CN114677843B

CN114677843B - Road condition information processing method, device, system and electronic equipment

Info

Publication number: CN114677843B
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: 2023-07-21
Anticipated expiration: 2042-02-17
Also published as: CN114677843A

Abstract

The application discloses a road condition information processing method, device and system 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 road network images acquired by the at least one image acquisition device; extracting features of the road network images to obtain local road condition information of the 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. The technical problem of the poor accuracy of road conditions information acquisition among the prior art has been solved to this application.

Description

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

Technical Field

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

Background

In daily life, people usually use navigation maps to conduct navigation route planning, real-time road condition information broadcasting, travel time prediction and the like when traveling. In the current road traffic control and active management and control scene, real-time road condition information is generally used to obtain the traffic state at the current moment, predict future traffic evolution trend and perform early warning management and control in advance. However, the wrong road condition information not only can reduce the use experience of the user, but also can bring hidden danger to traffic safety and life and property safety.

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

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

Disclosure of Invention

The embodiment of the application provides a method, a device, a system and electronic equipment for processing road condition information, which are used for at least solving the technical problem of poor accuracy of road condition information acquisition in the prior art.

According to an aspect of the embodiments of the present application, there is provided a method for processing traffic information, including: 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; extracting features of the road network images to obtain local road condition information of the 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 embodiments of the present application, there is further provided a method for processing road condition information, including: the cloud server acquires the road section position information of the target object in the road network; the cloud server acquires road network images acquired by at least one image acquisition device based on 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 performs feature extraction on the road network images to obtain local road condition information of the 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 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; and the cloud server pushes the road condition information to the terminal equipment.

According to another aspect of the embodiments of the present application, there is further provided a method for processing road condition information, including: responding to the road condition inquiry command, and displaying the road section position information of the target object in the road network; road condition information of a road network where the target object is located is obtained from a server, and the road condition information is displayed, wherein the road condition information is obtained by carrying out 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 carrying out feature extraction on road network images 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 feature extraction module is used for extracting features of the road network images to obtain local road condition information of the road section where each image acquisition device is located; and the matching module is used for 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 embodiments of the present application, there is also provided a computer readable storage medium having a computer program stored therein, wherein the computer program is configured to execute the above-described 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; and 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 running the programs, wherein the programs are configured to perform the road condition information processing method described above when run.

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 instructions to implement the method for processing road condition information.

In the embodiment of the application, a road network image acquired by an image acquisition device in real time is adopted 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, characteristic extraction is carried out 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 carried out 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.

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 the real-time road condition information of the lane level, so that the problem that the road condition information has errors due to the difference of traffic states among different lanes in the prior art is avoided, and the accuracy of road condition information acquisition is further improved.

Therefore, the scheme provided by the application achieves the purpose of acquiring the road condition information, so that the technical effect of improving the accuracy of road condition information acquisition is achieved, and the technical problem of poor accuracy of road condition information acquisition 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 embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

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

FIG. 2 is a flowchart of a method for processing traffic information according to an embodiment of the present application;

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

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

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

FIG. 6 is a flowchart of a method for processing traffic information according to an embodiment of the present application;

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

FIG. 8 is a flowchart of a method for processing traffic 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 present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise 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 embodiments of the present application, there is further provided an embodiment of a method for processing traffic information, where the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and although a logic order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that shown or described herein.

The method embodiment provided in 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 block diagram of a hardware structure of an electronic device (or mobile device) for implementing a processing method of traffic information. As shown in fig. 1, the electronic device 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …,102 n) processors (which may include, but are not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, the method may further include: 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 supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the 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 circuits described above may be referred to herein generally as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. 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 present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).

The memory 104 may be used for storing software programs and modules of application software, such as program instructions/data storage devices corresponding to the processing method of road condition information in the embodiment of the present application, and the processor executes the software programs and modules stored in the memory 104, thereby executing various functional applications and data processing, that is, implementing the processing method of road condition information. 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, memory 104 may further include memory located remotely from the processor, which may be connected to 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 means 106 is arranged to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communications provider of the electronic device 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

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 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 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 road condition information, and the system may further include a terminal device, where the terminal device may send a road condition information obtaining instruction to the server, and receive real-time road condition information returned by the server according to execution of road condition information obtaining.

In the above 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 an 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 and road network images acquired by the at least one image acquisition device are acquired.

In step S202, the at least one image capturing device may be a road camera, where the road camera is a camera disposed on two sides of a road for capturing an offending driving behavior of a driver. Optionally, in this embodiment, at least one image capturing device is configured to capture road network images of corresponding road segments in the road network, so that the server can acquire the road network images captured by the image capturing device in real time.

In addition, in step S202, the road network is a road network in the traffic field, which limits the movement track of the moving object, wherein the moving object may be, but is not limited to, pedestrians, vehicles. The road network topology data is topology data of a road network, and the topology data at least comprises a road section type, a road section width, a road section position, a road section length, a connection relationship between road sections and the like. Optionally, the server may acquire road network topology data from the data system of the traffic management department, and may also acquire road network topology data by way 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 implements the full coverage of the camera may capture all road network images. In addition, at certain specific moments, only road network images (i.e., local road network images) within a certain range acquired by the image acquisition device can be acquired, and the acquired road network images can be processed. In addition, in step S202, by acquiring the road network image acquired by each image acquisition device, the server is enabled to generate road condition information of the road network according to the road network image, and it is easy to notice that the road condition information can be directly detected by the road network image acquired by the image acquisition device, so that the problem of poor accuracy caused by acquiring the road condition information based on the indirect measurement method of the floating car GPS in the prior art is avoided.

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

In step S204, after 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 characterizes related information of a road section where the image acquisition device is located, for example, information such as the number of lanes, the width of lanes, and the like; the vehicle characteristic information characterizes information about vehicles in the image acquired by the image acquisition device, such as information about distribution of the vehicles on each lane, inter-vehicle distances between the vehicles, and the like.

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

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

Step S206, road network matching is carried out on the local road condition information corresponding to each image acquisition device according to the road network topology data, and the road condition information of the road network is obtained.

In step S206, the process of performing road network matching on the local road condition information corresponding to each image acquisition device by the server according to the road network topology data is essentially 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 local road condition information corresponding to each road section 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 set on one side of the road 1 according to the camera a, the camera B and the camera C, the server obtains the local road condition information corresponding to the camera a, the camera B and the camera C respectively in the road network matching process, 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 section 1 at the moment.

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

In addition, it should be noted that in the prior art, the real-time road condition of the road network is generally generated by adopting the GPS data based on the track of the floating vehicle, specifically, the server needs to access data firstly, that is, the server needs to access the GPS data source of the floating vehicle in real time, obtain the GPS information of the vehicle running on the road in a preset time period from the GPS data source, and then the server matches the GPS data of the individual vehicle on the road network, so as to obtain the road condition information in the small section range where each vehicle runs. And finally, the server performs multi-vehicle processing, namely, screening, counting, extracting characteristics and the like on road condition information obtained by a plurality of single vehicles, and performing characteristic fusion on the obtained characteristics and other information, so as to obtain 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 based on a sample vehicle running on a road, and the statistics result is easy to be interfered by an abnormal sample, so that the accuracy of acquiring the road condition information is affected. In addition, the prior art can only achieve the accuracy of road level and has larger granularity when acquiring road condition information, and can not realize the measurement of real-time road condition of the lane level, thereby not accurately and finely describing the traffic state of the road.

In order to solve the above-mentioned problems in the prior art, in this embodiment, a way of determining road condition information by using road network images acquired by image acquisition devices in real time is adopted, after road network topology data of a road network where at least one image acquisition device is located and the road network images acquired by at least one image acquisition device are acquired, feature extraction is performed on the road network images 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 is easy to notice that, in this embodiment, the road network image collected by the image collecting device can truly represent the traffic state of the road, that is, 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 the real-time road condition information of the lane level, so that the problem that the road condition information has errors due to the difference of traffic states among different lanes in the prior art is avoided, and the accuracy of road condition information acquisition is further improved.

In an alternative embodiment, in the process of obtaining the road condition information, the server mainly executes three steps, namely, a processing step of road network images collected by a single image collecting device, a processing step of local road condition information collected by a plurality of image collecting devices and an information adjusting step.

Optionally, fig. 3 shows a flowchart of a processing method of a road network image acquired by an optional single image acquisition device, and as can be seen from fig. 3, the server is connected with the image acquisition device first, and then performs operations such as data processing, object detection, feature extraction, and state classification on the road network image acquired by the image acquisition device, so as to obtain local road condition information of a road section where each image acquisition device is located.

In an alternative embodiment, the server may obtain road network topology data of the road network through a data system, big data and other technologies of the traffic management department, and the server may obtain the road network image by processing the image acquired by each image acquisition device.

Specifically, the server firstly acquires 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 the road network image corresponding to each image acquisition device. The trusted region characterizes an image range in which the accuracy of data in images acquired by each image acquisition device is higher than a preset accuracy.

Optionally, the server is first connected to each image capturing device to obtain an initial road network image captured by each image capturing device, and processes the captured initial road network image at a preset frequency. For example, the server captures and processes the acquired initial road network image every 2 minutes.

It should be noted that, in the initial road network image acquired by the image acquisition device, only a part of the data in the area is reliable and accurate, for example, the image acquisition device can acquire an image within a range of 500 meters from the image acquisition device, but may not be able to clearly capture a lane where a vehicle located 500 meters away from the image acquisition device is located. Thus, the data in the initial road network image acquired by the image acquisition device is not completely trusted. In order to ensure the reliability of the data acquired by the image acquisition device, in this embodiment, a trusted region is determined in the initial road network image acquired by the image acquisition device, and the road network image is determined based on the trusted region.

Furthermore, it should be noted that the trusted region is related to the device parameters of the image capturing device, i.e. the device parameters of the image capturing device determine the trusted 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, etc. of the image capturing device. In addition, the trusted region may also be related to the external environment in which the image capture device is located, such as temperature, humidity, air pollution level, height of the image capture device, and the like.

It should be noted that in this embodiment, the server processes the collected initial road network image with a preset frequency, so that the problem that the server occupies system resources caused by real-time processing of the collected initial road network image can be avoided.

Further, after processing the initial road network image acquired by at least one image acquisition device to obtain a 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 performs feature extraction on 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 characterizes the relevant information of the road section where the image acquisition device is located, where the road characteristic information at least includes: lane line information of a road section where the current image acquisition equipment is located; the vehicle characteristic information characterizes the relevant information of the vehicle in the image acquired by the image acquisition device, wherein the vehicle characteristic information at least comprises: vehicle position information, vehicle number information, and vehicle pitch information.

Optionally, the server may determine, according to the road feature information, information such as the number of lanes (e.g., single lanes, double lanes, etc.), the type of the road section (e.g., toll gate, intersection, junction, etc.), and the width of the road section, where the server determines, according to the vehicle feature information and the road feature information in the current road section, an operation state of each lane, where the operation state characterizes a congestion state (e.g., clear, general congestion, severe congestion, etc.) of each lane. For example, the server obtains through analysis of the vehicle characteristic information and the road characteristic information 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 smaller, and in 10 minutes, the server can judge that the single-lane road section is a congestion road section when the 10 vehicles appear in the image acquired by the image acquisition device; for another example, the server obtains through analysis of the vehicle characteristic information and the road characteristic information that the current road section is a three-lane road section, wherein 10 vehicles appear in the road section of the left lane, the distance between the 10 vehicles is smaller, and in 10 minutes, the 10 vehicles appear in the image acquired by the image acquisition device, so that the server can judge that the road section of the left lane is a congestion road section; and only two vehicles exist in the road section of the right lane at the current moment, and the distance between the two vehicles is larger, 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 traffic speed, traffic flow, traffic density and the like of the current road section by combining the position of each lane on the current road section and the running state of each lane, and performs weighted calculation on the traffic speed, traffic flow and traffic density of the current road section by determining the running state of all lanes on the current road section to obtain the local road condition information of the current road section. Wherein the positions of the lanes on the current road section are different, and the corresponding weight values are also different, for example, the weights of the middle lane and the left lane are different. Taking three lanes as an example for illustration, for example, the weight corresponding to the lane a is a, the weight corresponding to the lane B is B, the weight corresponding to the lane C is C, the traffic speeds corresponding to the three lanes are v1, v2 and v3 respectively, the traffic flows of the three lanes are s1, s2 and s3 respectively, and the traffic densities of the three lanes are m1, m2 and m3 respectively, then the traffic speed v, the traffic flow s and the traffic density m of the road section respectively satisfy the following formulas:

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 traffic speed, traffic flow and traffic density of the road section, for example, the server can calculate the traffic speed, traffic flow and traffic 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 obtaining the local road condition information corresponding to each image acquisition device, the server executes the processing steps of the local road condition information acquired by the plurality of image acquisition devices, as shown in fig. 4, the server performs quality check and data denoising processing on the local road condition information first, then performs road network matching and data fusion on the processed local road condition information, and performs missing complement on missing data at the same time, thereby obtaining the road condition information of the road network.

In an alternative embodiment, after extracting features of the road network image to obtain local road condition information of the 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 acquires local road condition information corresponding to at least one image acquisition device, removes abnormal data of the local road condition information, and obtains the 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 collecting unit of the image collecting device a does not face the road section, but faces other places, so that the local road condition information corresponding to the image collecting device a is inconsistent with the actual road condition information, at this time, the server determines that the local road condition information corresponding to the image collecting device a is abnormal, eliminates the local road condition information corresponding to the image collecting device a, generates early warning information, and reminds relevant staff to adjust the collecting angle of the image collecting device or maintain the image collecting device. For another example, the local road condition information corresponding to the image capturing device B indicates that the road section corresponding to the image capturing device B is in a smooth state, but the road section corresponding to the local road condition information surface image capturing device B corresponding to the image capturing device C having an overlapped road section corresponding to the image capturing device B is in a serious congestion state, and at this time, the server eliminates the local road condition information corresponding to the image capturing device B.

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

Furthermore, after the quality check 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 the road network topology data, so that the road condition information of the road network is obtained. Specifically, the server determines the position information of 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 image acquired by the at least one image acquisition device to obtain the road condition information of the road network. For example, the local road condition information of the road section 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 section corresponding to the image acquisition device a can be displayed on the navigation software of the user.

In addition, because the image acquisition equipment acquires road network images in real time, the server also needs to consider time factors in the process of road network matching of the local road condition information of the road section corresponding to the image acquisition equipment, namely, the local road conditions of the road sections corresponding to all the image acquisition equipment at the same moment are mapped on the corresponding positions of the road network.

It should be noted that, in this embodiment, because the local road condition information corresponding to each image capturing device is accurate, the road condition information of the whole road network at the corresponding moment is also accurate and reliable by matching and fusing the local road condition information corresponding to each image capturing device, so that the accuracy of the road condition information of the whole road network is ensured.

In addition, it should be noted that, in practical application, the image acquired by the image acquisition device may not cover all road segments of the road network, so after the road network matching is performed on the local road condition information corresponding to each image acquisition device according to the road network topology data, the server needs to complete the missing data to ensure the integrity of the road condition information of the road network after the road condition information of the road network is obtained.

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

In another alternative embodiment, the server may consider the road condition information of other road segments associated with the missing road segment when predicting the road condition information corresponding to the missing road segment based on the historical road condition information of 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 condition information of the road segment 1 and the road segment 2 may be considered when predicting the road condition information of the road segment 3, 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 with a high probability.

It should be noted that, the road condition information of the missing road section is predicted by the historical road condition information, so that the integrity of the road condition information of the road network is ensured.

In addition, it should be noted that in practical application, the road condition information of the road network may be determined inaccurately by determining the road condition information of the road network through the road network image acquired by the image acquisition device. As shown in fig. 5, in the process of adjusting road condition information, a server firstly performs data grouping on road segments, then identifies road condition scenes corresponding to the road segments, performs feature extraction on the road condition scenes, 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 a real-time map corresponding to the road network.

Specifically, after obtaining road information of a plurality of road segments included in a road network, a 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 road condition scenes corresponding to the road segments, extracts the road information and road segment road condition information of the road segments based on the road condition scenes, and adjusts the road condition information of the road network based on the road information and the road segment road condition information to obtain adjusted road condition information. The road condition scene characterizes the congestion degree corresponding to each group of road sections.

In the above process, the road information of the plurality of road segments includes at least a road type (for example, a toll gate, an intersection, etc.) of the road segment, 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. The server then identifies road condition scenes, such as a road closure scene, a road smoothness scene, a general congestion scene, and a severe congestion scene, corresponding to the road segments of each road type. 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 road information and the road section road condition information of the road section are adjusted, and the adjusted road conditions are mapped on different segments of different lanes of the road, so that final real-time road condition information is produced.

As can be seen from the above, the road condition information processing method provided by the embodiment analyzes the road network image acquired by the image acquisition device at the road side to obtain related data of the local road condition information with interference removed, and then synthesizes road network topology data and the local road condition information corresponding to each image acquisition device along the road network by adopting the data fusion technology to produce real-time and accurate road condition information of the road. Because the scheme provided by the embodiment is based on the image information to directly sense the real-time road condition information, the real-time road condition information is not influenced by abnormal driving behaviors and statistical sample sizes, and therefore the accuracy of the road condition information is improved. In addition, the scheme provided by the embodiment yields real-time road condition information at the level of the lane, and in the image information, not only the road traffic condition information can be obtained, but also different lanes or traffic states of the lanes can be distinguished. The real-time road condition information of the lane level can more accurately depict the road traffic state at special positions such as toll stations, intersections, junction and shunt ports.

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 foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

From the above description of the embodiments, it will be clear to those skilled in the art that the method for processing road condition information according to the above embodiments may be implemented by software plus a necessary general hardware platform, or may be implemented by hardware, but in many cases, the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.

Example 2

According to an embodiment of the present application, there is further provided a method for processing traffic information, as shown in fig. 6, where the method includes:

in step S602, the cloud server acquires 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 a driver, or an unmanned vehicle.

Alternatively, fig. 7 is a schematic block diagram showing an alternative road condition information processing method, and an example of a vehicle driven by a target object is illustrated. In the process that the driver drives the vehicle, the driver initiates a road condition request through navigation equipment (for example, vehicle-mounted navigation equipment), and the server can receive 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 of the road network where the driver is located.

It should be noted that, by acquiring the road segment position information of the target object in the road network, the server can acquire the corresponding road condition information corresponding to the road segment position information according to the road segment position information of the target object in the road network.

In step S604, the cloud server acquires the road network image acquired by the at least one image acquisition device based on the road segment position information, where the at least one image acquisition device is set in the road segment range determined by the road segment position information.

In step S604, after determining the road segment location information of the target object, the server may determine a target road segment of the target object in the road network based on the road segment location information of the target object and the destination to which the target object is to reach, and then acquire a road network image acquired by at least one image acquisition device in the target road segment, for example, the target object needs to drive a vehicle from the first place to the second place, and the cloud server acquires a 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, where the road camera is a camera disposed at both sides of a road for capturing an offending driving behavior of a driver. Optionally, in this embodiment, at least one image capturing device is configured to capture road network images of a corresponding road section in the road network, so that the cloud server can acquire the road network images 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 implements the full coverage of the camera may capture all road network images. In addition, at certain specific moments, only road network images (i.e., local road network images) within a certain range acquired by the image acquisition device can be acquired, and the acquired road network images can be processed. In addition, in step S604, by acquiring the road network image acquired by each image acquisition device, the cloud server is enabled to generate road condition information of the road network according to the road network image, and it is easy to notice that the road condition information can be directly detected by the road network image acquired by the image acquisition device, so that the problem of poor accuracy caused by acquiring the road condition information based on the indirect measurement method of the floating car GPS in the prior art is avoided.

In step S606, the cloud server performs feature extraction on the road network image to obtain local road condition information of the road section where each image acquisition device is located.

In step S606, after 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 characterizes related information of a road section where the image acquisition device is located, for example, information such as the number of lanes, the width of lanes, and the like; the vehicle characteristic information characterizes information about vehicles in the image acquired by the image acquisition device, such as information about distribution of the vehicles on each lane, inter-vehicle distances between the vehicles, and the like.

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

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

In 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 process of road network matching for the local road condition information corresponding to each image acquisition device by the cloud server according to the road network topology data is essentially a process of data fusion for the local road condition information corresponding to each image acquisition device. Optionally, the cloud server may perform data fusion on local road condition information corresponding to each road section according to position information of each image acquisition device in road network topology data and acquisition time of each image acquisition device for acquiring images, for example, three cameras are set on one side of the road 1 according to the camera a, the camera B and the camera C, the cloud server respectively acquires the local road condition information corresponding to the camera a, the camera B and the camera C in the road network matching process, 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 section 1 at the moment.

In 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 may be a terminal device mounted on the vehicle. In addition, after the cloud server acquires the road condition information, the road condition information is pushed to the terminal equipment, so that the terminal equipment can display real-time road conditions.

Based on the scheme defined in the steps S602 to S610, it may be known that in the embodiment of the present application, the road network image acquired by the image acquisition device in real time is used to determine the road condition information, after the road section position information of the target object in the road network is acquired, the cloud server acquires 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 the local road condition information of the 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 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, and finally pushes the road condition information to the terminal device. Wherein the at least one image acquisition device is arranged in the road section range determined by the road section position information.

It should be noted that, the cloud server may also be used as an execution body of the method provided in embodiment 1, where the feature extraction process of the road network image and the road network matching process are described in embodiment 1, and are not described herein again.

Example 3

According to an embodiment of the present application, there is further provided a method for processing traffic information, as shown in fig. 8, where the method includes:

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

Step S804, road condition information of the road network where the target object is located is obtained from the server, and the road condition information is displayed, 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 road network images 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.

Based on the scheme defined in step S802 to step S804, it can be known that in the embodiment of the present application, the road network image acquired by the image acquisition device in real time is used to determine the road condition information, after the road network topology data of the 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 the local road condition information of the 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 should be noted that, the server in this embodiment may execute the processing method of the road condition information provided in embodiment 1, and the related content is described in embodiment 1, which is not described herein again.

Example 4

According to an embodiment of the present application, there is also provided an apparatus for implementing the above-mentioned road condition information processing method, as shown in fig. 9, the apparatus 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 acquiring device is located, and a road network image acquired by the at least one image acquiring 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 the road network.

Here, it should be noted that the above-mentioned obtaining 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 examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above-mentioned embodiment one.

Example 5

According to an embodiment of the present application, there is also provided a system for processing traffic 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 instructions to implement the method for processing traffic information according to embodiment 1.

Example 6

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

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

In this embodiment, the electronic device includes one or more processors; and 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 running the programs, wherein the programs are configured to execute the road condition information processing method in the above embodiments when run.

Alternatively, 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 (only one shown) processors 1002, memory 1004, and a peripheral interface 1006.

The storage can be used for storing software programs and modules, such as program instructions/modules corresponding to the road condition information processing method and device in the embodiment of the application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the storage, so that the road condition information processing method is realized. 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 remotely located with respect to 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 may call the information and the application program stored in the memory through the transmission device to perform the following steps: 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; extracting features of the road network images to obtain local road condition information of the 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.

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

Optionally, the above processor may further execute program code for: extracting features of the road network image to obtain road feature information and vehicle feature information, wherein the road feature information at least comprises: the lane line information of the road section where the current image acquisition equipment is located, and the vehicle characteristic information at least comprises: vehicle position information, vehicle number information, and vehicle pitch information; and determining the local road condition information of the road section where the current image acquisition equipment is positioned according to the road characteristic information and the vehicle characteristic information.

Optionally, the above processor may further execute program code for: after extracting characteristics of road network images to obtain local road condition information of a road section where each image acquisition device is located, acquiring local road condition information corresponding to at least one image acquisition device; and removing abnormal data of the local road condition information to obtain the processed local road condition information.

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

Optionally, the above processor may further execute program code for: after road network matching is carried out 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, determining a missing road section according to the road network topology 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 topology 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 above processor may further execute program code for: 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, and then obtaining the 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 segments, wherein the road condition scene represents the congestion degree corresponding to each group of road segments; 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.

Optionally, the above processor may further execute program code for: the cloud server acquires the road section position information of the target object in the road network; the cloud server acquires road network images acquired by at least one image acquisition device based on 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 performs feature extraction on the road network images to obtain local road condition information of the 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 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; and the cloud server pushes the road condition information to the terminal equipment.

Optionally, the above processor may further execute program code for: responding to the road condition inquiry command, and displaying the road section position information of the target object in the road network; road condition information of a road network where the target object is located is obtained from a server, and the road condition information is displayed, wherein the road condition information is obtained by carrying out 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 carrying out feature extraction on road network images 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 will be appreciated by those skilled in the art that the structure shown in fig. 10 is only illustrative, and the computer terminal may be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palm-phone computer, a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 10 is not limited to the 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 of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.

Example 7

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

Alternatively, in this embodiment, the above-mentioned computer-readable storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

Alternatively, in the present embodiment, the computer readable storage medium stores a computer program, where the computer program is configured to execute the road condition information processing method in the embodiment at the time of execution.

Optionally, in the present embodiment, the 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 road network images acquired by the at least one image acquisition device; extracting features of the road network images to obtain local road condition information of the 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.

Optionally, in the present embodiment, the 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 trusted area corresponding to each image acquisition device, wherein the trusted area represents an image range, in which the accuracy of data in images acquired by each image acquisition device is higher than a preset accuracy; and performing screenshot processing on the corresponding initial road network image based on the trusted region corresponding to each image acquisition device to obtain the road network image corresponding to each image acquisition device.

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

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

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

Optionally, in the present embodiment, the computer readable storage medium is configured to store program code for performing the steps of: after road network matching is carried out 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, determining a missing road section according to the road network topology 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 topology 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 the present embodiment, the computer readable storage medium is configured to store program code for performing the steps of: 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, and then obtaining the 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 segments, wherein the road condition scene represents the congestion degree corresponding to each group of road segments; 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.

Optionally, in the present embodiment, the computer readable storage medium is configured to store program code for performing the steps of: the cloud server acquires the road section position information of the target object in the road network; the cloud server acquires road network images acquired by at least one image acquisition device based on 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 performs feature extraction on the road network images to obtain local road condition information of the 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 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; and the cloud server pushes the road condition information to the terminal equipment.

Optionally, in the present embodiment, the computer readable storage medium is configured to store program code for performing the steps of: responding to the road condition inquiry command, and displaying the road section position information of the target object in the road network; road condition information of a road network where the target object is located is obtained from a server, and the road condition information is displayed, wherein the road condition information is obtained by carrying out 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 carrying out feature extraction on road network images 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 foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in 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, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.

Claims

1. The processing method of the road condition information is characterized by comprising the following steps:

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;

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

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;

after 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, the method further comprises the steps of: 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, and determining a plurality of groups of road segments; identifying a road condition scene corresponding to each group of road segments, wherein the road condition scene represents the congestion degree corresponding to each group of road segments; 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.

2. The method of claim 1, wherein acquiring the road network image acquired by the at least one image acquisition device comprises:

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

determining a trusted area corresponding to each image acquisition device, wherein the trusted area represents an image range in which the accuracy of data in images acquired by each image acquisition device is higher than a preset accuracy;

and performing screenshot processing on the corresponding initial road network image based on the trusted region corresponding to each image acquisition device to obtain the road network image corresponding to each image acquisition device.

3. The method of claim 1, wherein the feature extraction of the road network image to obtain the local road condition information of the road section where each image acquisition device is located comprises:

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

And determining the 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 extracting features from the road network image to obtain local road condition information of a road section 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 the 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 carrying out 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 topology data and 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 topology 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 processing method of the road condition information is characterized by comprising the following steps:

the cloud server acquires the road section position information of the target object in the 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 performs feature extraction on the road network images 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 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;

the cloud server pushes the road condition information to a terminal device;

After 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, and obtains the road condition information of the road network where the target object is located, the method further comprises: the cloud server acquires 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, and determining a plurality of groups of road segments; identifying a road condition scene corresponding to each group of road segments, wherein the road condition scene represents the congestion degree corresponding to each group of road segments; 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. The processing method of the road condition information is characterized by comprising the following steps:

responding to the road condition inquiry command, and displaying the road section position information of the target object in the road network;

obtaining 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 carrying out 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 carrying out feature extraction on road network images 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;

The method further comprises the steps of: displaying the adjusted road condition information, wherein the adjusted road condition information is obtained by the following steps: 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, and determining a plurality of groups of road segments; identifying a road condition scene corresponding to each group of road segments, wherein the road condition scene represents the congestion degree corresponding to each group of road segments; 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.

9. A traffic information processing apparatus, comprising:

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 feature extraction module is used for extracting features of the road network images to obtain local road condition information of the road section where each image acquisition device is located;

the matching module is used for 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;

The adjusting module is used for 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, and determining a plurality of groups of road segments; identifying a road condition scene corresponding to each group of road segments, wherein the road condition scene represents the congestion degree corresponding to each group of road segments; 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.

10. A computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, wherein the computer program is configured to execute the road condition information processing method according to any one of claims 1 to 8 at a time of execution.

11. An electronic device, comprising one or more processors; a memory for storing one or more programs that when executed by the one or more processors cause the one or more processors to implement a method for running a program, wherein the program is configured to perform the method for processing road condition information of any one of claims 1 to 8 when run.

12. A system for processing traffic information, comprising:

a processor; and

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 a method of processing traffic information according to any one of claims 1 to 8.