. By adopting the technical means, the problem of low accuracy of the existing method for determining the optimal station through which the unmanned vehicle should pass in the prior art can be solved, and the accuracy of determining the optimal station through which the unmanned vehicle should pass is improved.
Optionally, the second determining module 305 is further configured to determine that the unmanned vehicle may not pass through the target area in a case that there is no other station in the station cluster through which the unmanned vehicle may pass; acquiring position information of a plurality of other areas near the target area; determining other target areas from the plurality of other areas based on the position information of each other area and the distance between each other area and the target area; and controlling the unmanned vehicles to pass through other target areas.
The other target area is an area which is closest to the unmanned vehicle and can pass through. Whether other areas of the target can pass through and whether the specific passing station are consistent with the confirmation method of whether the target area can pass through and the specific passing station in the previous embodiment.
Optionally, the obtaining module 301 is further configured to obtain an image of each station through an image obtaining device of each station in the target area, where the image of each station carries coordinates of each station; acquiring user request data corresponding to each site in a target area, wherein the user request data corresponding to each site carries coordinates of each site; and determining a plurality of crowd-concentrated sites from all the sites in the target area according to the image of each site in the target area and the user request data corresponding to each site so as to obtain the site data corresponding to the target area.
The image of each station is an image of the flow of people of each station, and the user request data corresponding to each station comprises the number of users making requests of each station, such as the number of users ordering at each station (which can be a demand sheet for a certain goods or service, such as a driving sheet). According to the image of each site in the target area and the user request data corresponding to each site, a plurality of densely populated sites, namely sites with large pedestrian volume and a large number of users sending requests, are determined from all the sites in the target area.
Optionally, the processing module 302 is further configured to set a preset number of clusters corresponding to the clustering algorithm; processing the site data by using a clustering algorithm to obtain a plurality of site clusters of a preset cluster corresponding to the target area; determining a target station from a plurality of cluster center stations of a preset cluster through a travelable judging module based on the coordinates of the cluster center stations of each station cluster; and controlling the unmanned vehicle to pass through the target station.
The first embodiment is to determine the target station based on one station cluster, and since the cluster center station is representative, the embodiments of the present disclosure determine the target station based on a predetermined cluster of a plurality of cluster center stations without managing other stations of each station cluster except the cluster center station. If the number of the preset clusters is large enough (whether the number of the preset clusters is large enough is relative to the area of the target area and the number of densely populated stations in the target area), the number of the station clusters obtained according to the station data is sufficient, and the cluster center stations of the station clusters with the preset number of clusters are sufficient to support the determination of the target station.
Optionally, the processing module 302 is further configured to generate a first station set based on a plurality of cluster center stations in a preset cluster according to a principle that each cluster center station is far away from an unmanned vehicle from the near side; and determining the target station through the travelable judging module based on the first station set.
The preset cluster number is 3, and the 3 cluster center stations which are far away from the unmanned vehicle are a cluster center station E, a cluster center station F and a cluster center station G respectively. The sequence of the elements in the first site set is cluster center site E, cluster center site F and cluster center site G, and the target site is determined by the travelable judging module according to the sequence.
Optionally, the first determining module 303 is further configured to generate a second site set based on a site cluster according to a principle that a site distance is from near to far, where a cluster center site is a first site in the second site set, and the site distance is a distance between the cluster center site and another site; determining a target station through a drivable judging module based on the second station set; and controlling the unmanned vehicle to pass through the target station.
For example, a cluster center station of a station cluster corresponding to the target area is a, and in the station cluster, a station B, a station C, and a station D are sequentially located from near to far from the cluster center station. The sequence of the elements in the second site set is cluster center site A, site B, site C and site D, and the target site is determined by the travelable judging module according to the sequence.
Optionally, the first determining module 303 is further configured to obtain, in real time, traffic information of a cluster center station based on a coordinate of the cluster center station of the station cluster; and judging whether the unmanned vehicle can pass through the cluster center station or not through the travelable judging module based on the road condition information.
The road condition information of the cluster center station comprises: traffic information, pedestrian flow information, obstacle information, presence or absence of a high-precision map, a passable path, and the like at a cluster center station. The driving judging module is an intelligent judging module and can judge whether the station allows the unmanned vehicle to pass according to the road condition information of the station. For example, if the road condition information of the cluster center station indicates that the cluster center station does not have a corresponding high-precision map or a passable path (if a lawn is in front of the cluster center station, it indicates that the cluster center station is impassable), the travelable determination module determines that the unmanned vehicle cannot pass through the cluster center station.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
Fig. 4 is a schematic diagram of an electronic device 4 provided by the embodiment of the present disclosure. As shown in fig. 4, the electronic apparatus 4 of this embodiment includes: a processor 401, a memory 402 and a computer program 403 stored in the memory 402 and executable on the processor 401. The steps in the various method embodiments described above are implemented when the processor 401 executes the computer program 403. Alternatively, the processor 401 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 403.
Illustratively, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to accomplish the present disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 403 in the electronic device 4.
The electronic device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other electronic devices. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that fig. 4 is merely an example of the electronic device 4, and does not constitute a limitation of the electronic device 4, and may include more or less components than those shown, or combine certain components, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or a memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 4. Further, the memory 402 may also include both internal storage units of the electronic device 4 and external storage devices. The memory 402 is used for storing computer programs and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, and multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.
The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.
Claims (10)
1. An unmanned vehicle passage control method is characterized by comprising the following steps:
acquiring site data corresponding to a target area, wherein the site data comprise coordinates of a plurality of crowd-concentrated sites;
processing the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area;
judging whether the unmanned vehicle can pass through the cluster center station or not through a travelable judging module based on the coordinates of the cluster center station of the station cluster;
when the unmanned vehicle can pass through the cluster center station, the cluster center station is used as a target station, and the unmanned vehicle is controlled to pass through the target station;
under the condition that the unmanned vehicle cannot pass through the cluster center station, judging whether other stations except the cluster center station in the station cluster allow the unmanned vehicle to pass through or not from near to far by taking the cluster center station as a starting point through the travelable judging module;
and when the other stations allow the unmanned vehicle to pass through, taking the other stations as the target stations, and controlling the unmanned vehicle to pass through the target stations.
2. The method according to claim 1, wherein the determining whether the unmanned vehicle is allowed to pass through the stations other than the cluster center station in the station cluster from near to far by the travelable determination module with the cluster center station as a starting point in a case where the unmanned vehicle cannot pass through the cluster center station comprises:
under the condition that other stations which can be passed by the unmanned vehicle do not exist in the station cluster, judging that the unmanned vehicle cannot pass through the target area;
acquiring position information of a plurality of other areas near the target area;
determining other target areas from the plurality of other areas based on the position information of each other area and the distance between each other area and the target area;
and controlling the unmanned vehicle to pass through the other target areas.
3. The method according to claim 1, wherein before the acquiring the station data corresponding to the target area, the method comprises:
acquiring an image of each station through an image acquisition device of each station in the target area, wherein the image of each station carries the coordinates of each station;
acquiring user request data corresponding to each site in the target area, wherein the user request data corresponding to each site carries coordinates of each site;
and determining a plurality of crowd-concentrated sites from all the sites in the target area according to the image of each site in the target area and the user request data corresponding to each site so as to obtain the site data corresponding to the target area.
4. The method according to claim 1, wherein after the acquiring the station data corresponding to the target area, the method further comprises:
setting a preset cluster number corresponding to the clustering algorithm;
processing the site data by using the clustering algorithm to obtain site clusters corresponding to the target area and with the preset cluster number;
determining the target station from a plurality of cluster center stations of the preset cluster through the travelable judging module based on the coordinates of the cluster center station of each station cluster;
and controlling the unmanned vehicle to pass through the target station.
5. The method according to claim 4, wherein the determining the target station from the cluster center stations of the preset cluster through the travelable determination module based on the coordinates of the cluster center station of each station cluster comprises:
generating a first station set based on a plurality of cluster center stations of the preset cluster according to the principle that each cluster center station is far away from the unmanned vehicle from the near side;
and determining the target station through the travelable judging module based on the first station set.
6. The method according to claim 1, wherein after the station data is processed by using a clustering algorithm to obtain a station cluster corresponding to the target area, the method further comprises:
generating a second station set based on the station cluster according to a principle that station distances are from near to far, wherein a cluster center station is a first station in the second station set, and the station distance is a distance between the cluster center station and the other stations;
determining the target station through the travelable judging module based on the second station set;
and controlling the unmanned vehicle to pass through the target station.
7. The method of claim 1, wherein the determining, by a travelable determination module, whether an unmanned vehicle can pass through a cluster center station of the station cluster based on coordinates of the cluster center station comprises:
acquiring road condition information of a cluster center station of the station cluster in real time based on the coordinates of the cluster center station;
and judging whether the unmanned vehicle can pass through the cluster center station or not through a drivable judging module based on the road condition information.
8. An unmanned vehicle passage control device, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is configured to acquire station data corresponding to a target area, and the station data comprises coordinates of a plurality of crowd-concentrated stations;
the processing module is configured to process the site data by using a clustering algorithm to obtain a site cluster corresponding to the target area;
a first judgment module configured to judge whether an unmanned vehicle can pass through a cluster center station of the station cluster through a travelable judgment module based on coordinates of the cluster center station;
a first control module configured to control the unmanned vehicle to pass through the target station by taking the cluster center station as the target station in a case where the unmanned vehicle can pass through the cluster center station;
a second judging module configured to judge whether the unmanned vehicle is allowed to pass through other stations except the cluster center station in the station cluster from near to far by using the cluster center station as a starting point through the travelable judging module when the unmanned vehicle cannot pass through the cluster center station;
and the second control module is configured to take the other station as the target station and control the unmanned vehicle to pass through the target station when the other station allows the unmanned vehicle to pass through.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210401483.0A CN114511044B (en) | 2022-04-18 | 2022-04-18 | Unmanned vehicle passing control method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210401483.0A CN114511044B (en) | 2022-04-18 | 2022-04-18 | Unmanned vehicle passing control method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114511044A true CN114511044A (en) | 2022-05-17 |
| CN114511044B CN114511044B (en) | 2022-06-28 |
Family
ID=81555283
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210401483.0A Active CN114511044B (en) | 2022-04-18 | 2022-04-18 | Unmanned vehicle passing control method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114511044B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150185034A1 (en) * | 2007-01-12 | 2015-07-02 | Raj V. Abhyanker | Driverless vehicle commerce network and community |
| CN107843267A (en) * | 2017-10-25 | 2018-03-27 | 广州汽车集团股份有限公司 | The path generating method and device of construction section automatic driving vehicle |
| CN108021133A (en) * | 2017-11-29 | 2018-05-11 | 江苏若博机器人科技有限公司 | A kind of Multi-sensor Fusion high speed unmanned vehicle detects obstacle avoidance system |
| US10012735B1 (en) * | 2017-05-04 | 2018-07-03 | Loveland Innovations, LLC | GPS offset calibrations for UAVs |
| CN108734429A (en) * | 2017-04-19 | 2018-11-02 | 通用汽车环球科技运作有限责任公司 | Multimodal transportation management |
| CN110381471A (en) * | 2018-12-04 | 2019-10-25 | 北京京东尚科信息技术有限公司 | The method and apparatus for determining optimum base station for unmanned vehicle |
| CN110645983A (en) * | 2018-06-26 | 2020-01-03 | 北京京东尚科信息技术有限公司 | Path planning method, device and system for unmanned vehicle |
| CN111046895A (en) * | 2018-10-15 | 2020-04-21 | 北京京东尚科信息技术有限公司 | Method and device for determining target area |
| CN111352417A (en) * | 2020-02-10 | 2020-06-30 | 合肥工业大学 | Rapid generation method of heterogeneous multi-unmanned aerial vehicle cooperative path |
| CN111366160A (en) * | 2020-05-25 | 2020-07-03 | 深圳市城市交通规划设计研究中心股份有限公司 | Path planning method, path planning device and terminal equipment |
| CN111457923A (en) * | 2019-01-22 | 2020-07-28 | 北京京东尚科信息技术有限公司 | Path planning method, device and storage medium |
| CN111861008A (en) * | 2020-07-22 | 2020-10-30 | 劢微机器人科技(深圳)有限公司 | Unmanned vehicle and path planning method, device and readable storage medium thereof |
| CN111857160A (en) * | 2020-08-17 | 2020-10-30 | 武汉中海庭数据技术有限公司 | Unmanned vehicle path planning method and device |
-
2022
- 2022-04-18 CN CN202210401483.0A patent/CN114511044B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150185034A1 (en) * | 2007-01-12 | 2015-07-02 | Raj V. Abhyanker | Driverless vehicle commerce network and community |
| CN108734429A (en) * | 2017-04-19 | 2018-11-02 | 通用汽车环球科技运作有限责任公司 | Multimodal transportation management |
| US10012735B1 (en) * | 2017-05-04 | 2018-07-03 | Loveland Innovations, LLC | GPS offset calibrations for UAVs |
| CN107843267A (en) * | 2017-10-25 | 2018-03-27 | 广州汽车集团股份有限公司 | The path generating method and device of construction section automatic driving vehicle |
| CN108021133A (en) * | 2017-11-29 | 2018-05-11 | 江苏若博机器人科技有限公司 | A kind of Multi-sensor Fusion high speed unmanned vehicle detects obstacle avoidance system |
| CN110645983A (en) * | 2018-06-26 | 2020-01-03 | 北京京东尚科信息技术有限公司 | Path planning method, device and system for unmanned vehicle |
| CN111046895A (en) * | 2018-10-15 | 2020-04-21 | 北京京东尚科信息技术有限公司 | Method and device for determining target area |
| CN110381471A (en) * | 2018-12-04 | 2019-10-25 | 北京京东尚科信息技术有限公司 | The method and apparatus for determining optimum base station for unmanned vehicle |
| CN111457923A (en) * | 2019-01-22 | 2020-07-28 | 北京京东尚科信息技术有限公司 | Path planning method, device and storage medium |
| CN111352417A (en) * | 2020-02-10 | 2020-06-30 | 合肥工业大学 | Rapid generation method of heterogeneous multi-unmanned aerial vehicle cooperative path |
| CN111366160A (en) * | 2020-05-25 | 2020-07-03 | 深圳市城市交通规划设计研究中心股份有限公司 | Path planning method, path planning device and terminal equipment |
| CN111861008A (en) * | 2020-07-22 | 2020-10-30 | 劢微机器人科技(深圳)有限公司 | Unmanned vehicle and path planning method, device and readable storage medium thereof |
| CN111857160A (en) * | 2020-08-17 | 2020-10-30 | 武汉中海庭数据技术有限公司 | Unmanned vehicle path planning method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114511044B (en) | 2022-06-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109785611B (en) | Unmanned vehicle control method, device, server and storage medium | |
| CN110132293B (en) | Route recommendation method and device | |
| JP2022050380A (en) | Method for monitoring vehicle, apparatus, electronic device, storage medium, computer program, and cloud control platform | |
| CN110390060B (en) | Parking information query method and device, terminal, cloud server and system | |
| CN114199268A (en) | Robot navigation and guidance method and device based on voice prompt and guidance robot | |
| CN113269168A (en) | Obstacle data processing method and device, electronic equipment and computer readable medium | |
| CN114511044B (en) | Unmanned vehicle passing control method and device | |
| CN114700957B (en) | Robot control method and device with low computational power requirement of model | |
| CN110017842B (en) | Freight vehicle navigation method, terminal device and storage medium | |
| CN114186932A (en) | Cargo distribution method and device for compartment grid robot in office area | |
| CN110381471A (en) | The method and apparatus for determining optimum base station for unmanned vehicle | |
| CN114565197B (en) | Method and device for generating operation path of unmanned vehicle | |
| CN115048430A (en) | Data verification method, system, device and storage medium | |
| CN113538951A (en) | Vehicle searching method and device, computer equipment and storage medium | |
| CN114393583B (en) | Method and device for controlling equipment through robot | |
| CN114572252B (en) | Unmanned vehicle control method and device based on driving authority authentication | |
| CN114596707B (en) | Traffic control method, traffic control device, traffic control equipment, traffic control system and traffic control medium | |
| CN114012741B (en) | Control method and device of robot based on program | |
| CN114881579A (en) | Method and device for determining distribution route | |
| CN113395677B (en) | Unmanned vehicle-based interaction method and device, electronic equipment and storage medium | |
| CN114169826A (en) | Cargo distribution method and device for service robot in office area | |
| EP4180765A1 (en) | High-precision-map data collection method and system | |
| CN113656425B (en) | Electronic map updating method and device, electronic equipment, storage medium and product | |
| CN114399746A (en) | Lane positioning method and device, electronic equipment, vehicle machine and computer readable medium | |
| CN114841615A (en) | Method and device for generating task progress of accessory robot |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |