CN114677843A

CN114677843A - Road condition information processing method, device and system and electronic equipment

Info

Publication number: CN114677843A
Application number: CN202210147803.4A
Authority: CN
Inventors: 王倩; 刘挺
Original assignee: Alibaba Cloud Computing Ltd
Current assignee: Alibaba Cloud Computing Ltd
Priority date: 2022-02-17
Filing date: 2022-02-17
Publication date: 2022-06-28
Anticipated expiration: 2042-02-17
Also published as: CN114677843B

Abstract

The application discloses a method, a device and a system for processing road condition information and electronic equipment. Wherein, the method comprises the following steps: acquiring road network topology data of a road network where at least one image acquisition device is located, and acquiring road network images by the at least one image acquisition device; performing feature extraction on the road network image to obtain local road condition information of a road section where each image acquisition device is located; and carrying out road network matching on the local road condition information corresponding to each image acquisition device according to the road network topological data to obtain the road condition information of the road network. The method and the device solve the technical problem that the accuracy of road condition information acquisition is poor in the prior art.

Description

Road condition information processing method, device and system and electronic equipment

Technical Field

The present application relates to the field of image recognition, and in particular, to a method, an apparatus, a system and an electronic device for processing road condition information.

Background

In daily life, when people go out, navigation maps are generally used for navigation route planning, real-time road condition information broadcasting, travel time prediction and the like. In the current road traffic control and active management and control scenes, the traffic state at the current moment is generally obtained by using real-time road condition information, and the future traffic evolution trend is predicted and early warning management and control are performed in advance. However, wrong road condition information may not only reduce the user experience, but also may bring hidden dangers to traffic safety and life and property safety.

At present, various dynamic information of roads, such as real-time traffic speed, flow rate, density and the like of the roads, are generally counted by an indirect measurement method based on a floating vehicle GPS (Global Positioning System). However, in the method, floating vehicles with abnormal driving behaviors such as abnormal parking, head drop at a special position and the like also participate in real-time road condition calculation, so that errors exist in road condition information calculation, and the errors are more obvious in roads with fewer passing vehicle samples. In addition, the precision of the GPS of the existing non-surveying and mapping vehicle is larger than the width of most lanes, the real-time road condition information obtained by the indirect measurement method based on the GPS of the floating vehicle can only reach the precision of the road level, and the detection of the road condition information of the lane level cannot be realized, but in many practical scenes, the real-time road condition difference of different lanes of the same road at the same time is large, and only the road condition information of the road level cannot accurately identify the road condition.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the application provides a method, a device and a system for processing road condition information and electronic equipment, so as to at least solve the technical problem of poor accuracy of road condition information acquisition in the prior art.

According to an aspect of the embodiment of the present application, a method for processing road condition information is provided, including: acquiring road network topology data of a road network where at least one image acquisition device is located, and acquiring road network images by the at least one image acquisition device; performing feature extraction on the road network image to obtain local road condition information of a road section where each image acquisition device is located; and carrying out road network matching on the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain the road condition information of the road network.

According to another aspect of the embodiment of the present application, a method for processing road condition information is further provided, including: the method comprises the steps that a cloud server obtains road section position information of a target object in a road network; the cloud server acquires road network images acquired by at least one image acquisition device based on the road section position information, wherein the at least one image acquisition device is arranged in a road section range determined by the road section position information; the cloud server extracts the characteristics of the road network image to obtain local road condition information of a road section where each image acquisition device is located; the cloud server performs road network matching on the local road condition information corresponding to each image acquisition device according to road network topology data of a road network where the target object is located to obtain road condition information of the road network where the target object is located; the cloud server pushes the road condition information to the terminal equipment.

According to another aspect of the embodiment of the present application, a method for processing road condition information is further provided, including: responding to the road condition query instruction, and displaying the road section position information of the target object in the road network; the method comprises the steps of obtaining road condition information of a road network where a target object is located from a server, and displaying the road condition information, wherein the road condition information is obtained by performing road network matching on local road condition information corresponding to each image acquisition device based on road network topology data of the road network where the target object is located, the local road condition information corresponding to each image acquisition device is obtained by performing feature extraction on a road network image acquired by the corresponding image acquisition device, and the corresponding image acquisition device is an image acquisition device arranged in a road section range determined by road section position information.

According to another aspect of the embodiments of the present application, there is also provided a device for processing road condition information, including: the acquisition module is used for acquiring road network topology data of a road network where at least one image acquisition device is located and road network images acquired by the at least one image acquisition device; the characteristic extraction module is used for extracting the characteristics of the road network images to obtain local road condition information of a road section where each image acquisition device is located; and the matching module is used for matching the road network to the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain the road condition information of the road network.

According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the above-mentioned road condition information processing method when running.

According to another aspect of embodiments of the present application, there is also provided an electronic device, including one or more processors; a memory for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement a processing method for executing the program, wherein the program is configured to execute the above-mentioned traffic information processing method when executed.

According to another aspect of the embodiments of the present application, there is also provided a system for processing road condition information, including: a processor; and a memory coupled to the processor for storing executable instructions of the processor; the processor is configured to execute the instructions to implement the method for processing the traffic information as described above.

In the embodiment of the application, a road network image acquired by image acquisition equipment in real time is used for determining road condition information, after road network topology data of a road network where at least one image acquisition equipment is located and the road network image acquired by the at least one image acquisition equipment are acquired, feature extraction is performed on the road network image to obtain local road condition information of a road section where each image acquisition equipment is located, and then road network matching is performed on the local road condition information corresponding to each image acquisition equipment according to the road network topology data to obtain the road condition information of the road network.

In the process, the road network image acquired by the image acquisition equipment can truly represent the traffic state of the road, namely the scheme provided by the application is to directly measure the road condition information of the road network, and the road condition information is not interfered by the number of vehicles passing through the road in real time and/or abnormal driving behaviors of the vehicles, so that the accuracy of the road condition information is ensured. In addition, the road condition information is determined based on the road network image, and the vehicle information of different lanes can be identified through the road network image, namely the scheme provided by the application can acquire real-time road condition information of lane levels, so that the problem that the road condition information has errors due to the difference of traffic states of different lanes in the prior art is avoided, and the accuracy of acquiring the road condition information is further improved.

Therefore, the scheme provided by the application achieves the purpose of acquiring the road condition information, the technical effect of improving the accuracy of acquiring the road condition information is achieved, and the technical problem that the accuracy of acquiring the road condition information is poor in the prior art is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

Fig. 1 is a hardware block diagram of an electronic device for implementing a traffic information processing method according to an embodiment of the present application;

fig. 2 is a flowchart of a traffic information processing method according to an embodiment of the present disclosure;

fig. 3 is a flowchart of a processing method of a road network image acquired by an optional single image acquisition device according to an embodiment of the present application;

fig. 4 is a flowchart of a processing method for local traffic information acquired by a plurality of selectable image acquisition devices according to an embodiment of the present application;

fig. 5 is a flowchart of an adjustment method for selectable traffic information according to an embodiment of the present application;

fig. 6 is a flowchart of a method for processing road condition information according to an embodiment of the present application;

fig. 7 is a schematic block diagram of an alternative traffic information processing method according to an embodiment of the present application;

fig. 8 is a flowchart of a method for processing road condition information according to an embodiment of the present application;

fig. 9 is a schematic diagram of a traffic information processing apparatus according to an embodiment of the present application;

fig. 10 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

According to the embodiment of the present application, there is also provided an embodiment of a method for processing road condition information, where it is to be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that in the flowchart.

The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Fig. 1 shows a hardware structure block diagram of an electronic device (or mobile device) for implementing a traffic information processing method. As shown in fig. 1, the electronic device 10 (or mobile device 10) may include one or more processors (shown as 102a, 102b, … …, 102n in the figures) which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, a memory 104 for storing data, and a transmission module 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a universal serial BUS (BUS) port (which may be included as one of the ports of the BUS), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, electronic device 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the electronic device 10 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory 104 may be configured to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the method for processing traffic information in the embodiment of the present application, and the processor executes various functional applications and data processing by operating the software programs and modules stored in the memory 104, that is, implements the method for processing traffic information. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor, which may be connected to the electronic device 10 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 transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the electronic device 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the electronic device 10 (or mobile device).

It should be noted here that in some alternative embodiments, the computer device (or mobile device) shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.

In addition, it should be further noted that, in this embodiment, the server may execute the method provided in this embodiment, where the server may be disposed in a processing system of traffic information, and the system may further include a terminal device, where the terminal device may send a traffic information obtaining instruction to the server, and receive and execute real-time traffic information returned by the server according to the traffic information.

In the foregoing operating environment, the present application provides a method for processing traffic information as shown in fig. 2, where fig. 2 is a flowchart of a method for processing traffic information according to a first embodiment of the present application, and as can be seen from fig. 2, the method includes the following steps:

Step S202, road network topology data of a road network where at least one image acquisition device is located are obtained, and road network images acquired by the at least one image acquisition device.

In step S202, at least one image capturing device may be a road camera, where the road camera is a camera disposed on both sides of a road and used for capturing an illegal driving behavior of a driver. Optionally, in this embodiment, at least one image acquisition device is configured to acquire a road network image of a corresponding road segment in a road network, so that the server can acquire the road network image acquired by the image acquisition device in real time.

In addition, in step S202, the road network is a road network in the traffic field, which limits the movement trajectory of the moving object, wherein the moving object may be, but is not limited to, a pedestrian or a vehicle. The road network topology data is topology data of a road network, and the topology data at least comprises road section types, road section widths, road section positions, road section lengths, connection relations among road sections and the like. Optionally, the server may obtain the road network topology data from a data system of a traffic management department, and may also obtain the road network topology data in a manner of big data.

It should be noted that, in step S202, the image capturing device may capture all road network images, for example, a camera on a highway that realizes full coverage of the camera may capture all road network images. In addition, at some specific time, only the road network image (i.e. local road network image) in a certain range collected by the image collecting device may be obtained, and the obtained road network image may be processed. In addition, in step S202, the road network image acquired by each image acquisition device is acquired, so that the server can generate the road condition information of the road network according to the road network image, and it is easy to note that the road network image acquired by the image acquisition device can directly detect the road condition information, thereby avoiding the problem of poor accuracy caused by acquiring the road condition information by an indirect measurement method based on a floating car GPS in the prior art.

And step S204, extracting the characteristics of the road network image to obtain local road condition information of the road section where each image acquisition device is located.

In step S204, after the feature extraction is performed on the road network image, the obtained image features may include, but are not limited to, road feature information and vehicle feature information, where the road feature information represents relevant information of a road segment where the image capturing device is located, for example, information such as the number of lanes, the width of lanes, and the like; the vehicle characteristic information represents relevant information of the vehicles in the image acquired by the image acquisition device, such as distribution information of the vehicles on each lane, inter-vehicle distance between the vehicles and the like.

It should be noted that the server may determine local road condition information of the road segment where each image acquisition device is located by analyzing image features of the road network image, where the image features may represent traffic indexes corresponding to the current road segment, for example, indexes such as a traffic speed, a traffic flow, and a traffic density of the current road segment; the local road condition information represents a traffic state corresponding to the current road section, and the traffic state may include, but is not limited to, a smooth state, a general congestion state, a severe congestion state, a road section closed state, and the like.

In addition, it should be noted that, because the road network image includes the road characteristic information and the vehicle characteristic information, the server can identify the vehicle information of different lanes by analyzing the road network image, so as to obtain the real-time road condition information at the lane level, thereby avoiding the problem that the acquisition of the lane-level real-time road condition cannot be realized due to the accuracy problem of the data source in the prior art.

And step S206, performing road network matching on the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain the road condition information of the road network.

In step S206, the server performs a road network matching process on the local road condition information corresponding to each image acquisition device according to the road network topology data, which is substantially a process of performing data fusion on the local road condition information corresponding to each image acquisition device. Optionally, the server may perform data fusion on the local road condition information corresponding to each road segment according to the position information of each image acquisition device in the road network topology data and the acquisition time of each image acquisition device for acquiring an image, for example, three cameras are arranged on one side of the road 1 according to the camera a, the camera B and the camera C, the server acquires the local road condition information corresponding to the camera a, the camera B and the camera C respectively in the process of performing road network matching, and then splices the local road condition information corresponding to the camera a, the camera B and the camera C at the same moment according to the position information of the camera a, the camera B and the camera C on the road 1, thereby obtaining the road condition information of the road segment 1 at the moment.

It should be noted that, in this embodiment, the acquisition of the image data is combined with the data fusion to obtain the road condition information of the road network, so that the real road condition information is directly obtained according to the road network image acquired by the image acquisition device, and then the data acquired by the image acquisition devices at different positions are fused to obtain the real-time road condition information of the whole road network, thereby avoiding the interference of the number of vehicles passing through the road in real time and the abnormal driving behavior of the vehicles, and improving the accuracy of the road condition information.

In addition, it should be noted that, in the prior art, the real-time road condition of the road network is generally generated by using GPS data based on the track of the floating car, specifically, the server needs to perform data access first, that is, the server needs to access the GPS data source of the floating car in real time, and acquire GPS information of the vehicle running on the road within a preset time period from the GPS data source, and then the server matches the GPS data of the single vehicle on the road network, so as to obtain the road condition information within a small segment range that each vehicle runs through. And finally, the server performs multi-vehicle processing, namely screening, counting, extracting characteristics and the like on the road condition information obtained by a plurality of single vehicles, and performing characteristic fusion on the obtained characteristics and other information to obtain the real-time road condition information of the road.

According to the scheme, in the prior art, when the road condition information is acquired, an indirect acquisition mode is adopted, new information statistics is carried out on the basis of sample vehicles running on a road, and the statistical result is easily interfered by an abnormal sample, so that the accuracy of acquiring the road condition information is influenced. In addition, when the prior art acquires road condition information, the accuracy of the road level can only be achieved, the granularity is large, and the measurement of the real-time road condition of the lane level cannot be realized, so that the passing state of the road cannot be accurately and finely depicted.

In order to solve the above problems in the prior art, in the embodiment of the present application, a road network image acquired by an image acquisition device in real time is used to determine road condition information, after road network topology data of a road network where at least one image acquisition device is located and the road network image acquired by at least one image acquisition device are acquired, feature extraction is performed on the road network image to obtain local road condition information of a road section where each image acquisition device is located, and then road network matching is performed on the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain the road condition information of the road network.

It is easy to notice that, in this embodiment, the road network image acquired by the image acquisition device can truly represent the traffic state of the road, that is, the scheme provided by the present application is a direct measurement of the road condition information of the road network, and is not interfered by the number of vehicles passing through the road in real time and/or abnormal driving behaviors of the vehicles, so as to ensure the accuracy of the road condition information. In addition, the road condition information is determined based on the road network image, and the vehicle information of different lanes can be identified through the road network image, namely the scheme provided by the application can acquire real-time road condition information of lane levels, so that the problem that the road condition information has errors due to the difference of traffic states of different lanes in the prior art is avoided, and the accuracy of acquiring the road condition information is further improved.

In an optional embodiment, in the process of acquiring the road condition information, the server mainly performs three steps, that is, a step of processing a road network image acquired by a single image acquisition device, a step of processing local road condition information acquired by a plurality of image acquisition devices, and an information adjusting step.

Optionally, fig. 3 shows a flow chart of a processing method for a road network image acquired by an optional single image acquisition device, as shown in fig. 3, a server is first connected with the image acquisition device, and then the road network image acquired by the image acquisition device is subjected to data processing, target detection, feature extraction, state classification and other operations, so that local road condition information of a road section where each image acquisition device is located can be obtained.

In an optional embodiment, the server may acquire road network topology data of the road network by using technologies such as a data system and big data of a traffic management department, and the server may obtain a road network image by processing an image acquired by each image acquisition device.

Specifically, the server first obtains an initial road network image acquired by at least one image acquisition device, then determines a trusted area corresponding to each image acquisition device, and performs screenshot processing on the corresponding initial road network image based on the trusted area corresponding to each image acquisition device to obtain a road network image corresponding to each image acquisition device. The credible area represents an image range with data accuracy higher than preset accuracy in the image acquired by each image acquisition device.

Optionally, the server is first connected to each image acquisition device to obtain an initial road network image acquired by each image acquisition device, and processes the acquired initial road network image at a preset frequency. For example, the server performs screenshot and image processing on the acquired initial road network image every 2 minutes.

It should be noted that, in the initial road network image collected by the image collecting device, only data in a partial region is reliable and accurate, for example, the image collecting device can collect an image within a range of 500 meters from the image collecting device, but may not clearly shoot a lane where a vehicle located 500 meters away from the image collecting device is located. Therefore, the data in the initial road network image collected by the image collecting device is not completely credible. In order to ensure the reliability of the data acquired by the image acquisition device, in the embodiment, the credible area is determined in the initial road network image acquired by the image acquisition device, and the road network image is determined based on the credible area.

In addition, it should be noted that the confidence region is related to the device parameters of the image capturing device, that is, the device parameters of the image capturing device determine the confidence region in the initial road network image. The device parameters of the image capturing device may include, but are not limited to, a focal length, a field angle, an aperture, and the like of the image capturing device. Furthermore, the trusted zone may also be related to the external environment in which the image capturing device is located, such as temperature, humidity, air pollution level, height of the image capturing device, and the like.

It should be noted that, in this embodiment, the server processes the acquired initial road network image at a preset frequency, which may avoid a problem of system resource occupation caused by real-time processing of the acquired initial road network image by the server.

Further, after the initial road network image acquired by at least one image acquisition device is processed to obtain the road network image, the server performs feature extraction on the road network image to obtain local road condition information of a road section where each image acquisition device is located. Specifically, the server extracts features of the road network image to obtain road feature information and vehicle feature information, and determines local road condition information of a road section where the current image acquisition device is located according to the road feature information and the vehicle feature information.

In the above process, the road characteristic information represents related information of a road segment where the image capturing device is located, where the road characteristic information at least includes: the lane line information of the road section where the current image acquisition equipment is located; the vehicle characteristic information represents the relevant information of the vehicle in the image acquired by the image acquisition equipment, wherein the vehicle characteristic information at least comprises the following components: vehicle position information, vehicle number information, and vehicle-to-vehicle distance information.

Optionally, the server may determine, according to the road characteristic information, information such as a number of lanes (for example, a single lane, a double lane, and the like), a type of a road segment (for example, a toll gate, an intersection, a junction, and a split junction, and the like), and a width of a lane of the road segment corresponding to the image capturing device, and the server determines, according to the vehicle characteristic information and the road characteristic information in the current road segment, an operation state of each lane, where the operation state represents a congestion state (for example, clear, general congestion, severe congestion, and the like) of each lane. For example, the server analyzes the vehicle characteristic information and the road characteristic information to obtain that the current road section is a single-lane road section, 10 vehicles appear in the road section, the distance between the 10 vehicles is small, and in 10 minutes, the 10 vehicles all appear in the image acquired by the image acquisition device, so that the server can judge that the single-lane road section is a congested road section; for another example, the server analyzes the vehicle characteristic information and the road characteristic information to know that the current road segment is a three-lane road segment, wherein 10 vehicles appear in the road segment of the left lane, and the distance between the 10 vehicles is small, and in 10 minutes, the 10 vehicles appear in the image acquired by the image acquisition device, and then the server can judge that the road segment of the left lane is a congested road segment; and the road section of the right lane has only two vehicles at the current moment, and the distance between the two vehicles is larger, so that the server can judge that the road section of the right lane is a smooth road section.

After the running state of each lane is obtained, the server determines the passing speed, the passing flow, the passing density and the like of the current road section according to the position of each lane on the current road section and the running state of each lane, and determines the passing speed, the passing flow and the passing density of the current road section according to the running states of all the lanes on the current road section to carry out weighted calculation so as to obtain the local road condition information of the current road section. The position of the lane on the current road segment is different, and the corresponding weight values are also different, for example, the weights corresponding to the middle lane and the left lane are different. Taking three lanes as an example for illustration, for example, if the weight corresponding to lane a is a, the weight corresponding to lane B is B, the weight corresponding to lane C is C, the traffic speeds corresponding to the three lanes are v1, v2 and v3 respectively, the traffic flow rates of the three lanes are s1, s2 and s3 respectively, and the traffic densities of the three lanes are m1, m2 and m3 respectively, the traffic speed v, the traffic flow rate s and the traffic density m of the link satisfy the following formulas respectively:

v＝a*v1+b*v2+c*v3

s＝a*s1+b*s2+c*s3

m＝a*m1+b*m2+c*m3

the server determines the real-time road condition of the road section by analyzing the passing speed, the passing flow and the passing density of the road section, for example, the server can perform data calculation on the passing speed, the passing flow and the passing density of the road section to obtain a calculation result, and determine the real-time road condition corresponding to the road section according to the numerical range where the calculation result is located.

Further, after the local road condition information corresponding to each image acquisition device is obtained, the server performs the processing step of the local road condition information acquired by the plurality of image acquisition devices, as shown in fig. 4, the server performs quality verification and data denoising on the local road condition information, performs road network matching and data fusion on the processed local road condition information, and performs missing completion on the missing data, thereby obtaining the road condition information of the road network.

In an optional embodiment, after feature extraction is performed on the road network image to obtain local road condition information of a road section where each image acquisition device is located, the server performs preprocessing on the local road condition information of the road section. Specifically, the server obtains local road condition information corresponding to at least one image acquisition device, removes abnormal data of the local road condition information, and obtains processed local road condition information.

Optionally, the server may determine an image parameter of an image acquired by each image acquisition device according to the device parameter of each image acquisition device, and then determine whether the local road condition information is abnormal according to the image parameter. For example, the acquisition unit of the image acquisition device a is not oriented to the road segment, but oriented to other places, so that the local road condition information corresponding to the image acquisition device a is not in accordance with the reality, at this time, the server determines that the local road condition information corresponding to the image acquisition device a is abnormal, rejects the local road condition information corresponding to the image acquisition device a, generates early warning information, and reminds related workers to adjust the acquisition angle of the image acquisition device or maintain the image acquisition device. For another example, the local traffic information corresponding to the image capturing device B indicates that the road segment corresponding to the image capturing device B is in a smooth state, but the local traffic information corresponding to the image capturing device C having the overlapped road segment with the local traffic corresponding to the image capturing device B indicates that the road segment corresponding to the image capturing device B is in a severe congestion state, and at this time, the server rejects the local traffic information corresponding to the image capturing device B.

It should be noted that, by performing quality verification and data denoising processing on the local road condition information corresponding to the single image acquisition device, the processed local road condition information accurately determines the road condition information of the road network, and further, the accuracy of the road condition information of the road network is improved.

Furthermore, after the quality verification and the data denoising are performed on the local road condition information, the server performs road network matching on the local road condition information corresponding to each image acquisition device according to road network topology data, so as to obtain the road condition information of the road network. Specifically, the server determines position information of the at least one image acquisition device in the road network according to the road network topology data, and performs data fusion on the processed local road condition information according to the position information and the acquisition time of the at least one image acquisition device for acquiring the images, so as to obtain the road condition information of the road network. For example, the local road condition information of the road segment corresponding to the image acquisition device a is mapped on the corresponding position in the road network topological graph, so that the local road condition information of the road segment corresponding to the image acquisition device a can be displayed on the navigation software of the user.

In addition, since the image capturing device captures the image of the road network in real time, the server needs to consider time factors in the process of matching the local road condition information of the road section corresponding to the image capturing device, that is, the local road conditions of the road sections corresponding to all the image capturing devices at the same time are mapped on the corresponding positions of the road network.

It should be noted that, in this embodiment, since the local traffic information corresponding to each image acquisition device is accurate, the obtained traffic information of the whole road network at the corresponding time is also accurate and reliable by matching and fusing the local traffic information corresponding to each image acquisition device, so as to ensure the accuracy of the traffic information of the whole road network.

In addition, it should be noted that, in practical applications, the image acquired by the image acquisition device may not cover all road segments of the road network, and therefore, after the local road condition information corresponding to each image acquisition device is subjected to road network matching according to the road network topology data to obtain the road condition information of the road network, the server needs to perform complementation of missing data to ensure the integrity of the road condition information of the road network.

Specifically, the server determines the missing road section according to the road network topology data and the road condition information of the road network, acquires historical road condition information of the missing road section, and then predicts the road condition information corresponding to the missing road section based on the historical road condition information. The missing road sections are road sections which are not covered by road condition information of the road network in the road network topology data. For example, the road segment corresponding to the image acquisition device a is road segment 1, the road segment corresponding to the image acquisition device B is road segment 2, the road segment 1 and the road segment 2 are connected through road segment 3, but no image acquisition device corresponds to the road segment 3, and at this time, the road condition information of the whole road network lacks the local road condition information corresponding to the road segment 3. In this scenario, the server predicts the current traffic information of the road segment 3 based on the historical traffic information of the road segment 3, for example, the current time is 5 pm 30 min, and the server predicts the current traffic information of 5 pm 30 min based on the historical traffic information of the road segment 3 at 5 pm 30 min.

In another optional embodiment, when the server predicts the traffic information corresponding to the missing road segment based on the historical traffic information of the missing road segment, the server may also consider the traffic information of other road segments associated with the missing road segment, for example, the road segment 1 and the road segment 2 are connected through the road segment 3, and the road segment 3 is the missing road segment, when predicting the traffic information of the road segment 3, besides considering the historical traffic information of the road segment 3, the traffic information of the road segment 1 and the road segment 2 may also be considered, and if the road segment 1 and the road segment 2 are in a congestion state, the road segment 3 is also in a congestion state at a high probability.

It should be noted that the traffic information of the missing road segment is predicted through the historical traffic information, so that the integrity of the traffic information of the road network is ensured.

In addition, it should be noted that, in practical applications, the problem that the road condition information determination may be inaccurate when determining the road condition information of the road network according to the road network image acquired by the image acquisition device is solved by performing road network matching on local road condition information corresponding to each image acquisition device according to road network topology data to obtain the road condition information of the road network, and then performing information adjustment on the road condition information by the server. As shown in fig. 5, in the process of adjusting the road condition information, the server firstly performs data grouping on the road segments, then identifies the road condition scene corresponding to the road segments, performs feature extraction on the road condition scene, adjusts the road condition information based on the extracted features, and then mounts the adjusted road condition information on the road segments of the corresponding road network, thereby generating the real-time map corresponding to the road network.

Specifically, after acquiring the road information of a plurality of road segments included in the road network, the server performs grouping processing on the plurality of road segments included in the road network based on the road information, determines a plurality of groups of road segments, identifies a road condition scene corresponding to each group of road segments, extracts the road information and road segment road condition information of each group of road segments based on the road condition scene, and adjusts the road condition information of the road network based on the road information and the road segment road condition information to obtain the adjusted road condition information. The road condition scene represents the congestion degree corresponding to each group of road sections.

In the above process, the road information of the plurality of road segments at least includes road types (e.g., toll gate, intersection, etc.) of the road segments, and the server may perform grouping processing on the plurality of road segments according to the road type of each road segment, where each group of road segments corresponds to one road type. And then the server identifies road condition scenes corresponding to the road sections of each road type, such as a road closed scene, a road unobstructed scene, a general congestion scene and a severe congestion scene. Further, the server extracts road information and road section road condition information in each group of road sections according to the identified road condition scene to determine whether the road information and the road section road condition information are matched with the road condition scene, if not, the server adjusts the road information and the road section road condition information of the road section and maps the adjusted road condition on different sections of different lanes of the road, thereby generating final real-time road condition information.

As can be seen from the above, the method for processing road condition information provided in this embodiment obtains relevant data of the local road condition information without interference by analyzing the road network image acquired by the image acquisition device at the road side, and then generates real-time and accurate road condition information of the road by integrating the road network topology data and the local road condition information corresponding to each image acquisition device along the road network by using a data fusion technology. The scheme provided by the embodiment is that the real-time road condition information is directly sensed based on the image information, and is not influenced by abnormal driving behaviors or the size of the statistical sample, so that the accuracy of the road condition information is improved. In addition, the scheme provided by the embodiment generates lane-level real-time road condition information, and in the image information, not only can the road traffic condition information be obtained, but also different lanes or the traffic states of the lanes can be distinguished. The real-time road condition information of the lane level can more accurately describe the road traffic state at special positions such as toll stations, intersections, junction and shunt ports and the like.

Therefore, the scheme provided by the embodiment can directly detect and identify the road condition information, and solves the problem that the traditional road condition detection algorithm is interfered by the number of real-time users and abnormal driving behaviors.

It should be noted that for simplicity of description, the above-mentioned embodiments of the method are described as a series of acts, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art will recognize that the embodiments described in this specification are preferred embodiments and that acts or modules referred to are not necessarily required for this application.

Through the above description of the embodiments, it is clear to those skilled in the art that the method for processing the traffic information according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the foregoing is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.

Example 2

According to an embodiment of the present application, a method for processing road condition information is also provided, as shown in fig. 6, the method includes:

step S602, the cloud server obtains the road segment position information of the target object in the road network.

In step S602, the target object may be a pedestrian, a vehicle driven by the driver, or an unmanned vehicle.

Optionally, fig. 7 is a schematic block diagram of a processing method of optional road condition information, which is described by taking a vehicle driven by a target object as a driver as an example. In the process of driving a vehicle, a driver initiates a road condition request through a navigation device (e.g., a vehicle-mounted navigation device), and a server receives the road condition request. After receiving the road condition request, the server positions the vehicle driven by the driver, so as to obtain the road section position information in the road network where the driver is located.

By acquiring the link position information of the target object in the road network, the server can acquire the corresponding road condition information corresponding to the link position information according to the link position information of the target object in the road network.

Step S604, the cloud server acquires the road network image acquired by at least one image acquisition device based on the road section position information, wherein the at least one image acquisition device is arranged in the road section range determined by the road section position information.

In step S604, after determining the road segment position information of the target object, the server may determine a target road segment in the road network of the target object based on the road segment position information of the target object and the destination to be reached by the target object, and then acquire the road network image acquired by at least one image acquisition device in the target road segment, for example, if the target object needs to drive a vehicle from a first place to a second place, the cloud server acquires the road network image acquired by each image acquisition device in the road segment from the first place to the second place.

In addition, in step S604, the at least one image capturing device may be a road camera, wherein the road camera is a camera disposed on both sides of the road for capturing the illegal driving behavior of the driver. Optionally, in this embodiment, at least one image capturing device is configured to capture a road network image of a corresponding road segment in a road network, so that the cloud server can obtain the road network image captured by the image capturing device in real time.

It should be noted that in step S604, the image capturing device may capture all road network images, for example, a camera on a highway that realizes full coverage of the camera may capture all road network images. In addition, at some specific time, only the road network image within a certain range (i.e. the local road network image) acquired by the image acquisition device may be acquired, and the acquired road network image may be processed. In addition, in step S604, the road network image acquired by each image acquisition device is acquired, so that the cloud server can generate the road condition information of the road network according to the road network image, and it is easy to note that the road network image acquired by the image acquisition device can directly detect the road condition information, thereby avoiding the problem of poor accuracy caused by acquiring the road condition information by an indirect measurement method based on a floating car GPS in the prior art.

Step 606, the cloud server extracts the features of the road network image to obtain the local road condition information of the road section where each image acquisition device is located.

In step S606, after the feature extraction is performed on the road network image, the obtained image features may include, but are not limited to, road feature information and vehicle feature information, where the road feature information represents relevant information of a road segment where the image capturing device is located, for example, information such as the number of lanes and the width of lanes; the vehicle characteristic information represents relevant information of the vehicles in the image acquired by the image acquisition device, such as distribution information of the vehicles on each lane, inter-vehicle distance between the vehicles and the like.

It should be noted that the cloud server may determine local road condition information of a road section where each image acquisition device is located by analyzing image features of the road network image, where the image features may represent traffic indexes corresponding to the current road section, such as indexes of a traffic speed, a traffic flow, a traffic density, and the like of the current road section; the local road condition information represents a traffic state corresponding to the current road section, and the traffic state may include, but is not limited to, a smooth state, a general congestion state, a severe congestion state, a road section closed state, and the like.

In addition, it should be noted that, because the road network image includes the road characteristic information and the vehicle characteristic information, the cloud server can identify the vehicle information of different lanes by analyzing the road network image, so as to obtain the real-time road condition information at lane level, thereby avoiding the problem in the prior art that the acquisition of the lane-level real-time road condition cannot be realized due to the accuracy problem of the data source.

Step S608, the cloud server performs road network matching on the local road condition information corresponding to each image acquisition device according to the road network topology data of the road network where the target object is located, so as to obtain the road condition information of the road network where the target object is located.

In step S608, the cloud server performs a road network matching process on the local road condition information corresponding to each image acquisition device according to the road network topology data, which is substantially a process of performing data fusion on the local road condition information corresponding to each image acquisition device. Optionally, the cloud server may perform data fusion on the local road condition information corresponding to each road segment according to the position information of each image acquisition device in the road network topology data and the acquisition time of each image acquisition device acquiring an image, for example, three cameras are arranged on one side of the road 1 according to the camera a, the camera B and the camera C, the cloud server acquires the local road condition information corresponding to the camera a, the camera B and the camera C respectively in the process of performing road network matching, and then the local road condition information corresponding to the camera a, the camera B and the camera C at the same moment is spliced according to the position information of the camera a, the camera B and the camera C on the road 1, so as to obtain the road condition information of the road segment 1 at the moment.

Step S610, the cloud server pushes the road condition information to the terminal device.

In step S610, the terminal device may be a device held by the target object or a terminal device mounted on the vehicle. In addition, after acquiring the road condition information, the cloud server pushes the road condition information to the terminal device, so that the terminal device can display the real-time road condition.

Based on the schemes defined in the above steps S602 to S610, it can be known that, in this embodiment of the application, a way of determining road condition information is performed by using a road network image acquired by an image acquisition device in real time, after obtaining road section position information of a target object in a road network, a cloud server obtains the road network image acquired by at least one image acquisition device based on the road section position information, performs feature extraction on the road network image to obtain local road condition information of a road section where each image acquisition device is located, then performs road network matching on the local road condition information corresponding to each image acquisition device according to road network topology data of the road network where the target object is located to obtain road condition information of the road network where the target object is located, and finally, pushes the road condition information to a terminal device. Wherein the at least one image acquisition device is arranged within the road section range determined by the road section position information.

It is easy to notice that, in this embodiment, the road network image acquired by the image acquisition device can truly represent the traffic state of the road, that is, the scheme provided by the present application is a direct measurement of the road condition information of the road network, and is not interfered by the number of vehicles passing through the road in real time and/or abnormal driving behaviors of the vehicles, so as to ensure the accuracy of the road condition information. In addition, the road condition information is determined based on the road network image, and the vehicle information of different lanes can be identified through the road network image, namely the scheme provided by the application can acquire the real-time road condition information of lane levels, so that the problem that the road condition information has errors due to the difference of the traffic states of different lanes in the prior art is solved, and the accuracy of acquiring the road condition information is further improved.

It should be noted that the cloud server may also serve as an execution subject of the method provided in embodiment 1, where the process of extracting features of the road network image and the process of matching the road network are already described in embodiment 1, and are not described herein again.

Example 3

According to an embodiment of the present application, a method for processing road condition information is also provided, as shown in fig. 8, the method includes:

step S802, responding to the road condition query instruction, and displaying the road section position information of the target object in the road network.

Step S804, obtaining road condition information of a road network where the target object is located from the server, and displaying the road condition information, where the road condition information is obtained by performing road network matching on local road condition information corresponding to each image acquisition device based on road network topology data of the road network where the target object is located, the local road condition information corresponding to each image acquisition device is obtained by performing feature extraction on a road network image acquired by the corresponding image acquisition device, and the corresponding image acquisition device is an image acquisition device arranged in a road section range determined by the road section position information.

Based on the schemes defined in steps S802 to S804, it can be known that, in the embodiment of the present application, a road network image acquired in real time by an image acquisition device is used to determine road condition information, after road network topology data of a road network where at least one image acquisition device is located and the road network image acquired by at least one image acquisition device are acquired, feature extraction is performed on the road network image to obtain local road condition information of a road section where each image acquisition device is located, and then road network matching is performed on the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain road condition information of the road network.

It should be noted that the server in this embodiment may execute the method for processing the traffic information provided in embodiment 1, and related contents are already described in embodiment 1 and are not described herein again.

Example 4

According to the embodiment of the present application, there is also provided a device for implementing the method for processing road condition information, as shown in fig. 9, the device 90 includes: an acquisition module 901, a feature extraction module 903, and a matching module 905.

The acquiring module 901 is configured to acquire road network topology data of a road network where at least one image acquisition device is located, and road network images acquired by the at least one image acquisition device; the feature extraction module 903 is configured to perform feature extraction on the road network image to obtain local road condition information of a road section where each image acquisition device is located; the matching module 905 is configured to perform road network matching on the local road condition information corresponding to each image acquisition device according to the road network topology data, so as to obtain road condition information of a road network.

It should be noted here that the acquiring module 901, the feature extracting module 903, and the matching module 905 correspond to steps S202 to S206 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the first embodiment.

Example 5

According to an embodiment of the present application, there is also provided a system for processing road condition information, including: a processor; and a memory. The memory is connected with the processor and used for storing executable instructions of the processor; wherein, the processor is configured to execute the instructions to implement the processing method of the traffic information according to embodiment 1.

Example 6

Embodiments of the present application may provide an electronic device, which may be any one of computer terminal devices in a computer terminal group. Alternatively, in this embodiment, the electronic device may be replaced with a terminal device such as a mobile terminal.

Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of a computer network.

In this embodiment, the electronic device includes one or more processors; a memory, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement a method for operating the programs, where the programs are configured to execute the method for processing the traffic information in the foregoing embodiments when running.

Optionally, fig. 10 is a block diagram of an electronic device according to an embodiment of the present application. As shown in fig. 10, the electronic device may include: one or more processors 1002 (only one of which is shown), memory 1004, and peripheral interfaces 1006.

The memory may be configured to store a software program and a module, such as a program instruction/module corresponding to the method and apparatus for processing traffic information in the embodiment of the present application, and the processor executes various functional applications and data processing by operating the software program and the module stored in the memory, so as to implement the method for processing traffic information. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, which may be connected to the electronic device 10 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 processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring road network topology data of a road network where at least one image acquisition device is located, and acquiring road network images acquired by the at least one image acquisition device; performing feature extraction on the road network image to obtain local road condition information of a road section where each image acquisition device is located; and carrying out road network matching on the local road condition information corresponding to each image acquisition device according to the road network topological data to obtain the road condition information of the road network.

Optionally, the processor may further execute the program code of the following steps: acquiring an initial road network image acquired by at least one image acquisition device; determining a credible area corresponding to each image acquisition device, wherein the credible area represents an image range with data accuracy higher than preset accuracy in the image acquired by each image acquisition device; and performing screenshot processing on the corresponding initial road network image based on the credible area corresponding to each image acquisition device to obtain the road network image corresponding to each image acquisition device.

Optionally, the processor may further execute the program code of the following steps: performing feature extraction on the road network image to obtain road feature information and vehicle feature information, wherein the road feature information at least comprises the following components: the lane line information of the road section where the current image acquisition equipment is located, the vehicle characteristic information at least comprises: vehicle position information, vehicle number information, and vehicle distance information; and determining local road condition information of the road section where the current image acquisition equipment is located according to the road characteristic information and the vehicle characteristic information.

Optionally, the processor may further execute the program code of the following steps: after the characteristics of the road network image are extracted to obtain the local road condition information of the road section where each image acquisition device is located, the local road condition information corresponding to at least one image acquisition device is obtained; and removing abnormal data of the local road condition information to obtain the processed local road condition information.

Optionally, the processor may further execute the program code of the following steps: determining position information of at least one image acquisition device in a road network according to road network topological data; and performing data fusion on the processed local road condition information according to the position information and the acquisition time of the image acquired by the at least one image acquisition device to obtain the road condition information of the road network.

Optionally, the processor may further execute the program code of the following steps: after local road condition information corresponding to each image acquisition device is subjected to road network matching according to road network topological data to obtain road condition information of a road network, determining a missing road section according to the road network topological data and the road condition information of the road network, wherein the missing road section is a road section which is not covered by the road condition information of the road network in the road network topological data; acquiring historical road condition information of a missing road section; and predicting the road condition information corresponding to the missing road section based on the historical road condition information.

Optionally, the processor may further execute the program code of the following steps: the method comprises the steps that road network matching is carried out on local road condition information corresponding to each image acquisition device according to road network topological data, road condition information of a road network is obtained, and then road information of a plurality of road sections contained in the road network is obtained; grouping a plurality of road sections contained in a road network based on road information to determine a plurality of groups of road sections; identifying a road condition scene corresponding to each group of road sections, wherein the road condition scene represents the congestion degree corresponding to each group of road sections; extracting road information and road section road condition information of each group of road sections based on the road condition scene; and adjusting road condition information of the road network based on the road information and road section road condition information to obtain the adjusted road condition information.

Optionally, the processor may further execute the program code of the following steps: the method comprises the steps that a cloud server obtains road section position information of a target object in a road network; the cloud server acquires road network images acquired by at least one image acquisition device based on the road section position information, wherein the at least one image acquisition device is arranged in a road section range determined by the road section position information; the cloud server extracts the characteristics of the road network image to obtain local road condition information of a road section where each image acquisition device is located; the cloud server performs road network matching on the local road condition information corresponding to each image acquisition device according to road network topology data of a road network where the target object is located to obtain road condition information of the road network where the target object is located; the cloud server pushes the road condition information to the terminal equipment.

Optionally, the processor may further execute the program code of the following steps: responding to a road condition query instruction, and displaying road section position information of a target object in a road network; the method comprises the steps of obtaining road condition information of a road network where a target object is located from a server, and displaying the road condition information, wherein the road condition information is obtained by performing road network matching on local road condition information corresponding to each image acquisition device based on road network topology data of the road network where the target object is located, the local road condition information corresponding to each image acquisition device is obtained by performing feature extraction on a road network image acquired by the corresponding image acquisition device, and the corresponding image acquisition device is an image acquisition device arranged in a road section range determined by road section position information.

It can be understood by those skilled in the art that the structure shown in fig. 10 is only an illustration, and the computer terminal may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 10 is a diagram illustrating a structure of the electronic device. For example, the electronic device 10 may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 10, or have a different configuration than shown in FIG. 10.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

Example 7

Embodiments of the present application also provide a computer-readable storage medium. Optionally, in this embodiment, the computer-readable storage medium may be configured to store a program code executed by the processing method for road condition information provided in the foregoing embodiment.

Optionally, in this embodiment, the computer-readable storage medium may be located in any one of a group of computer terminals in a computer network, or in any one of a group of mobile terminals.

Optionally, in this embodiment, a computer program is stored in the computer-readable storage medium, where the computer program is configured to execute the processing method of the traffic information in the above embodiment when running.

Optionally, in this embodiment, a computer-readable storage medium is configured to store program code for performing the steps of: acquiring road network topology data of a road network where at least one image acquisition device is located, and acquiring road network images by the at least one image acquisition device; performing feature extraction on the road network image to obtain local road condition information of a road section where each image acquisition device is located; and carrying out road network matching on the local road condition information corresponding to each image acquisition device according to the road network topological data to obtain the road condition information of the road network.

Optionally, in this embodiment, a computer-readable storage medium is configured to store program code for performing the steps of: acquiring an initial road network image acquired by at least one image acquisition device; determining a credible area corresponding to each image acquisition device, wherein the credible area represents an image range with data accuracy higher than preset accuracy in the image acquired by each image acquisition device; and performing screenshot processing on the corresponding initial road network image based on the credible area corresponding to each image acquisition device to obtain the road network image corresponding to each image acquisition device.

Optionally, in this embodiment, a computer-readable storage medium is configured to store program code for performing the steps of: extracting the characteristics of the road network image to obtain road characteristic information and vehicle characteristic information, wherein the road characteristic information at least comprises the following steps: the lane line information of the road section where the current image acquisition equipment is located, the vehicle characteristic information at least comprises: vehicle position information, vehicle number information, and vehicle interval information; and determining local road condition information of the road section where the current image acquisition equipment is located according to the road characteristic information and the vehicle characteristic information.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: after the characteristics of the road network image are extracted to obtain the local road condition information of the road section where each image acquisition device is located, the local road condition information corresponding to at least one image acquisition device is obtained; and removing abnormal data of the local road condition information to obtain the processed local road condition information.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: determining position information of at least one image acquisition device in a road network according to road network topological data; and performing data fusion on the processed local road condition information according to the position information and the acquisition time of the image acquired by the at least one image acquisition device to obtain the road condition information of the road network.

Optionally, in this embodiment, a computer-readable storage medium is configured to store program code for performing the steps of: after local road condition information corresponding to each image acquisition device is subjected to road network matching according to road network topological data to obtain road condition information of a road network, determining a missing road section according to the road network topological data and the road condition information of the road network, wherein the missing road section is a road section which is not covered by the road condition information of the road network in the road network topological data; acquiring historical road condition information of a missing road section; and predicting the road condition information corresponding to the missing road section based on the historical road condition information.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: the method comprises the steps that road network matching is carried out on local road condition information corresponding to each image acquisition device according to road network topological data, road condition information of a road network is obtained, and then road information of a plurality of road sections contained in the road network is obtained; grouping a plurality of road sections contained in a road network based on road information to determine a plurality of groups of road sections; identifying a road condition scene corresponding to each group of road sections, wherein the road condition scene represents the congestion degree corresponding to each group of road sections; extracting road information and road section road condition information of each group of road sections based on the road condition scene; and adjusting road condition information of the road network based on the road information and road section road condition information to obtain the adjusted road condition information.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: the method comprises the steps that a cloud server obtains road section position information of a target object in a road network; the cloud server acquires road network images acquired by at least one image acquisition device based on the road section position information, wherein the at least one image acquisition device is arranged in a road section range determined by the road section position information; the cloud server extracts the characteristics of the road network image to obtain local road condition information of a road section where each image acquisition device is located; the cloud server performs road network matching on the local road condition information corresponding to each image acquisition device according to road network topology data of a road network where the target object is located to obtain road condition information of the road network where the target object is located; the cloud server pushes the road condition information to the terminal equipment.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: responding to a road condition query instruction, and displaying road section position information of a target object in a road network; the method comprises the steps of obtaining road condition information of a road network where a target object is located from a server, and displaying the road condition information, wherein the road condition information is obtained by performing road network matching on local road condition information corresponding to each image acquisition device based on road network topology data of the road network where the target object is located, the local road condition information corresponding to each image acquisition device is obtained by performing feature extraction on a road network image acquired by the corresponding image acquisition device, and the corresponding image acquisition device is an image acquisition device arranged in a road section range determined by road section position information.

The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the advantages and disadvantages of the embodiments.

In the embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be implemented in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.

Claims

1. A method for processing traffic information, comprising:

the method comprises the steps of obtaining road network topology data of a road network where at least one image acquisition device is located, and road network images acquired by the at least one image acquisition device;

extracting the characteristics of the road network image to obtain local road condition information of a road section where each image acquisition device is located;

and performing road network matching on the local road condition information corresponding to each image acquisition device according to the road network topological data to obtain the road condition information of the road network.

2. The method of claim 1, wherein obtaining the road network image captured by the at least one image capture device comprises:

acquiring an initial road network image acquired by the at least one image acquisition device;

determining a credible area corresponding to each image acquisition device, wherein the credible area represents an image range with data accuracy higher than preset accuracy in an image acquired by each image acquisition device;

And performing screenshot processing on the corresponding initial road network image based on the credible area corresponding to each image acquisition device to obtain the road network image corresponding to each image acquisition device.

3. The method according to claim 1, wherein the extracting the features of the road network image to obtain the local road condition information of the road segment where each image acquisition device is located comprises:

extracting the features of the road network image to obtain road feature information and vehicle feature information, wherein the road feature information at least comprises the following components: the lane line information of the road section where the current image acquisition equipment is located, and the vehicle characteristic information at least comprises the following components: vehicle position information, vehicle number information, and vehicle distance information;

and determining local road condition information of the road section where the current image acquisition equipment is located according to the road characteristic information and the vehicle characteristic information.

4. The method according to claim 3, wherein after the feature extraction is performed on the road network image to obtain the local road condition information of the road segment where each image acquisition device is located, the method further comprises:

acquiring local road condition information corresponding to the at least one image acquisition device;

And removing abnormal data of the local road condition information to obtain the processed local road condition information.

5. The method according to claim 4, wherein performing road network matching on the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain the road condition information of the road network comprises:

determining the position information of the at least one image acquisition device in the road network according to the road network topology data;

and performing data fusion on the processed local road condition information according to the position information and the acquisition time of the image acquired by the at least one image acquisition device to obtain the road condition information of the road network.

6. The method according to claim 1, wherein after performing road network matching on the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain the road condition information of the road network, the method further comprises:

determining a missing road section according to the road network topological data and the road condition information of the road network, wherein the missing road section is a road section which is not covered by the road condition information of the road network in the road network topological data;

Acquiring historical road condition information of the missing road section;

and predicting the road condition information corresponding to the missing road section based on the historical road condition information.

7. The method according to claim 1, wherein after performing road network matching on the local road condition information corresponding to each image acquisition device according to the road network topology data to obtain the road condition information of the road network, the method further comprises:

acquiring road information of a plurality of road sections contained in the road network;

grouping a plurality of road segments contained in the road network based on the road information to determine a plurality of groups of road segments;

identifying a road condition scene corresponding to each group of road sections, wherein the road condition scene represents the congestion degree corresponding to each group of road sections;

extracting road information and road section road condition information of each group of road sections based on the road condition scene;

and adjusting the road condition information of the road network based on the road information and the road section road condition information to obtain the adjusted road condition information.

8. A method for processing traffic information, comprising:

the method comprises the steps that a cloud server obtains road section position information of a target object in a road network;

the cloud server acquires road network images acquired by at least one image acquisition device based on the road section position information, wherein the at least one image acquisition device is arranged in a road section range determined by the road section position information;

The cloud server extracts the characteristics of the road network image to obtain local road condition information of a road section where each image acquisition device is located;

the cloud server performs road network matching on the local road condition information corresponding to each image acquisition device according to road network topology data of a road network where the target object is located to obtain road condition information of the road network where the target object is located;

and the cloud server pushes the road condition information to the terminal equipment.

9. A method for processing road condition information is characterized by comprising the following steps:

responding to a road condition query instruction, and displaying road section position information of a target object in a road network;

and acquiring road condition information of a road network where the target object is located from a server, and displaying the road condition information, wherein the road condition information is obtained by performing road network matching on local road condition information corresponding to each image acquisition device based on road network topology data of the road network where the target object is located, the local road condition information corresponding to each image acquisition device is obtained by performing feature extraction on a road network image acquired by the corresponding image acquisition device, and the corresponding image acquisition device is an image acquisition device arranged in a road section range determined by the road section position information.

10. A traffic information processing apparatus, comprising:

the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring road network topology data of a road network where at least one image acquisition device is located and road network images acquired by the at least one image acquisition device;

the characteristic extraction module is used for extracting the characteristics of the road network image to obtain local road condition information of a road section where each image acquisition device is located;

and the matching module is used for performing road network matching on the local road condition information corresponding to each image acquisition device according to the road network topological data to obtain the road condition information of the road network.

11. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the method for processing road condition information according to any one of claims 1 to 9 when the computer program runs.

12. An electronic device, characterized in that the electronic device comprises one or more processors; a memory for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method for executing a program, wherein the program is configured to execute the method for processing road condition information according to any one of claims 1 to 9 when executed.

13. A system for processing traffic information, comprising:

a processor; and

a memory coupled to the processor for storing executable instructions of the processor; the processor is configured to execute the instructions to implement the method for processing the traffic information according to any one of claims 1 to 9.