CN114511044B - Unmanned vehicle passing control method and device - Google Patents
Unmanned vehicle passing control method and device Download PDFInfo
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- CN114511044B CN114511044B CN202210401483.0A CN202210401483A CN114511044B CN 114511044 B CN114511044 B CN 114511044B CN 202210401483 A CN202210401483 A CN 202210401483A CN 114511044 B CN114511044 B CN 114511044B
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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 process of implementing the disclosed concept, the inventors found that at least the following technical problems exist 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 disclosed embodiments provide a method and an apparatus for controlling the passage of an unmanned vehicle, 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 the optimal station through which the 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 device, 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 disclosed embodiments, 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, in which a computer program is stored, 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 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.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings used in the embodiments or the prior art descriptions will be briefly described below, 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 passage control method provided in an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an unmanned vehicle passage control device provided in an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to 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. It will be apparent, however, 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 providing various services, for example, a backend server receiving a request sent by a terminal device establishing 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 for the terminal devices 1 and 3, and the unmanned vehicle 2. When the server 4 is software, it may be multiple 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 in this disclosure.
The network 5 may be a wired network connected by a coaxial cable, a twisted pair cable, 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 specific types, numbers, and combinations of the terminal devices 1 and 3, the unmanned vehicle 2, the server 4, and the network 5 may be adjusted according to actual requirements of an application scenario, which is not limited in the embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of an unmanned vehicle passage control method provided in an embodiment of the present 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 comprise coordinates of a plurality of densely populated sites;
s202, processing the site data by using a clustering algorithm to obtain a site cluster corresponding to a target area;
s203, based on the coordinates of the cluster center station of the station cluster, judging whether the unmanned vehicle can pass through the cluster center station through the travelable judging module;
s204, under the condition that the unmanned vehicle can pass through the cluster center station, taking the cluster center station as a target station, and controlling the unmanned vehicle to pass through the target station;
s205, 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 S206, when the other stations allow the unmanned vehicles to pass through, taking the other stations as target stations and controlling the unmanned vehicles 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 densely populated site may be a site with a high volume of people. 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.
By way of example: a cluster center site of a site cluster corresponding to the target area is A, and other sites which are far away from the cluster center site in the site cluster are a site B, a site C and a site 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 are obtained, wherein the station data comprise coordinates of a plurality of densely populated 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 whether the unmanned vehicle is allowed to pass through the stations other than the cluster center station in the cluster of stations from near to far by using the cluster center station as a starting point by the travelable determining module includes: under the condition that other stations through which the unmanned vehicles can pass do not exist in the station cluster, judging that the unmanned vehicles 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 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 specifically passed station are consistent with the confirmation method of whether the target area can pass through and the specifically passed 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 a coordinate 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 drivable judging module based on the first station set.
The preset cluster number is 3, and 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 site of a site cluster corresponding to the target area is a, and in the site cluster, a site B, a site C, and a site D are sequentially located from near to far from the cluster center site. 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 in detail herein.
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 passage 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 determining module 305, configured to determine, from near to far using the cluster center station as a starting point, whether or not the other stations in the station cluster except the cluster center station allow the unmanned vehicle to pass through, by the travelable determining module, when the unmanned vehicle cannot pass through the cluster center station;
And the second control module 306 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.
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 densely populated site may be a site with a high volume of people. The clustering algorithm processes data to obtain clusters is a common technology, and is not described in detail herein. The clustering algorithm may be a commonly used clustering algorithm such as kmeans clustering. The cluster center site may 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 to mean a location such as a bus stop board, school doorway, etc.
By way of example: a cluster center site of a site cluster corresponding to the target area is A, and other sites which are far away from the cluster center site in the site cluster are a site B, a site C and a site 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 are obtained, wherein the station data comprise coordinates of a plurality of densely populated stations; processing the site data by using a clustering algorithm to obtain a site cluster corresponding to a target area; based on the coordinates of the cluster center station of the station cluster, judging whether the unmanned vehicle can pass through the cluster center station through a travelable judging module; under the condition that the unmanned vehicle can pass through the cluster center station, taking the cluster center station as a target station, and controlling the unmanned vehicle 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, taking the other stations as target stations and controlling the unmanned vehicles 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 when 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 specifically passed station are consistent with the confirmation method of whether the target area can pass through and the specifically passed 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 a coordinate 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 densely populated sites from all 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 to obtain 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 corresponding to a preset cluster of a 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 the road condition information of the cluster center station in real time based on the coordinates of the cluster center station of the station cluster; based on the road condition information, whether the unmanned vehicle can pass through the cluster center station or not is judged through the drivable judging module.
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.
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 operable 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 used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any 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 technical solution. 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 type of logical function, another division may be made in an actual implementation, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. 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.
The 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 position, 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 may be implemented in the form of hardware, or may also be implemented in the 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, all or part of the flow in the method of the above embodiments may be realized by the present disclosure, and the computer program may be stored in a computer readable storage medium to instruct related hardware, and when the computer program is executed by a processor, the steps of the above method embodiments may be realized. 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 subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
The above examples are only intended to illustrate the technical solution of the present disclosure, not to limit it; 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 vehicles to pass through, taking the other stations as the target stations, and controlling the unmanned vehicles to pass through the target stations.
2. The method of claim 1, wherein in a case that the unmanned vehicle cannot pass through the cluster center station, after the determining, by the travelable determination module, whether the unmanned vehicle is allowed to pass through by stations other than the cluster center station in the station cluster from near to far with the cluster center station as a starting point comprises:
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 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 a coordinate 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 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;
a second control module configured to control the other station as the target station to pass the unmanned vehicle when the other station allows the unmanned vehicle to pass.
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 realizes the steps of the method according to any one 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 a method according to any one of claims 1 to 7.
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