CN112258840A - Image acquisition control method, device, equipment and storage medium - Google Patents
Image acquisition control method, device, equipment and storage medium Download PDFInfo
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- CN112258840A CN112258840A CN202011146051.7A CN202011146051A CN112258840A CN 112258840 A CN112258840 A CN 112258840A CN 202011146051 A CN202011146051 A CN 202011146051A CN 112258840 A CN112258840 A CN 112258840A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/66—Remote control of cameras or camera parts, e.g. by remote control devices
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/183—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source
- H04N7/185—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source from a mobile camera, e.g. for remote control
Abstract
The application discloses a control method, a control device, control equipment and a storage medium for image acquisition, and relates to the fields of automatic driving and intelligent transportation. The specific implementation scheme is as follows: the first equipment acquires the abnormal road condition information in the driving process of the vehicle, and sends a first acquisition instruction to the unmanned aerial vehicle according to the abnormal road condition information so as to indicate the unmanned aerial vehicle to acquire road condition images, and the first equipment receives the road condition images acquired by the unmanned aerial vehicle. Above-mentioned in-process, first equipment control unmanned aerial vehicle gathers road conditions image, because unmanned aerial vehicle flies in a flexible way, can fly and reach the position that the vehicle can't arrive, consequently utilizes unmanned aerial vehicle can enlarge the field of vision of unmanned vehicle, gathers more road conditions information to make more accurate decision.
Description
Technical Field
The application relates to the technical field of control, in particular to a control method, a control device, control equipment and a storage medium for image acquisition, which can be used in the fields of automatic driving and intelligent transportation.
Background
With the development of unmanned technologies, unmanned vehicles are used in a variety of fields. For example, unmanned vehicles may be used not only to carry passengers, but also to perform tasks such as traffic routing inspection.
Unmanned car is at the in-process of traveling, can gather multiple road conditions information through perception device, for example: road information, traffic light information, obstacle information, etc., which may be used to guide an unmanned vehicle to make a decision.
Because the unmanned vehicle runs on the road surface, the view of the unmanned vehicle is limited, namely the road condition information which can be collected by the unmanned vehicle is limited, so that the accuracy of making a decision by the unmanned vehicle is poor.
Disclosure of Invention
The application provides a control method, a control device, control equipment and a storage medium for image acquisition, which are used for acquiring more road condition information so as to make more accurate decisions.
In a first aspect, the present application provides a method for controlling image acquisition, including:
the method comprises the steps that first equipment obtains road condition abnormal information in the driving process of a vehicle;
the first equipment sends a first acquisition instruction to the unmanned aerial vehicle according to the road condition abnormal information, wherein the first acquisition instruction is used for instructing the unmanned aerial vehicle to acquire a road condition image;
the first equipment receives the road condition image acquired by the unmanned aerial vehicle.
In a second aspect, the present application provides an image acquisition control apparatus, including:
the acquisition module is used for acquiring the road condition abnormal information in the driving process of the vehicle;
the sending module is used for sending a first acquisition instruction to the unmanned aerial vehicle according to the road condition abnormal information, wherein the first acquisition instruction is used for instructing the unmanned aerial vehicle to acquire a road condition image;
and the receiving module is used for receiving the road condition image acquired by the unmanned aerial vehicle.
In a third aspect, the present application provides an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the first aspects.
In a fourth aspect, the present application provides a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of the first aspects.
According to the image acquisition control method, the image acquisition control device, the image acquisition control equipment and the storage medium, the first equipment acquires the abnormal road condition information in the driving process of the vehicle and sends a first acquisition instruction to the unmanned aerial vehicle according to the abnormal road condition information so as to indicate the unmanned aerial vehicle to acquire road condition images, and the first equipment receives the road condition images acquired by the unmanned aerial vehicle. Above-mentioned in-process, first equipment control unmanned aerial vehicle gathers road conditions image, because unmanned aerial vehicle flies in a flexible way, can fly and reach the position that the vehicle can't arrive, consequently utilizes unmanned aerial vehicle can enlarge the field of vision of unmanned vehicle, gathers more road conditions information to make more accurate decision.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram of a control system for image acquisition provided herein;
fig. 2 is a schematic flowchart of a control method for image acquisition according to the present application;
fig. 3 is a schematic flowchart of another image acquisition control method provided in the present application;
fig. 4 is a schematic flowchart of another image acquisition control method provided in the present application;
fig. 5 is a schematic flowchart of another image acquisition control method provided in the present application;
FIG. 6 is a schematic view of a traffic scene provided herein;
FIG. 7 is a schematic illustration of another traffic scenario provided herein;
fig. 8 is a schematic structural diagram of a control device for image acquisition provided by the present application;
fig. 9 is a schematic structural diagram of another image acquisition control device provided in the present application;
fig. 10 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The application provides a control method, a device, equipment and a storage medium for image acquisition, which are applied to the fields of automatic driving, intelligent traffic and the like in the technical field of control. In the field, in the driving process of the unmanned vehicle, the unmanned vehicle drives on the road surface, so that the visual field of the unmanned vehicle is limited, namely the road condition information which can be acquired by the unmanned vehicle is limited, and the accuracy of making a decision by the unmanned vehicle is poor.
For example: in the process of carrying passengers by an unmanned vehicle, the situation of long-time front blockage can be met. In this scenario, as the unmanned vehicle does not know the specific blocking reason, it is difficult to make the next decision, and it may need to apply for human intervention, and it needs to consume more manpower and time cost, which affects the experience.
Another example is: the unmanned vehicle needs to acquire road condition images in a scene of executing a specific task. However, since the unmanned vehicle is running at a specific speed, the best shooting position may be missed, so that a road condition image with higher definition cannot be acquired, or a plurality of road condition images before and after a specific interval are acquired, thereby influencing the decision making of the unmanned vehicle.
In order to solve at least one of the above technical problems, the present application provides a control method for image acquisition, which utilizes the flight characteristics of an unmanned aerial vehicle and acquires road condition images by means of the unmanned aerial vehicle. Because unmanned aerial vehicle's flexibility, it can fly to the position that unmanned vehicle can't arrive, consequently unmanned aerial vehicle can enlarge unmanned vehicle's the field of vision for unmanned vehicle can acquire more road conditions image.
First, a system architecture related to the present application is described with reference to fig. 1. Fig. 1 is a schematic diagram of a control system for image acquisition provided in the present application. As shown in fig. 1, the system may include an unmanned vehicle, a drone, and a cloud server.
Sensing devices such as a camera and a radar can be arranged in the unmanned vehicle, and road condition information can be collected through the sensing devices. The unmanned aerial vehicle can be provided with a camera. Unmanned aerial vehicle can gather road conditions image under unmanned vehicle or high in the clouds server's control.
In some examples, direct communication may be between the drone and the drone. The unmanned vehicle acquires road condition information in the driving process, and when the unmanned vehicle detects abnormal road condition information, an acquisition instruction can be sent to the unmanned vehicle to instruct the unmanned vehicle to fly to a specified position (for example, a position where the unmanned vehicle cannot reach) and acquire road condition images. The unmanned aerial vehicle sends the acquired road condition images to the unmanned vehicle, so that the unmanned vehicle can acquire more road condition images, and an accurate decision can be made conveniently.
In other examples, there is no direct communication between the drone vehicle and the drone. Unmanned vehicle and unmanned aerial vehicle all communicate with high in the clouds server. The unmanned vehicle and/or the unmanned aerial vehicle continuously obtain the road condition information and report the collected road condition information to the cloud server. The cloud server analyzes the received road condition information, and when the road condition abnormal information is detected, an acquisition instruction can be sent to the unmanned aerial vehicle to instruct the unmanned aerial vehicle to fly to a specified position (for example, a position where the unmanned aerial vehicle cannot reach) to acquire a road condition image. The unmanned aerial vehicle sends the acquired road condition images to the server, so that the server can acquire more road condition images, and accurate decisions can be made conveniently.
In one possible embodiment, the drone may be a peripheral to the drone vehicle. The unmanned vehicle is provided with a parking position for the unmanned vehicle to park. The drone rests at this parking position. When the unmanned aerial vehicle receives an acquisition instruction sent by the unmanned aerial vehicle or the server, the unmanned aerial vehicle takes off from the parking position and flies to the designated position to acquire road condition images. The unmanned aerial vehicle returns to the parking position after acquiring the road condition image; or the unmanned aerial vehicle returns to the parking position when receiving the parking instruction of the unmanned aerial vehicle or the server.
Wherein, this application is berthhed at unmanned vehicle's specific position to unmanned aerial vehicle and does not do the restriction. For example, the parking position may be provided on the roof of the unmanned vehicle, or other positions. Through setting up unmanned aerial vehicle's the position of berthhing on unmanned vehicles, make things convenient for taking off and berthhing of unmanned aerial vehicle, can improve the control efficiency to unmanned aerial vehicle.
In this embodiment, unmanned vehicle and unmanned aerial vehicle can be a one-to-one relation (that is, an unmanned vehicle corresponds to an unmanned aerial vehicle), also can be many-to-one relation (that is, a plurality of unmanned vehicles correspond to an unmanned aerial vehicle), also can be a one-to-many relation (that is, an unmanned vehicle corresponds to a plurality of unmanned aerial vehicles), and this embodiment does not limit this. For convenience of description, the following embodiments are described only by taking a one-to-one relationship as an example.
The technical solution of the present application will be described in detail with reference to several specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a schematic flow chart of a control method for image acquisition according to the present application. As shown in fig. 2, the method of the present embodiment includes:
s201, the first equipment acquires road condition abnormal information in the driving process of the vehicle.
The present embodiment is applicable to the system shown in fig. 1. Wherein the vehicle may be the unmanned vehicle of fig. 1. Alternatively, the first device may be an unmanned vehicle, or an electronic device integrated in an unmanned vehicle. Optionally, the first device may also be a cloud server, or an electronic device integrated in the cloud server.
Illustratively, sensing devices such as a camera and a radar are arranged in the unmanned vehicle, and the unmanned vehicle acquires road condition information through the sensing devices in the driving process. The traffic information includes but is not limited to: traffic light information, road vehicle information, pedestrian information, road sign line information, road sign information, and the like.
The first equipment can analyze and process the road condition information acquired by the unmanned vehicle to acquire the road condition abnormal information. The abnormal road condition information may be information used for indicating that the abnormal road condition exists and the road condition image is further acquired, and includes but is not limited to: road anomalies (such as road barriers, collapse and the like), road congestion, traffic accidents, violation incidents (illegal parking incidents, red light running incidents, overspeed driving incidents) and the like.
S202, the first equipment sends a first acquisition instruction to the unmanned aerial vehicle according to the abnormal road condition information, and the first acquisition instruction is used for indicating the unmanned aerial vehicle to acquire road condition images.
In an actual application scene, the unmanned vehicle runs on the road surface, and the road condition information acquired in the running process is limited. For example, when a vehicle is jammed in front of an unmanned vehicle, the unmanned vehicle cannot acquire road condition information of a traffic jam position due to reasons such as blocking of the front vehicle, or even if the road condition information of the traffic jam position can be acquired, the acquired road condition information does not meet requirements, such as low definition. For another example, a traffic accident occurs on a road, and an evidence image of the traffic accident is acquired by the unmanned vehicle during driving. But the collected evidence images are not clear enough and need to be shot again, and the best shooting position may be missed because the unmanned vehicle is continuously driven, so that the evidence images cannot be shot again.
In this embodiment, unmanned aerial vehicle is provided with the camera. When the first device acquires the abnormal road condition information, a first acquisition instruction can be sent to the unmanned aerial vehicle according to the abnormal road condition information so as to instruct the unmanned aerial vehicle to acquire road condition images. Can understand, because unmanned aerial vehicle flies in a flexible way, unmanned aerial vehicle can fly and reach the position that unmanned vehicle can't arrive, consequently, unmanned aerial vehicle can gather more road conditions images.
Illustratively, when the abnormal road condition information acquired by the first device indicates that the unmanned aerial vehicle is blocked in front of the unmanned aerial vehicle, the first device can control the unmanned aerial vehicle to fly to a proper position to acquire a road condition image of a traffic jam position according to the abnormal road condition information.
Illustratively, when the abnormal road condition information acquired by the first device indicates that a traffic accident occurs, the first device may control the unmanned aerial vehicle to fly to a proper position according to the abnormal road condition information to acquire a road condition image of the accident location.
It should be noted that, the road condition image acquired by the unmanned aerial vehicle in this embodiment may be a single image, a continuous multi-frame image, or a video stream, which is not limited in this embodiment.
S203, the first equipment receives road condition images acquired by the unmanned aerial vehicle.
The unmanned aerial vehicle transmits the collected road condition image back to the first equipment, so that the first equipment receives the road condition image.
In some possible embodiments, the drone vehicle is provided with a parking position for the drone to park. The drone rests at this parking position. When the first device acquires the abnormal road condition information, a first acquisition instruction is sent to the unmanned aerial vehicle to instruct the unmanned aerial vehicle to take off from a parking position and acquire road condition images.
Optionally, after the first device receives the road condition image acquired by the unmanned aerial vehicle, the method may further include: the first equipment sends the instruction of berthing to unmanned aerial vehicle, and this instruction of berthing is used for instructing unmanned aerial vehicle to berth at unmanned vehicle's berth position.
In this embodiment, through set up unmanned aerial vehicle's the position of berthing on unmanned aerial vehicle, make things convenient for taking off and berthhing of unmanned aerial vehicle, can improve the control efficiency to unmanned aerial vehicle.
According to the control method for image acquisition, the first device acquires the abnormal road condition information in the driving process of the vehicle, and sends the first acquisition instruction to the unmanned aerial vehicle according to the abnormal road condition information so as to instruct the unmanned aerial vehicle to acquire road condition images, and the first device receives the road condition images acquired by the unmanned aerial vehicle. Above-mentioned in-process first equipment control unmanned aerial vehicle gathers road conditions image, because unmanned aerial vehicle flies in a flexible way, can fly and reach the position that the vehicle can't arrive, consequently utilizes unmanned aerial vehicle can enlarge unmanned vehicle's the field of vision, gathers more road conditions information to make more accurate decision.
On the basis of the above embodiment, a specific control manner of the first device for the unmanned aerial vehicle is described below with reference to fig. 3.
Fig. 3 is a schematic flowchart of another image acquisition control method provided in the present application. As shown in fig. 3, the method of the present embodiment includes:
s301, the first equipment acquires road condition abnormal information in the driving process of the vehicle.
It should be noted that the specific implementation of S301 is similar to S201 in fig. 2, and is not described herein again.
S302, the first device determines a first flight parameter and/or a first shooting parameter of the unmanned aerial vehicle according to the abnormal road condition information.
The flight parameters in this embodiment include, but are not limited to: flight height, flight speed, pose parameters and the like.
For different road condition abnormal conditions, the unmanned aerial vehicle needs to fly according to different flight parameters. Therefore, the first flight parameter of the unmanned aerial vehicle can be determined by the first device according to the abnormal road condition information.
Optionally, if the abnormal road condition information indicates that the road is abnormal, the first device determines that the flying height of the unmanned aerial vehicle is lower than a first preset height. Under the condition that the road is abnormal, the unmanned aerial vehicle can be controlled to fly below a certain height, and a close-distance road image can be shot, so that the shot image can show road details.
Optionally, if the abnormal road condition information indicates that the road is congested, the first device determines that the flying height of the unmanned aerial vehicle is higher than a second preset height. Under the condition of road congestion, the unmanned aerial vehicle can be controlled to fly above a certain height, and a long-range image is obtained by shooting, so that the road range displayed by the shot image is large.
The shooting parameters in the present embodiment include, but are not limited to: shooting angle, field of view, etc.
To the unusual condition of different road conditions, unmanned aerial vehicle needs shoot according to different shooting angle, field of vision scope. Therefore, the first shooting parameter of the unmanned aerial vehicle can be determined by the first device according to the abnormal road condition information.
For example, in the case where a road abnormality is detected, the photographing angle of the drone may be controlled toward the road ground. Under the unusual condition of road sign of detection, can control unmanned aerial vehicle's shooting angle orientation road sign. Under the condition that the traffic accident happens in the position that detects far away, can control unmanned aerial vehicle's the great field of vision scope of adoption and shoot. Under the condition that traffic accidents happen at the detected nearer position, the unmanned aerial vehicle can be controlled to shoot in a smaller visual field range.
S303, the first equipment sends a first acquisition instruction to the unmanned aerial vehicle according to the first flight parameter and/or the first shooting parameter.
The first acquisition instruction is used for instructing the unmanned aerial vehicle to fly according to the first flight parameter and acquiring road condition images. Or, the first acquisition instruction is used for instructing the unmanned aerial vehicle to acquire the road condition image according to the first shooting parameter. Or, the first acquisition instruction is used for instructing the unmanned aerial vehicle to fly according to the first flight parameter and acquiring the road condition image according to the first shooting parameter.
S304, the first equipment receives the road condition image acquired by the unmanned aerial vehicle according to the first acquisition instruction.
Optionally, after receiving the road condition image acquired by the unmanned aerial vehicle, the first device may further continue to execute subsequent S305 to S306, that is, adjust the flight parameters and shooting parameters of the unmanned aerial vehicle according to the road condition image returned by the unmanned aerial vehicle, so as to shoot a clearer road condition image.
S305, the first device determines a second flight parameter and/or a second shooting parameter of the unmanned aerial vehicle according to the road condition image.
For example, if the traffic jam position is not shot in the road condition image, the unmanned aerial vehicle can be controlled to fly to a higher position or a farther position. If the road details shown in the road condition image are not clear, the unmanned aerial vehicle can be controlled to fly to a lower position.
Illustratively, if the traffic accident information shot in the road condition image is incomplete, the unmanned aerial vehicle can be controlled to update the shooting angle, or the unmanned aerial vehicle is controlled to shoot in a larger visual field range. If the traffic accident information is shot in the road condition image, but the details of the traffic accident are not clear, the unmanned aerial vehicle can be controlled to shoot in a smaller visual field range.
S306, the first device sends a second acquisition instruction to the unmanned aerial vehicle according to the second flight parameter and/or the second shooting parameter.
The second acquisition instruction is used for instructing the unmanned aerial vehicle to fly according to the second flight parameter and acquiring road condition images. Or the second acquisition instruction is used for instructing the unmanned aerial vehicle to acquire the road condition image according to the second shooting parameter. Or the second acquisition instruction is used for instructing the unmanned aerial vehicle to fly according to the second flight parameter and acquiring the road condition image according to the second shooting parameter.
And S307, the first equipment receives the road condition image acquired by the unmanned aerial vehicle according to the second acquisition instruction.
It can be understood that the road condition image received by the first device in S307 is acquired after the flight parameters and the shooting parameters of the unmanned aerial vehicle are adjusted according to the road condition image last returned by the unmanned aerial vehicle, so that the definition of the road condition image is ensured.
In this embodiment, the first device determines the flight parameters and the shooting parameters of the unmanned aerial vehicle according to the abnormal road condition information, so that the unmanned aerial vehicle can acquire clear road condition images according to the flight parameters and the shooting parameters. Further, the first equipment adjusts the flight parameters and the shooting parameters of the unmanned aerial vehicle according to the road condition images returned by the unmanned aerial vehicle, so that the unmanned aerial vehicle can shoot clearer road condition images.
On the basis of any of the above embodiments, the following further describes the technical solution of the present application with reference to fig. 4 and 5.
Fig. 4 is a schematic flowchart of another image acquisition control method provided in the present application. This embodiment describes a specific implementation process when the first device is an unmanned vehicle or when the first device is an electronic device integrated in an unmanned vehicle. In this embodiment, unmanned vehicle has data analysis and processing function to have certain decision-making ability, unmanned vehicle carries out direct control to unmanned aerial vehicle. The embodiment can be suitable for traffic scenes with higher real-time requirements and can also be suitable for weak network environments,
as shown in fig. 4, the method of the present embodiment includes:
s401, acquiring road condition information through a sensing device during driving of the unmanned vehicle.
S402, acquiring abnormal road condition information by the unmanned vehicle according to the acquired road condition information.
And S403, sending an acquisition instruction to the unmanned aerial vehicle according to the road condition abnormal information by the unmanned aerial vehicle.
S404, the unmanned aerial vehicle acquires road condition images according to the acquisition instructions.
S405, the unmanned aerial vehicle sends road condition images to the unmanned aerial vehicle.
And S406, controlling a driving route by the unmanned vehicle according to the road condition image.
For example, if the road condition image indicates that the front of the unmanned vehicle is congested, the unmanned vehicle drives with the head of the unmanned vehicle, so as to avoid long-time congestion.
For example, if the road condition image indicates that an obstacle exists in a position in front of the lane where the unmanned vehicle is located, the unmanned vehicle is driven to change lanes to avoid colliding with the obstacle.
In this embodiment, unmanned vehicle control unmanned aerial vehicle gathers road conditions image, because unmanned aerial vehicle flies in a flexible way, can fly and reach the position that unmanned vehicle can't arrive, consequently utilizes unmanned aerial vehicle can enlarge the field of vision of unmanned vehicle, gathers more road conditions information to unmanned vehicle makes more accurate decision-making.
Fig. 5 is a schematic flowchart of another image acquisition control method provided in the present application. This embodiment describes a specific implementation process when the first device is a cloud server, or the first device is an electronic device integrated in the cloud server. In this embodiment, unmanned vehicle and unmanned aerial vehicle are not direct communication, and unmanned vehicle and unmanned aerial vehicle all communicate with high in the clouds server. The unmanned aerial vehicle and the unmanned aerial vehicle continuously report the road condition information to the cloud server, the cloud server analyzes the road condition information, and the unmanned aerial vehicle is controlled to acquire road condition images according to the analysis result. The embodiment is applicable to scenes with large data volume of road condition information.
As shown in fig. 5, the method of the present embodiment includes:
s501, the unmanned vehicle and/or the unmanned aerial vehicle sends road condition information to the cloud server.
S502, the cloud server acquires abnormal road condition information according to the received road condition information.
S503, the cloud server sends an acquisition instruction to the unmanned aerial vehicle according to the abnormal road condition information.
S504, the unmanned aerial vehicle acquires road condition images according to the acquisition instructions.
S505, the unmanned aerial vehicle sends a road condition image to the cloud server.
S506, the cloud server identifies the road condition image to obtain a road condition identification result.
And S507, broadcasting the road condition identification result to the vehicles in the road by the cloud server.
Wherein the vehicles in the road comprise: unmanned vehicles and/or ordinary vehicles. Illustratively, if the road condition identification result indicates that the road is congested, the cloud server broadcasts the road condition identification result to vehicles in the road to remind the vehicles to shunt in time, so that the road is prevented from being further congested.
In this embodiment, the high in the clouds server control unmanned aerial vehicle and gather road conditions image, because unmanned aerial vehicle flies in a flexible way, can fly and reach the position that unmanned vehicle can't arrive, consequently utilize unmanned aerial vehicle can enlarge the field of vision of unmanned vehicle, gather more road conditions information to the high in the clouds server makes more accurate decision.
On the basis of any of the above embodiments, the control process of image acquisition is described below in connection with two example scenarios.
Fig. 6 is a schematic view of a traffic scene provided in the present application. As shown in fig. 6, the unmanned aerial vehicle stops at the stop position of the unmanned vehicle, and the unmanned vehicle travels on the road. Congestion occurs at a position in front of the unmanned vehicle in the current traffic scene (congestion due to a large pit occurring on the road is exemplified in fig. 6). In the traffic scene, due to the fact that other vehicles are arranged in front of the unmanned vehicle, the unmanned vehicle cannot acquire road condition information of the position of congestion, specific congestion reasons cannot be known by the unmanned vehicle or the cloud server, and therefore a decision cannot be made in time.
By adopting the method of the embodiment, the unmanned vehicle can determine the congestion in front of the unmanned vehicle by analyzing the road condition information acquired by the unmanned vehicle during the driving process. Therefore, as shown in fig. 6, the unmanned vehicle sends an acquisition instruction to the unmanned aerial vehicle to control the unmanned aerial vehicle to take off from the parking position and fly to a proper position to acquire the road condition image at the congestion position. The unmanned aerial vehicle sends the acquired road condition image to the unmanned vehicle. Therefore, the unmanned vehicle determines the congestion reason caused by the road pit according to the road condition image, and then the unmanned vehicle can turn around to drive so as to avoid long-time congestion.
Alternatively, the first and second electrodes may be,
during the driving process of the unmanned vehicle, the unmanned vehicle and/or the unmanned aerial vehicle continuously report the road condition information acquired by the unmanned vehicle and/or the unmanned aerial vehicle to the cloud server, and the cloud server analyzes the road condition information to determine that the congestion occurs in front of the unmanned vehicle. Therefore, as shown in fig. 6, the server sends an acquisition instruction to the unmanned aerial vehicle to control the unmanned aerial vehicle to take off from a parking position and fly to a proper position to acquire road condition images. The unmanned aerial vehicle sends the acquired road condition image to the cloud server. Therefore, the cloud server determines the reason for congestion caused by the road pit according to the road condition image, and then sends the reason for congestion to the unmanned vehicle, so that the unmanned vehicle runs without the head, and long-time congestion is avoided. In some scenarios, the server may also broadcast the congestion cause to other vehicles to avoid traffic congestion problems to the greatest extent.
Fig. 7 is a schematic view of another traffic scenario provided herein. As shown in fig. 7, the unmanned aerial vehicle stops at the stop position of the unmanned vehicle, and the unmanned vehicle drives on the road for traffic inspection. The unmanned vehicle finds a traffic accident at a certain position in traffic inspection and carries out evidence collection on the traffic accident. Then, the collected evidence image is not clear enough and needs to be photographed again. However, as the unmanned vehicle continuously runs, the best shooting position is missed, and the evidence image cannot be collected again.
By adopting the method of the embodiment, the unmanned vehicle analyzes the road condition information acquired by the unmanned vehicle during the driving process, determines that the road condition image of the accident position needs to be acquired again, and the unmanned vehicle can not acquire the road condition image, and sends an acquisition instruction to the unmanned vehicle as shown in fig. 7 so as to control the unmanned vehicle to take off from the parking position and fly to a proper position to acquire the road condition image of the accident position. The unmanned aerial vehicle sends the acquired road condition image to the unmanned vehicle. Therefore, the unmanned vehicle analyzes the road condition image to obtain the reason of the traffic accident. Optionally, the unmanned vehicle may further broadcast the cause of the traffic accident, or notify the vehicle related to the traffic accident, so as to solve the traffic accident as soon as possible and avoid causing road congestion.
Alternatively, the first and second electrodes may be,
during the driving process of the unmanned vehicle, the unmanned vehicle and/or the unmanned aerial vehicle continuously reports the acquired road condition information to the cloud server, and the cloud server analyzes the road condition information and determines that the road condition image of the accident position needs to be acquired again. As shown in fig. 7, the cloud server sends an acquisition instruction to the unmanned aerial vehicle to control the unmanned aerial vehicle to take off from a parking position and fly to a proper position to acquire a road condition image at the accident position. The unmanned aerial vehicle sends the collected road condition image to the cloud server. And the cloud server analyzes the reasons of the traffic accident according to the road condition image. In some scenes, the cloud server can inform the unmanned vehicle of the cause of the traffic accident and/or vehicles involved in the traffic accident, so that the traffic accident can be solved as soon as possible, and road congestion is avoided. Optionally, the cloud server may also broadcast the information of the traffic accident to vehicles on the road, so that the vehicles on the road are shunted as soon as possible, and congestion in a large range is avoided.
Fig. 8 is a schematic structural diagram of a control device for image acquisition according to the present application. The control device provided by the embodiment can be applied to unmanned vehicles and cloud servers. As shown in fig. 8, the control device 10 for image capturing of the present embodiment may include: the device comprises an acquisition module 11, a sending module 12 and a receiving module 13. Wherein the content of the first and second substances,
the acquiring module 11 is used for acquiring abnormal road condition information in the driving process of the vehicle;
the sending module 12 is configured to send a first acquisition instruction to the unmanned aerial vehicle according to the abnormal road condition information, where the first acquisition instruction is used to instruct the unmanned aerial vehicle to acquire a road condition image;
and the receiving module 13 is used for receiving the road condition image acquired by the unmanned aerial vehicle.
The control device for image acquisition provided in this embodiment may be used to implement the technical solution of the method embodiment shown in fig. 2, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 9 is a schematic structural diagram of another image acquisition control device provided in the present application. As shown in fig. 9, on the basis of the embodiment shown in fig. 8, in this embodiment, the sending module 12 may include: a determining unit 121 and a transmitting unit 122.
The determining unit 121 is configured to determine a first flight parameter and/or a first shooting parameter of the unmanned aerial vehicle according to the road condition abnormal information;
the sending unit 122 is configured to send a first acquisition instruction to the unmanned aerial vehicle according to the first flight parameter and/or the first shooting parameter.
In a possible embodiment, the first flight parameter comprises: a flying height; the determining unit 121 is specifically configured to:
if the road condition abnormal information indicates that the road is abnormal, determining that the flying height of the unmanned aerial vehicle is lower than a first preset height;
and if the abnormal road condition information indicates road congestion, determining that the flying height of the unmanned aerial vehicle is higher than a second preset height.
In a possible implementation, the determining unit 121 is further configured to: determining a second flight parameter and/or a second shooting parameter of the unmanned aerial vehicle according to the road condition image;
the sending unit 122 is further configured to: and sending a second acquisition instruction to the unmanned aerial vehicle according to the second flight parameter and/or the second shooting parameter.
In a possible implementation, the sending module 12 is further configured to: and sending a parking instruction to the unmanned aerial vehicle, wherein the parking instruction is used for indicating the unmanned aerial vehicle to park at a preset position of the vehicle.
In a possible implementation manner, as shown in fig. 9, the apparatus of this embodiment further includes: a processing module 14, wherein the processing module 14 is configured to control a driving route of the vehicle according to the road condition image.
In a possible implementation manner, the device of this embodiment is a cloud server, and the device further includes a processing module 14, where the processing module 14 is configured to perform recognition processing on the road condition image to obtain a road condition recognition result;
the sending module 12 is further configured to broadcast the road condition identification result to vehicles in the road.
The control device for image acquisition provided in this embodiment may be configured to implement the technical solution of any of the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 10, the electronic device is a block diagram of an electronic device of a control method of image capturing according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 10, the electronic apparatus includes: one or more processors 101, memory 102, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 10 illustrates an example of one processor 101.
The memory 102 is a non-transitory computer readable storage medium, and can be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the control method of image acquisition in the embodiments of the present application (for example, the acquisition module 11, the transmission module 12, the reception module 13, and the processing module 14 shown in fig. 8 and 9). The processor 101 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 102, that is, implements the control method of image acquisition in the above method embodiments.
The memory 102 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 102 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 102 may optionally include memory located remotely from processor 101, which may be connected to an electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the control method of image acquisition may further include: an input device 103 and an output device 104. The processor 101, the memory 102, the input device 103, and the output device 104 may be connected by a bus or other means, and the bus connection is exemplified in fig. 10.
The input device 103 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus of the control method of image capture, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 104 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (16)
1. A method of controlling image acquisition, comprising:
the method comprises the steps that first equipment obtains road condition abnormal information in the driving process of a vehicle;
the first equipment sends a first acquisition instruction to the unmanned aerial vehicle according to the road condition abnormal information, wherein the first acquisition instruction is used for instructing the unmanned aerial vehicle to acquire a road condition image;
the first equipment receives the road condition image acquired by the unmanned aerial vehicle.
2. The method of claim 1, wherein the first device sends a first acquisition instruction to the drone according to the abnormal road condition information, and the first acquisition instruction comprises:
the first equipment determines a first flight parameter and/or a first shooting parameter of the unmanned aerial vehicle according to the road condition abnormal information;
and the first equipment sends a first acquisition instruction to the unmanned aerial vehicle according to the first flight parameter and/or the first shooting parameter.
3. The method of claim 2, wherein the first flight parameter comprises: a flying height; the first device determines a first flight parameter of the unmanned aerial vehicle according to the abnormal road condition information, and the method includes:
if the abnormal road condition information indicates that the road is abnormal, the first equipment determines that the flying height of the unmanned aerial vehicle is lower than a first preset height;
and if the abnormal road condition information indicates that the road is congested, the first device determines that the flying height of the unmanned aerial vehicle is higher than a second preset height.
4. The method according to claim 2 or 3, after the first device receives the road condition image collected by the unmanned aerial vehicle, further comprising:
the first equipment determines a second flight parameter and/or a second shooting parameter of the unmanned aerial vehicle according to the road condition image;
and the first equipment sends a second acquisition instruction to the unmanned aerial vehicle according to the second flight parameter and/or the second shooting parameter.
5. The method according to any one of claims 1 to 4, wherein after the first device receives the road condition image collected by the unmanned aerial vehicle, the method further comprises:
the first device sends a parking instruction to the unmanned aerial vehicle, and the parking instruction is used for indicating the unmanned aerial vehicle to park at a preset position of the vehicle.
6. The method according to any one of claims 1 to 5, wherein after the first device receives the road condition image collected by the unmanned aerial vehicle, the method further comprises:
and the first equipment controls the driving route of the vehicle according to the road condition image.
7. The method of any one of claims 1 to 6, wherein the first device is a cloud server, and after receiving the road condition image acquired by the unmanned aerial vehicle, the method further comprises:
the first equipment identifies the road condition image to obtain a road condition identification result;
and the first equipment broadcasts the road condition identification result to vehicles in the road.
8. An image acquisition control apparatus comprising:
the acquisition module is used for acquiring the road condition abnormal information in the driving process of the vehicle;
the sending module is used for sending a first acquisition instruction to the unmanned aerial vehicle according to the road condition abnormal information, wherein the first acquisition instruction is used for instructing the unmanned aerial vehicle to acquire a road condition image;
and the receiving module is used for receiving the road condition image acquired by the unmanned aerial vehicle.
9. The apparatus of claim 8, wherein the means for transmitting comprises: a determination unit and a transmission unit, wherein,
the determining unit is used for determining a first flight parameter and/or a first shooting parameter of the unmanned aerial vehicle according to the road condition abnormal information;
the sending unit is used for sending a first acquisition instruction to the unmanned aerial vehicle according to the first flight parameter and/or the first shooting parameter.
10. The apparatus of claim 9, wherein the first flight parameter comprises: a flying height; the determining unit is specifically configured to:
if the road condition abnormal information indicates that the road is abnormal, determining that the flying height of the unmanned aerial vehicle is lower than a first preset height;
and if the abnormal road condition information indicates road congestion, determining that the flying height of the unmanned aerial vehicle is higher than a second preset height.
11. The apparatus of claim 9 or 10, the determination unit further to: determining a second flight parameter and/or a second shooting parameter of the unmanned aerial vehicle according to the road condition image;
the sending unit is further configured to: and sending a second acquisition instruction to the unmanned aerial vehicle according to the second flight parameter and/or the second shooting parameter.
12. The apparatus of any of claims 8 to 11, the sending module further configured to: and sending a parking instruction to the unmanned aerial vehicle, wherein the parking instruction is used for indicating the unmanned aerial vehicle to park at a preset position of the vehicle.
13. The device according to any one of claims 8 to 12, further comprising a processing module for controlling a driving route of the vehicle according to the road condition image.
14. The device according to any one of claims 8 to 13, which is a cloud server, and further comprises a processing module, wherein the processing module is configured to perform recognition processing on the road condition image to obtain a road condition recognition result;
the sending module is further used for broadcasting the road condition identification result to vehicles in the road.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1 to 7.
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