CN114581872A - Method, device, equipment, medium and program product for monitoring road elements - Google Patents
Method, device, equipment, medium and program product for monitoring road elements Download PDFInfo
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Abstract
The present disclosure provides a method and an apparatus for monitoring road elements, an electronic device, a program product, and a storage medium, and relates to the field of computer technologies, in particular to the field of artificial intelligence technologies. The method comprises the steps of firstly, acquiring a road image according to an initial monitoring period; then extracting road element information from the road image to obtain the current monitoring result of the road element; and finally, predicting a new monitoring period by using the road element information, and acquiring road images according to the new monitoring period so as to realize continuous monitoring of the road elements. The monitoring period which is consistent with the change of the road elements can be accurately predicted by utilizing the characteristics of the road element information, and the self-adaptive monitoring period determining mode can effectively improve the timeliness of updating the electronic map and simultaneously avoid the defect of operation resource waste caused by the fact that the determined monitoring period is short.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to the technical field of artificial intelligence, and discloses a method and a device for monitoring road elements, electronic equipment, a non-transitory computer-readable storage medium storing computer instructions and a computer program product.
Background
With the increasing year by year of automobile keeping quantity, in order to standardize road traffic, more and more road sections are additionally provided with road elements such as electronic eyes and traffic lights to monitor irregular behaviors on roads. Road elements such as electronic eyes and traffic lights are important components of real-world road data, and it is an important part of electronic navigation maps, such as navigation map, to find changes and update the changes. If the efficiency of data acquisition and updating of road elements is not enough, navigation path planning and ETA (traffic duration estimation) are directly influenced, and the travel experience of users of the electronic map is further directly influenced.
The traditional updating mode of road elements such as electronic eyes, traffic lights and the like is mainly carried out in a planning scheduling mode, namely, relevant images and other data are collected according to a fixed monitoring period collection mode, and after changed data are collected, the electronic map is updated based on the data. The traditional mode easily causes a certain problem in the updating time efficiency of the electronic map.
Disclosure of Invention
The present disclosure at least provides a method and apparatus for monitoring road elements, an electronic device, a program product, and a storage medium.
According to an aspect of the present disclosure, there is provided a method of monitoring a road element, including:
acquiring road images according to an initial monitoring period;
extracting road element information from the road image;
and predicting a new monitoring period by using the road element information, and acquiring road images according to the new monitoring period so as to realize continuous monitoring of the road elements.
According to another aspect of the present disclosure, there is provided a road element monitoring device, including:
the image acquisition module is used for acquiring road images according to an initial monitoring period;
the element monitoring module is used for extracting road element information from the road image;
and the period prediction module is used for predicting a new monitoring period by using the road element information and acquiring road images according to the new monitoring period so as to realize continuous monitoring of the road elements.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the 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 a method according to any one of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method in any embodiment of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the method in any of the embodiments of the present disclosure.
According to the technology disclosed by the invention, the road element information is firstly extracted from the acquired road image to obtain the current monitoring result of the road element, and then the monitoring period which is consistent with the change of the road element can be accurately predicted by utilizing the characteristics of the road element information.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is one of the flow charts of a method of monitoring a road element according to the present disclosure;
FIG. 2 is a second flow chart of a method of monitoring road elements according to the present disclosure;
FIG. 3 is a schematic diagram of a monitoring device for road elements according to the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. 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 disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
With the increasing year by year of automobile keeping quantity, in order to standardize road traffic, more and more road sections are additionally provided with road elements such as electronic eyes and traffic lights to monitor irregular behaviors on roads. Road elements such as electronic eyes and traffic lights are important components of real-world road data, and it is an important part of electronic map, for example, navigation map production to find changes and update the changes. If the efficiency of data acquisition and updating of road elements is not enough, navigation path planning and ETA (traffic duration estimation) are directly influenced, and the travel experience of users of the electronic map is further directly influenced.
In the related art, a planning scheduling mode is used for updating the electronic map, specifically, a fixed monitoring period is used for issuing a monitoring task, road images in the real world are collected, then a visual technology is used for extracting road element information, the extracted road element information and the latest version of the electronic map are used for carrying out differential processing, and finally the electronic map is updated based on the differential processing result. Because the mileage magnitude of the total road network to be collected is large and is limited by the integral collection cost, the monitoring period is generally set as monthly or weekly, images or images are collected periodically, and the electronic map is updated periodically.
The image acquisition and the updating period of the electronic map are relatively long, so that the updating timeliness of the electronic map is poor, and the change of road elements in the real world cannot be found in time, and the traveling experience of users of the electronic map is influenced.
In view of the above technical drawbacks, the present disclosure provides at least a method and an apparatus for monitoring road elements, an electronic device, a non-transitory computer-readable storage medium storing computer instructions, and a computer program product. According to the method, the road element information is extracted from the acquired road image to obtain the current monitoring result of the road element, and then the monitoring period which is consistent with the change of the road element can be predicted more accurately by using the characteristics of the road element information.
The following describes a method for monitoring a road element according to the present disclosure with reference to specific examples.
Fig. 1 shows a flowchart of a monitoring method of a road element of an embodiment of the present disclosure, and an execution subject of the embodiment may be a device having a computing capability. As shown in fig. 1, a method for monitoring a road element according to an embodiment of the present disclosure may include the following steps:
and S110, acquiring a road image according to an initial monitoring period.
The road image may be acquired by a camera on the map acquisition vehicle according to an initial monitoring period. The area in the road image is a preset monitoring area, for example, an area in an electronic map.
And S120, extracting the road element information from the road image.
After the road image is acquired, the trained deep neural network can be used for extracting the road element information of the road image. Before extracting the road element information, it may also be determined whether the shooting quality of the road image meets a preset condition, for example, whether the degree of sharpness of the road image meets the preset degree of sharpness, and if the shooting quality of the road image meets the preset condition, the road element information in the road image is extracted.
Illustratively, the deep neural network may include at least one convolutional layer, and may further include a full link layer, and the like, and the deep neural network first extracts image features in the road image, and then processes the extracted image features to obtain a detection frame of each preset road element. And then, the position information of each detection frame in a map coordinate system corresponding to the electronic map can be determined by combining the motion track of a camera for shooting the road image. The detection frame can be a detection frame corresponding to the cross bar, a detection frame corresponding to the electronic eye and a detection frame corresponding to the traffic light.
After the position information of the detection frames of the road elements in the map coordinate system corresponding to the electronic map is obtained, the position information of the detection frames and the types of the detection frames are combined, so that the road element information can be determined, for example, whether traffic lights are arranged in the corresponding area or not is determined, and the position information of the traffic lights is determined under the condition that the traffic lights are arranged; determining whether an electronic eye is arranged in the corresponding area, and determining the position information of the electronic eye under the condition that the electronic eye is arranged; and determining whether a cross bar is arranged in the corresponding area, and determining the position information of the cross bar and whether an electronic eye or a traffic light is arranged on the cross bar under the condition that the cross bar is arranged. In addition, information such as the length of time for setting up the cross bar can be determined from information such as road element information, shooting time, and shooting frequency of the plurality of road images.
Therefore, the road element information may include electronic eye information corresponding to the electronic eyes, traffic light information corresponding to the traffic lights, and crossbar information corresponding to the crossbars. The transverse rod is used for installing electronic eyes and/or traffic lights, and the transverse rod information further comprises transverse rod establishing time, transverse rod position and other information.
The road element information may include attribute information of the road in the corresponding area, such as a road rank, a road width, the number of lanes in the road, the number of vehicles in the same lane, and the like.
Illustratively, the deep neural network may specifically be a neural network corresponding to fast-RCNN and inclusion V3, and the extracted road element information of the network models may further include information of speed-limiting license plates, ground vehicle information, traffic-limiting signs, and the like.
The change rule of the electronic eye and the traffic light in the corresponding area can be accurately predicted according to the road element information, and a new monitoring period can be accurately predicted based on the change rule, so that the change of the road elements such as the traffic light and the electronic eye can be timely monitored.
And S130, predicting a new monitoring period by using the road element information, and acquiring road images according to the new monitoring period to realize continuous monitoring of the road elements.
When the new monitoring period is predicted by using the road element information, the element characteristic information may be determined according to the extracted road element information, and then the new monitoring period may be predicted by using the element characteristic information.
For example, the feature information may be a feature vector constructed according to the road feature information, for example, when a traffic light is provided in a corresponding region, a corresponding element in the feature vector is set to 1, when no traffic light is provided in the corresponding region, a corresponding element in the feature vector is set to 0, when an electronic eye is provided in the corresponding region, a corresponding element in the feature vector is set to 1, when no electronic eye is provided in the corresponding region, a corresponding element in the feature vector is set to 0, when a crossbar is provided in the corresponding region, a corresponding element in the feature vector is set to 1, and when a crossbar is not provided in the corresponding region, a corresponding element in the feature vector is set to 0. In addition, elements for representing road grade, road width, number of lanes in the road, number of vehicles in the same row and cross bar establishing time are further arranged in the feature vector.
The element characteristic information can accurately represent the characteristics of the road elements in the corresponding area, and the monitoring period which is consistent with the change of the road elements can be accurately predicted based on the characteristics of the road elements in the corresponding area.
In some embodiments, the variation probability of the road element may be predicted using the element feature information; a new monitoring period is then determined based on the change probability.
The higher the change probability is, the higher the possibility that the road elements change is, the monitoring period can be shortened at the moment, so that the road images with the changed road elements can be acquired more timely, the electronic map can be updated more timely, and the updating timeliness of the electronic map is improved. The smaller the change probability is, the lower the possibility that the road elements change is, and at the moment, the monitoring period can be increased, so that when the small probability of the road elements changes, the acquisition period of the road images is increased, and the waste of unnecessary computing resources is reduced.
In some embodiments, after determining the change probability, the following steps may be specifically utilized to determine a new monitoring period:
firstly, determining a first negative correlation between the change probability and a monitoring period; and then, determining a new monitoring period according to the change probability by using the first negative correlation relation.
According to the above statement, the change probability and the monitoring period are in a negative correlation relationship, i.e. the larger the change probability, the smaller the monitoring period; the smaller the probability of change, the larger the monitoring period. The negative correlation may be a linear variation or a non-linear variation, which is not limited in the present disclosure. According to the first negative correlation, a reasonable monitoring period can be determined, and therefore timeliness of updating of the electronic map can be improved.
In some embodiments, after determining the change probability, a new monitoring period may be determined by using the following steps:
when the change probability is larger than a first preset probability, reducing the initial monitoring period to obtain a new monitoring period;
when the change probability is smaller than a second preset probability, increasing the initial monitoring period to obtain a new monitoring period;
and when the change probability is less than or equal to a first preset probability and greater than or equal to a second preset probability, taking the initial monitoring period as a new monitoring period.
The first preset probability may be equal to or greater than the second preset probability.
In some embodiments, the new monitoring period is obtained by using the element characteristic information prediction, and the following steps can be further used for realizing:
determining a second negative correlation between the establishment duration of the cross bar and the monitoring period under the condition that the characteristic of the element characteristic information is provided with the cross bar and no traffic light and/or electronic eye is arranged; and determining a new monitoring period according to the established duration by utilizing the second negative correlation.
When the cross bar is already arranged in the area corresponding to the road image, but the traffic light and/or the electronic eye are not arranged, it indicates that the traffic light and/or the electronic eye will be arranged on the cross bar in a short time in the future, and the monitoring period needs to be reduced. And the longer the cross bar is established, the shorter the time is, the traffic lights and/or the electronic eyes can be arranged on the cross bar, and the monitoring period needs to be set to be shorter. The above embodiment can realize the above function by the second negative correlation, and a more reasonable new monitoring period is obtained.
In some embodiments, the new monitoring period is obtained by using the element characteristic information prediction, and the following steps can be further used for realizing:
and under the condition that the characteristic information of the elements is provided with traffic lights and/or electronic eyes, increasing the initial monitoring period to obtain a new monitoring period.
When the traffic light and/or the electronic eye are already arranged in the area corresponding to the road image, it indicates that the road elements such as the cross bar, the traffic light, the electronic eye and the like will not change in a longer time in the future, and at this time, the monitoring period needs to be increased so as to reduce unnecessary waste of computing resources.
In some embodiments, the obtaining of the new monitoring period by using the element feature information prediction may also be implemented by using a trained network model, and specifically, the element feature information may be input into the trained network model, and the new monitoring period may be output by processing the element feature information through the network model.
The network model can comprise network structures such as a convolution layer and a full connection layer, is obtained by utilizing a plurality of road sample images for training, and has high prediction precision and high prediction speed.
Illustratively, when the network model is trained, information such as interval duration, road attribute, traffic light/electronic eye change period and the like between the L-shaped cross bar and the traffic light and/or the electronic eye can be collected by using history, truth marking is performed, and the network model is trained by using the technology including but not limited to XGBoost. And outputting the network model as a new monitoring period T. When the road elements are changed at high probability, T is reduced, and at the moment, the tasks of image acquisition and electronic map updating are issued with frequency; when the road elements are changed at low probability, T is correspondingly increased, and the tasks of image acquisition and electronic map updating are carried out by reducing the frequency.
The extracted road element information can be used as a monitoring result of the road element in the current monitoring period, after the monitoring result is obtained, the electronic map of the latest version can be obtained, and the road element information is extracted from the electronic map of the latest version to obtain the original element information; then, determining difference information between the original element information and the currently extracted road element information; and finally updating the electronic map based on the difference information.
Illustratively, when the difference information indicates that there is a new road element, or a change in the position information of the road element, the electronic map is updated based on the difference information. According to the difference information, the electronic map can be updated accurately, the updating accuracy of the electronic map is improved, and therefore the experience of users of the electronic map is improved.
The following describes a method for monitoring road elements according to the present application with a specific example.
As shown in fig. 2, the method for monitoring road elements of the present embodiment may include the following steps:
firstly, acquiring a current monitoring period;
secondly, acquiring road images according to the current monitoring period;
thirdly, extracting road element information from the road image based on a vision technology to obtain a monitoring result of the road element in the current monitoring period;
fourthly, determining difference information between the road element information extracted from the electronic map of the latest version and the currently extracted road element information;
fifthly, judging whether the road elements are changed or not based on the difference information, and updating the electronic map based on the difference information under the condition that the road elements are changed;
sixthly, constructing element characteristic information based on the road element information;
and seventhly, processing the element characteristic information by using the network model to obtain a new monitoring period, and adjusting the current monitoring period to the new monitoring period when the new monitoring period is different from the current monitoring period in the first step.
According to the method in the embodiment, the timeliness of updating the electronic map can be improved, and meanwhile, the waste of computing resources can be reduced.
Based on the same inventive concept, the embodiment of the present disclosure further provides a device for monitoring a road element corresponding to the method for monitoring a road element, and since the principle of solving the problem of the device in the embodiment of the present disclosure is similar to that of the method for monitoring a road element in the embodiment of the present disclosure, the implementation of the device may refer to the implementation of the method, and repeated details are omitted.
As shown in fig. 3, a schematic structural diagram of a road element monitoring device provided in the embodiment of the present disclosure includes:
and an image acquisition module 310, configured to acquire a road image according to an initial monitoring period.
And an element monitoring module 320, configured to extract road element information from the road image.
And the period prediction module 330 is configured to predict a new monitoring period by using the road element information, and acquire a road image according to the new monitoring period, so as to implement continuous monitoring on the road element.
In some embodiments, the period prediction module 330, when predicting a new monitoring period using the road element information, is configured to:
determining element feature information based on the road element information;
and predicting to obtain a new monitoring period by using the element characteristic information.
In some embodiments, the apparatus further comprises a map update module 340 configured to:
acquiring the electronic map of the latest version, and extracting road element information from the electronic map of the latest version to obtain original element information;
determining difference information between the original element information and the currently extracted road element information;
and updating the electronic map based on the difference information.
In some embodiments, the road element information includes at least one of the following
Attribute information of the corresponding road; cross bar information; electronic eye information; traffic light information;
the transverse rod is used for installing electronic eyes and/or traffic lights, and the transverse rod information comprises transverse rod establishing time.
In some embodiments, the period prediction module 330, when predicting a new monitoring period using the factor feature information, is configured to:
predicting the change probability of the road element by using the element characteristic information;
based on the probability of change, a new monitoring period is determined.
In some embodiments, the period prediction module 330, when determining a new monitoring period based on the probability of change, is to:
determining a first negative correlation between the probability of change and the monitoring period;
and determining a new monitoring period according to the change probability by using the first negative correlation relation.
In some embodiments, the period prediction module 330, when predicting a new monitoring period using the factor feature information, is configured to:
determining a second negative correlation between the establishment duration of the cross bar and the monitoring period under the condition that the characteristic of the element characteristic information is provided with the cross bar and no traffic light and/or electronic eye is arranged;
and determining a new monitoring period according to the established duration by utilizing the second negative correlation.
In some embodiments, the period prediction module 330, when predicting a new monitoring period using the factor feature information, is configured to:
and under the condition that the characteristic information of the elements is provided with traffic lights and/or electronic eyes, increasing the initial monitoring period to obtain a new monitoring period.
In some embodiments, the period prediction module 330, when predicting a new monitoring period using the factor feature information, is configured to:
and inputting the element characteristic information into a trained network model, processing the element characteristic information through the network model, and outputting a new monitoring period.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 4 shows a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present disclosure. 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 disclosure described and/or claimed herein.
As shown in fig. 4, the device 400 includes a computing unit 410 that may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)420 or a computer program loaded from a storage unit 480 into a Random Access Memory (RAM) 430. In the RAM430, various programs and data required for the operation of the device 400 can also be stored. The computing unit 410, the ROM420 and the RAM430 are connected to each other by a bus 440. An input/output (I/O) interface 450 is also connected to bus 440.
Various components in device 400 are connected to I/O interface 450, including: an input unit 460 such as a keyboard, a mouse, etc.; an output unit 470 such as various types of displays, speakers, and the like; a storage unit 480 such as a magnetic disk, an optical disk, or the like; and a communication unit 490 such as a network card, modem, wireless communication transceiver, etc. The communication unit 490 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), 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.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and 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. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
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 disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. 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 disclosure should be included in the scope of protection of the present disclosure.
Claims (21)
1. A method of monitoring a road element, comprising:
acquiring road images according to an initial monitoring period;
extracting road element information from the road image;
and predicting a new monitoring period by using the road element information, and acquiring road images according to the new monitoring period so as to realize continuous monitoring of the road elements.
2. The method of claim 1, wherein the predicting a new monitoring period using the road element information comprises:
determining element feature information based on the road element information;
and predicting to obtain a new monitoring period by using the element characteristic information.
3. The method of claim 1 or 2, further comprising:
acquiring the electronic map of the latest version, and extracting road element information from the electronic map of the latest version to obtain original element information;
determining difference information between the original element information and the currently extracted road element information;
and updating the electronic map based on the difference information.
4. The method of any of claims 1 to 3, wherein the road element information comprises at least one of:
attribute information, cross bar information, electronic eye information and traffic light information of the corresponding road;
the cross bar is used for installing electronic eyes and/or traffic lights, and the cross bar information comprises cross bar establishing time.
5. The method of claim 2, wherein the predicting a new monitoring period using the feature information comprises:
predicting the change probability of the road element by using the element characteristic information;
based on the change probability, a new monitoring period is determined.
6. The method of claim 5, wherein said determining a new monitoring period based on said probability of change comprises:
determining a first negative correlation between the probability of change and a monitoring period;
and determining a new monitoring period according to the change probability by using the first negative correlation.
7. The method of claim 2, wherein the predicting a new monitoring period using the feature information comprises:
determining a second negative correlation between the establishment duration of the cross bar and the monitoring period under the condition that the characteristic feature of the element is provided with the cross bar and no traffic light and/or electronic eye is arranged;
and determining a new monitoring period according to the establishing duration by utilizing the second negative correlation.
8. The method of claim 2, wherein the predicting a new monitoring period using the feature information comprises:
and under the condition that the characteristic information of the elements represents that traffic lights and/or electronic eyes are/is arranged, increasing the initial monitoring period to obtain a new monitoring period.
9. The method according to any one of claims 2 and 5 to 8, wherein the predicting a new monitoring period using the element feature information comprises:
and inputting the element characteristic information into a trained network model, processing the element characteristic information through the network model, and outputting the new monitoring period.
10. A device for monitoring a road element, comprising:
the image acquisition module is used for acquiring road images according to an initial monitoring period;
the element monitoring module is used for extracting road element information from the road image;
and the period prediction module is used for predicting a new monitoring period by using the road element information and acquiring road images according to the new monitoring period so as to realize continuous monitoring of the road elements.
11. The apparatus of claim 10, wherein the period prediction module, when predicting a new monitoring period using the road element information, is to:
determining element feature information based on the road element information;
and predicting to obtain a new monitoring period by using the element characteristic information.
12. The apparatus of claim 10 or 11, further comprising a map update module to:
acquiring the electronic map of the latest version, and extracting road element information from the electronic map of the latest version to obtain original element information;
determining difference information between the original element information and the currently extracted road element information;
and updating the electronic map based on the difference information.
13. The apparatus according to any one of claims 10 to 12, wherein the road element information includes at least one of the following
Attribute information of the corresponding road; cross bar information; electronic eye information; traffic light information;
the cross bar is used for installing electronic eyes and/or traffic lights, and the cross bar information comprises cross bar establishing time.
14. The apparatus of claim 11, wherein the period prediction module, when predicting a new monitoring period using the feature information, is configured to:
predicting the change probability of the road element by using the element characteristic information;
based on the change probability, a new monitoring period is determined.
15. The apparatus of claim 14, wherein the period prediction module, in determining a new monitoring period based on the probability of change, is to:
determining a first negative correlation between the probability of change and a monitoring period;
and determining a new monitoring period according to the change probability by using the first negative correlation.
16. The apparatus of claim 11, wherein the period prediction module, when predicting a new monitoring period using the feature information, is configured to:
determining a second negative correlation between the establishment duration of the cross bar and the monitoring period under the condition that the characteristic feature of the element is provided with the cross bar and no traffic light and/or electronic eye is arranged;
and determining a new monitoring period according to the establishing duration by utilizing the second negative correlation.
17. The apparatus of claim 11, wherein the period prediction module, when predicting a new monitoring period using the feature information, is configured to:
and under the condition that the characteristic information of the elements represents that traffic lights and/or electronic eyes are/is arranged, increasing the initial monitoring period to obtain a new monitoring period.
18. The apparatus of any of claims 11, 14 to 17, wherein the period prediction module, when predicting a new monitoring period using the factor characteristic information, is to:
inputting the element characteristic information into a trained network model, processing the element characteristic information through the network model, and outputting the new monitoring period.
19. 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 9.
20. 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 9.
21. A computer program product comprising computer programs/instructions, wherein the computer programs/instructions, when executed by a processor, implement the method of any of claims 1 to 9.
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