Road traffic control method, device and equipment and storage medium

Abstract

The invention discloses a road traffic control method, device and equipment and a storage medium, and relates to the technical field of traffic control, and the method comprises the steps: obtaining road traffic information in a road traffic environment of a to-be-controlled road, the road traffic information comprising road information, vehicle driving information and road condition index information; constructing a road traffic digital twinborn model based on the actual physical environment and the road information of the road to be managed and controlled; inputting the vehicle driving information and the road condition index information into a road traffic digital twin model for analog simulation so as to determine traffic flow information of the to-be-controlled road; and generating a road traffic control strategy of the to-be-controlled road based on the traffic flow information, and performing road traffic control according to the road traffic control strategy. According to the technical scheme, the technical problems that in the prior art, after road traffic congestion occurs, on-site traffic dispersion is difficult to conduct through workers, and efficiency is not high are solved.

Description

Technical Field

[0001] The present application relates to the field of traffic control technology, and in particular to road traffic control methods, devices, equipment and storage media. Background Art

[0002] With the rapid development of the national economy and the improvement of people's income level, the number of private cars has increased, which has greatly facilitated people's lives. However, with the rapid development of transportation and the increase in the number of vehicles, the traffic flow has also increased rapidly. Traffic congestion during peak travel periods such as commuting and holidays has become a common phenomenon.

[0003] At present, when traffic congestion occurs on the road, staff are usually arranged to conduct traffic control on site to ease the traffic. Although this method has alleviated the traffic pressure to a certain extent, conducting traffic diversion after traffic congestion occurs will greatly increase the difficulty of traffic diversion and the efficiency is also low.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a road traffic control method, device, equipment and storage medium, aiming to solve the technical problems in the prior art that it is difficult and inefficient to conduct on-site traffic diversion by staff after road traffic congestion occurs.

[0006] To achieve the above objectives, the present application proposes a road traffic control method, wherein the method comprises:

[0007] Acquire road traffic information in a road traffic environment of a road to be controlled, the road traffic information including: road information, vehicle driving information and road condition index information;

[0008] Building a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information;

[0009] Inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled;

[0010] A road traffic control strategy for the road to be controlled is generated based on the traffic flow information, and road traffic control is performed according to the road traffic control strategy.

[0011] In one embodiment, the step of obtaining road traffic information in the road traffic environment of the road to be controlled includes:

[0012] Collecting an image corresponding to the road traffic environment of the road to be controlled, and determining the image as an image to be identified;

[0013] Extracting three-dimensional geometric dimensions of a road physical entity based on a digital model of the road physical entity, wherein the digital model of the road physical entity is constructed based on point cloud data of the road object entity, wherein the point cloud data is acquired by a three-dimensional laser scanner;

[0014] By using virtual reality technology, the road physical entity and the image feature information in the image to be identified are fused based on the three-dimensional geometric dimensions to obtain road information in the image to be identified, wherein the road information includes lane information and road sign information;

[0015] Collecting vehicle driving information on the road to be controlled by millimeter wave radar, wherein the vehicle driving information includes: vehicle driving position, vehicle driving speed and vehicle driving direction;

[0016] Acquire road condition index information corresponding to the road to be controlled based on a multi-scale road surface damage recognition model, wherein the road condition index information includes: route information and damage degree information;

[0017] The road traffic information in the road traffic environment is acquired based on the road information, the vehicle driving information and the road condition index information.

[0018] In one embodiment, before the step of acquiring the road condition index information corresponding to the road to be controlled based on the multi-scale road surface damage recognition model, the step further includes:

[0019] The coded structured light technology is used to obtain the road surface phase data of the sample road surface;

[0020] Using laser radar to obtain road surface geometry data of the sample road surface;

[0021] Acquiring road surface image data of the sample road surface by using binocular vision technology;

[0022] The road surface phase data, the road surface geometry data and the road surface image data are fused by an image fusion algorithm to obtain an image pair of fused three-source heterogeneous data of the sample road surface;

[0023] Performing binocular reconstruction on the image pair of the fused three-source heterogeneous data to obtain three-dimensional texture morphology data of the sample road surface;

[0024] Based on the three-dimensional texture morphology data, road surface crack identification, flatness analysis and road surface anti-skid performance evaluation are performed on the sample road surface to construct a multi-scale road surface disease identification model.

[0025] In one embodiment, the step of constructing a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information includes:

[0026] Determining the terrain characteristics and building distribution characteristics of the road to be controlled according to the actual physical environment of the road to be controlled;

[0027] Constructing a virtual environment model of the road to be controlled based on the terrain features and the building distribution features;

[0028] A road traffic digital twin model is constructed based on the actual physical environment, the virtual environment model and the road information.

[0029] In one embodiment, the step of constructing a road traffic digital twin model based on the actual physical environment, the virtual environment model and the road information includes:

[0030] Mapping the roads in the actual physical environment to the virtual environment model;

[0031] Determining target lane information and target road sign information of the mapped road in the virtual environment model according to the road information;

[0032] updating the mapped road based on the target lane information and the target road sign information;

[0033] Build a road traffic digital twin model based on the updated mapped roads.

[0034] In one embodiment, the step of inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled includes:

[0035] Inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to generate a road simulation result;

[0036] Determine the road driving image corresponding to the road to be controlled at the target time based on the road simulation result by using a target detection algorithm;

[0037] Determine a first number of pixels and a second number of pixels in the road driving image, wherein the first number of pixels is the number of pixels of the moving object in the road driving image, and the second number of pixels is the number of pixels of the lane in the road driving image;

[0038] Determine the current spatial ratio of the road to be controlled based on the first number of pixels and the second number of pixels;

[0039] The traffic flow information of the road to be controlled is determined according to the current road space occupation ratio.

[0040] In one embodiment, the step of generating a road traffic control strategy for the road to be controlled based on the traffic flow information includes:

[0041] Determining the vehicle moving speed of the vehicle on the road to be controlled according to the traffic flow information;

[0042] comparing the vehicle moving speed with the road congestion moving speed;

[0043] If the vehicle moving speed is lower than the road congestion moving speed, it is determined that the road to be controlled is in a congested state;

[0044] A road traffic control strategy for the road to be controlled is generated based on the congestion status.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a road traffic control device, the device comprising:

[0046] A road information acquisition module, used to acquire road traffic information in the road traffic environment of the road to be controlled, wherein the road traffic information includes: road information, vehicle driving information and road condition index information;

[0047] A model building module, used to build a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information;

[0048] A simulation module, used for inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation, so as to determine the traffic flow information of the road to be controlled;

[0049] A road traffic control module is used to generate a road traffic control strategy for the road to be controlled based on the traffic flow information, and perform road traffic control according to the road traffic control strategy.

[0050] In addition, to achieve the above-mentioned purpose, the present application also proposes a road traffic control device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the road traffic control method as described above.

[0051] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the road traffic control method described above are implemented.

[0052] The present application provides a road traffic control method, which discloses obtaining road traffic information in a road traffic environment of a road to be controlled, wherein the road traffic information includes: road information, vehicle driving information and road condition index information; constructing a road traffic digital twin model based on the actual physical environment and road information of the road to be controlled; inputting the vehicle driving information and road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled; generating a road traffic control strategy for the road to be controlled based on the traffic flow information, and performing road traffic control according to the road traffic control strategy; since the present invention constructs a road traffic digital twin model based on the actual physical environment and road information of the road to be controlled, and performs simulation through the road traffic digital twin model to determine the traffic flow information, and then generates a road traffic control strategy based on the traffic process information to perform traffic control, the technical problem in the prior art that it is difficult and inefficient to conduct on-site traffic diversion by staff after road traffic congestion occurs is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0055] Figure 1 A flowchart of the first embodiment of the road traffic control method of the present application is provided;

[0056] Figure 2 A flowchart of the second embodiment of the road traffic control method of the present application is provided;

[0057] Figure 3 A flowchart of the third embodiment of the road traffic control method of the present application is provided;

[0058] Figure 4 This is a schematic diagram of the module structure of the road traffic control device according to an embodiment of the present application;

[0059] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the road traffic control method in the embodiment of the present application.

[0060] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0062] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0063] The main solution of the embodiment of the present application is: obtaining road traffic information in the road traffic environment of the road to be controlled, the road traffic information including: road information, vehicle driving information and road condition index information; constructing a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information; inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled; generating a road traffic control strategy for the road to be controlled based on the traffic flow information, and performing road traffic control according to the road traffic control strategy.

[0064] The existing technology usually arranges staff to conduct traffic control on site to relieve traffic after traffic congestion occurs on the road. Although this method relieves traffic pressure to a certain extent, it will greatly increase the difficulty of traffic diversion after traffic congestion occurs, and the efficiency is also low.

[0065] The present application provides a solution, which can build a road traffic digital twin model based on the actual physical environment and road information of the road to be controlled, and simulate the road traffic digital twin model to determine the traffic flow information, and then generate a road traffic control strategy based on the traffic process information to perform traffic control, thereby solving the technical problem in the prior art that it is difficult and inefficient to conduct on-site traffic diversion by staff after road traffic congestion occurs.

[0066] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a road traffic control device, etc. The following takes the road traffic control device as an example (hereinafter referred to as the device) to illustrate this embodiment and the following embodiments.

[0067] Based on this, the present application embodiment provides a road traffic control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the road traffic control method of the present application.

[0068] In this embodiment, the road traffic control method includes steps S10 to S40:

[0069] Step S10: Acquire road traffic information in the road traffic environment of the road to be controlled, wherein the road traffic information includes: road information, vehicle driving information and road condition index information.

[0070] It should be understood that the above-mentioned road to be controlled can be any road that is prone to traffic congestion incidents. This embodiment does not limit the location of the road to be controlled and the surrounding environment and other features. Correspondingly, the above-mentioned road traffic information can be information that affects the traffic operation of the road to be controlled. In this embodiment, the road traffic information can include: road information, vehicle driving information and road condition index information. Among them, the road information can include lane information in the road (such as the number of lanes, lane width, lane line information, etc.) and road sign information (such as speed limit signs, turn signs, etc.); vehicle driving information is related information about vehicles traveling on the road, such as: vehicle driving position, vehicle driving speed and vehicle driving direction; road condition index information can be index information used to measure the quality of the road, such as: line information and disease degree information, wherein the line information can be road surface width, minimum curve radius, maximum slope and annual average day and night traffic volume of the road; disease degree information can be information used to measure the degree of damage to the road surface, such as: cracks, potholes, ruts, etc. in the road surface.

[0071] Furthermore, the step S10 includes:

[0072] Step S101: collecting an image corresponding to the road traffic environment of the road to be controlled, and determining the image as an image to be identified.

[0073] It should be noted that the above-mentioned image to be identified is an image of the road to be controlled captured by a camera, and the image shows the road traffic environment of the road to be controlled. In this embodiment, images related to the road to be controlled can be captured by a vehicle-mounted image acquisition device loaded on the vehicle, such as images related to the road taken by a vehicle-mounted camera.

[0074] Step S102: extracting the three-dimensional geometric dimensions of the road physical entity based on the digital model of the road physical entity, wherein the digital model of the road physical entity is constructed based on the point cloud data of the road object entity, and the point cloud data is acquired by a three-dimensional laser scanner.

[0075] It should be understood that the above-mentioned digital model of the physical entity of the road may be a model for digitally simulating the physical objects on the road, wherein the physical objects on the road may include but are not limited to lanes, road signs, and the like.

[0076] In practical applications, drones can be equipped with 3D laser scanners to conduct aerial photography and 3D modeling of road physical entities. Specifically, the 3D laser scanner can be used to obtain point cloud data of road object entities; then, the point cloud data can be subjected to abnormal point elimination (such as fitting surface elimination based on the least squares method, by classifying the point cloud data and fitting the surface using the least squares method, points far from the entity can be eliminated), interpolation (such as cubic spline interpolation) to supplement missing data, point cloud data denoising (such as Gaussian filtering), and point cloud fitting 3D reconstruction (such as quadratic surface fitting method), thereby completing the 3D digital modeling of road physical entities and controlling its accuracy within 5mm.

[0077] It can be understood that the above three-dimensional geometric dimensions can be specific dimensions of the road physical entity in three dimensions (length, width and height), wherein these dimensions are key parameters for describing the size and shape of the object in three-dimensional space.

[0078] Step S103: The road physical entity and the image feature information in the image to be identified are fused based on the three-dimensional geometric dimensions through virtual reality technology to obtain road information in the image to be identified, where the road information includes lane information and road sign information.

[0079] It should be noted that the above-mentioned virtual reality technology can be a three-dimensional virtual environment generated by computer simulation, which enables users to immerse themselves in a completely virtual world and interact with it. In this embodiment, the use of virtual reality technology can achieve the restoration of real road landform data through accurate modeling recognition and texture rendering technology, so that users can obtain a cinema-like immersive and realistic visual effect. In practical applications, the device can use virtual reality technology and fuse the image feature information of the road physical entity and the image to be identified (such as texture features, shape features, spatial features, etc. in the image to be identified) based on three-dimensional geometric dimensions, and then identify the road boundary, height limit and marking position through feature extraction and height difference changes, and finally complete the perception and recognition of the road driving space (i.e., marking and height limit) and the boundary (i.e., the curb line or physical boundary of the road), so as to obtain the above-mentioned road information.

[0080] Step S104: Collecting vehicle driving information on the road to be controlled by millimeter wave radar, wherein the vehicle driving information includes: vehicle driving position, vehicle driving speed and vehicle driving direction.

[0081] It should be understood that the millimeter wave radar is a radar that works in the millimeter wave band. In this embodiment, the device can collect information such as the current position, driving speed, and driving direction of all vehicles traveling on the road to be controlled through the millimeter wave radar to obtain the above vehicle driving information.

[0082] Step S105: obtaining road condition index information corresponding to the road to be controlled based on the multi-scale road surface damage recognition model, wherein the road condition index information includes: route information and damage degree information.

[0083] It should be noted that the above-mentioned multi-scale road surface damage identification model can be a model for identifying the degree of damage of a road surface from multiple scales (such as road surface cracks, road surface flatness, road surface skid resistance, etc.).

[0084] Furthermore, before step S105, the method also includes: using coded structured light technology to obtain pavement phase data of the sample pavement; using laser radar to obtain pavement geometry data of the sample pavement; using binocular vision technology to obtain pavement image data of the sample pavement; fusing the pavement phase data, the pavement geometry data and the pavement image data through an image fusion algorithm to obtain an image pair of fused three-source heterogeneous data of the sample pavement; performing binocular reconstruction on the image pair of fused three-source heterogeneous data to obtain three-dimensional texture morphology data of the sample pavement; and performing pavement crack recognition, flatness analysis and pavement anti-skid performance evaluation on the sample pavement based on the three-dimensional texture morphology data to construct a multi-scale road surface disease recognition model.

[0085] It should be noted that coded structured light technology can be used to simplify the matching pixel search problem by projecting specific textures into the measured space. This technology can be applied to the fields of object information segmentation and recognition, somatosensory gesture recognition, and three-dimensional scene reconstruction. In practical applications, in road detection, structured light technology can be used to automatically detect vehicles. These vehicles are usually equipped with high-resolution linear array road image acquisition systems, laser structured light three-dimensional measurement systems, on-board computers, embedded integrated multi-sensor synchronous control units and other equipment, so that they can automatically and quickly collect route information, disease degree and other major road condition indicators at normal traffic speeds, and then obtain the above-mentioned road condition indicator information.

[0086] It can be understood that the above-mentioned sample road surface can be any road surface used as a sample to train the multi-scale road surface disease recognition model. This embodiment does not limit the geographical location of the sample road surface and its morphology, road conditions and other information.

[0087] It should be noted that the above-mentioned road surface phase data can be data used to control traffic in different directions in the sample road surface; the above-mentioned road surface geometry data can be data used to describe the physical characteristics and spatial position relationship of the road surface, which may include the length, width, curvature, slope, superelevation on both sides, etc. of the road surface, and this embodiment does not limit this; the above-mentioned road surface image data can be image information of the sample road surface obtained by an image acquisition device such as a camera. In this embodiment, the road surface image data can be obtained through binocular vision technology, wherein the binocular vision technology can be a method based on the parallax principle and obtaining three-dimensional geometric information of objects through multiple images.

[0088] It should be understood that the above-mentioned image pair fused with three-source heterogeneous data can be a new image pair formed by fusing data from different sources and with different structures. In practical applications, the device can fuse the three types of data, namely, road surface phase data, road surface geometry data, and road surface image data, to obtain an image pair fused with three-source heterogeneous data. This embodiment can improve the comprehensiveness and accuracy of the image by fusing the data, so as to facilitate subsequent analysis of the image.

[0089] It can be understood that binocular reconstruction of an image pair can be a process of processing two images using binocular vision technology to reconstruct a scene. Specifically, the 3D depth information in the scene can be obtained by calculating the parallax of the same-name points in two images at different perspectives, thereby reconstructing the three-dimensional data of the scene.

[0090] It can be understood that the above-mentioned three-dimensional texture and morphology data can be three-dimensional data used to describe the surface texture and morphology characteristics of the sample road surface, wherein these data can include not only the geometric shape information of the sample road surface, but also the texture details of the sample road surface, such as color, glossiness, roughness, etc., which is not limited in this embodiment.

[0091] In the specific implementation, the device can use three methods, namely coded structured light, laser radar and binocular vision, to obtain the phase data, geometric data and image data of the sample road surface respectively. After obtaining these data, the phase data of the coded structured light can be phase unwrapped by using a convolutional neural network (such as U-net) to achieve real-time acquisition of the continuous phase of the coded structured light; then, the image fusion algorithm (such as NSCT image fusion algorithm) can be used to fuse the phase data, geometric data and image data in sequence to obtain an image pair of fused three-source heterogeneous data; finally, the fused image pair can be binocularly reconstructed to obtain the digital three-dimensional texture morphology data of the road surface, thereby achieving high-speed (i.e. real-time) and high-precision acquisition of three-source heterogeneous data with an accuracy of 0.2mm. After that, the equipment can carry out road crack recognition (the crack size is about 1mm, and the YOLOv8 deep learning network can be used to identify and classify cracks), flatness analysis (roughness refers to extra-large structures with a wavelength of 0.5m to 50m and a vertical dimension greater than 50mm. The profile method can be used to directly obtain roughness data) and road skid resistance evaluation based on high-precision road surface three-dimensional texture morphology data [The skid resistance mainly depends on the macro and micro textures. Among them, the macro morphology refers to the coarse structure with a wavelength of 0.5mm to 50mm and a vertical dimension of 0.5mm to 20mm; the micro texture is a fine structure with a wavelength of less than 0.5mm and a vertical dimension between 1μm and 0.5mm. Based on the three-dimensional texture morphology of the road surface, the macro texture and micro texture are separated by bandpass filtering method, and the separated texture data are used to calculate the macro texture indicators (such as: average structural depth; profile root mean square wavelength; profile skewness, profile root mean square slope, etc.) and micro texture indicators (such as: average structural depth, profile single peak average spacing, hump degree, apparent anisotropy, etc.), and then complete the evaluation of anti-skid performance. Finally, the initial road surface disease recognition model can be continuously trained and iteratively optimized based on the data generated by these analysis results, and finally the above-mentioned multi-scale road surface disease recognition model can be obtained.

[0092] Step S106: Acquire road traffic information in the road traffic environment based on the road information, the vehicle driving information and the road condition index information.

[0093] In practical applications, after obtaining the road information, vehicle driving information and road condition index information of the road to be controlled, this information can be combined to obtain the road traffic information in the road traffic environment.

[0094] Step S20: Construct a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information.

[0095] It should be understood that the road traffic digital twin model can be a model that digitizes physical objects such as road traffic facilities, vehicles and pedestrians. Digital twin technology uses technical means of real-time interaction with actual physical objects to achieve real-time synchronization and interaction between digital and real information, thereby predicting, simulating and optimizing real-life scenarios on digital models. In practical applications, the road traffic digital twin model can not only be used for the detection and analysis of traffic congestion, but also includes the optimization and adjustment of traffic flow, providing more optimized means and decision support for traffic planning, design and operation.

[0096] Step S30: Input the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled.

[0097] It should be noted that the above-mentioned traffic flow information may be information used to reflect the usage frequency and busyness of the road to be controlled, such as traffic volume, vehicle occupancy rate, traffic flow density, etc., which is not limited in this embodiment.

[0098] In this embodiment, the device can input vehicle driving information and road condition index information into the road traffic digital twin model, so that the road traffic digital twin model can determine the position, speed, and driving direction of vehicles traveling on the road to be controlled based on the vehicle driving information, and determine the road surface width, road surface slope and other information of the road to be controlled based on the road condition index information, and then simulate the current vehicle driving conditions on the road to be controlled based on this information. Finally, the road traffic digital twin model can predict the traffic flow information at a certain moment or period in the future based on the historical traffic data pre-stored in the device.

[0099] Step S40: generating a road traffic control strategy for the road to be controlled based on the traffic flow information, and performing road traffic control according to the road traffic control strategy.

[0100] It can be understood that the above-mentioned road traffic control strategy can be a strategy adopted for traffic diversion on the controlled roads, for example, diverting vehicles according to vehicle types, extending the duration of traffic lights, setting traffic guidance signs, establishing traffic signals, etc. This embodiment does not impose any restrictions on this.

[0101] In this embodiment, it can be determined whether traffic diversion is needed on the road to be controlled based on the traffic flow information of the road to be controlled. If necessary, the road traffic control strategy used for traffic diversion can be determined based on the size of the traffic flow, and the corresponding road traffic control strategy can be used to perform road traffic control on the road to be controlled.

[0102] It should be noted that in order to provide a basic guarantee for subsequent digital twin modeling and traffic control, this solution can also use deep neural networks (such as YOLOv5) to classify and refine traffic signals, traffic signs and traffic markings, and digitally encode the refined traffic signals for efficient subsequent application of these traffic signals. On the other hand, it is also possible to use integrated laser and vision technology (i.e., integrating laser radar and visual perception) to achieve accurate perception of signal states such as traffic flow, pedestrian conditions and signal control under complex climatic conditions (due to the strong anti-interference ability of laser radar), and quickly classify the perceived traffic signals in combination with digital coding.

[0103] The present embodiment provides a road traffic control method, which discloses obtaining road traffic information in a road traffic environment of a road to be controlled, the road traffic information including: road information, vehicle driving information and road condition index information; constructing a road traffic digital twin model based on the actual physical environment and road information of the road to be controlled; inputting the vehicle driving information and road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled; generating a road traffic control strategy for the road to be controlled based on the traffic flow information, and performing road traffic control according to the road traffic control strategy; since the present embodiment constructs a road traffic digital twin model based on the actual physical environment and road information of the road to be controlled, and performs simulation through the road traffic digital twin model to determine the traffic flow information, and then generates a road traffic control strategy based on the traffic process information to perform traffic control, thereby solving the technical problem in the prior art that it is difficult and inefficient to conduct on-site traffic diversion by staff after road traffic congestion occurs.

[0104] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , Figure 2 A flow chart of the second embodiment of the road traffic control method of the present application is provided.

[0105] In this embodiment, step S20 includes steps S201 to S203:

[0106] Step S201: determining the terrain characteristics and building distribution characteristics of the road to be controlled according to the actual physical environment of the road to be controlled.

[0107] It is understandable that the above-mentioned actual physical environment is the actual environment around the road to be controlled. In this embodiment, the actual physical environment of the road to be controlled may include the topography, buildings, etc. around the road to be controlled, and this embodiment does not limit this.

[0108] It should be understood that the above-mentioned terrain features may be features used to characterize the terrain around the road to be controlled; correspondingly, the above-mentioned building distribution features are features used to characterize the distribution of buildings around the road to be controlled.

[0109] Step S202: constructing a virtual environment model of the road to be controlled based on the terrain features and the building distribution features.

[0110] It should be noted that the above-mentioned virtual environment model may be a model of the surrounding environment of the road to be controlled simulated in a virtual environment.

[0111] Step S203: Construct a road traffic digital twin model based on the actual physical environment, the virtual environment model and the road information.

[0112] Specifically, the step S203 includes: mapping the road in the actual physical environment to the virtual environment model; determining the target lane information and target road sign information of the mapped road in the virtual environment model according to the road information; updating the mapped road based on the target lane information and the target road sign information; and constructing a road traffic digital twin model based on the updated mapped road.

[0113] It is understandable that the above-mentioned mapped road can be a road mapped from the actual physical environment of the road to be controlled. Correspondingly, the above-mentioned target lane information and target road sign information are lane information and road sign information in the mapped road respectively. In this embodiment, since the road in the actual physical environment of the road to be controlled is directly mapped to the virtual environment model, the target lane information and target road sign information of the mapped road in the virtual environment model are the same as the lane information and road sign information of the road in the actual physical environment of the road to be controlled.

[0114] In actual applications, after mapping the roads in the actual physical environment to the virtual environment model, usually only the outline of the road is mapped to the virtual environment model. Therefore, it is also necessary to obtain the lane information and road sign information of the road to be controlled, and use the lane information and road sign information as the target lane information and target road sign information mapped to the mapped road in the virtual environment model, so as to obtain a mapped road with complete road information, and then a road traffic digital twin model can be constructed based on the mapped road with complete road information.

[0115] In this embodiment, it is disclosed that the actual physical environment of the road to be controlled is constructed to determine the terrain characteristics and building distribution characteristics of the road to be controlled; a virtual environment model of the road to be controlled is constructed based on the terrain characteristics and building distribution characteristics; a road traffic digital twin model is constructed based on the actual physical environment, the virtual environment model and the road information, so that the traffic flow information of the road to be controlled can be directly predicted based on the road traffic digital twin model, thereby improving the efficiency of road traffic control.

[0116] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 3 , Figure 3 A flowchart diagram is provided for Example 3 of the road traffic control method of this application.

[0117] In this embodiment, step S30 includes steps S301 to S304:

[0118] Step S301: input the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to generate a road simulation result.

[0119] Step S302: Determine the road driving image corresponding to the road to be controlled at the target time based on the road simulation result through a target detection algorithm.

[0120] It should be noted that the road simulation results generated after the road traffic digital twin model is simulated can be a frame-by-frame traffic video sequence. Correspondingly, the above-mentioned target detection algorithm can be an algorithm for detecting images corresponding to moving objects in the traffic video sequence (that is, the above-mentioned road driving images). In this embodiment, the road driving images can include: vehicle images and pedestrian images, which is not limited in this embodiment.

[0121] Step S303: determining a first number of pixels and a second number of pixels in the road driving image, wherein the first number of pixels is the number of pixels of the moving target in the road driving image, and the second number of pixels is the number of pixels of the lanes in the road driving image.

[0122] Step S304: Determine the current spatial occupancy ratio of the road to be controlled based on the first number of pixels and the second number of pixels.

[0123] It should be understood that the above-mentioned current space occupancy ratio can be the ratio between occupied roads and unoccupied roads in the road to be controlled. In this embodiment, the current space occupancy ratio can be the ratio of the width occupied by vehicles and pedestrians in the road to be controlled to the width of the road on which they are located.

[0124] It should be noted that the first number of pixels is the number of pixels of the cross section of the moving target (such as pedestrians and vehicles) in the road driving image; the second number of pixels is the number of pixels of the cross section of the lane in the road driving image. In this embodiment, the calculation formula of the spatial lane ratio can be: spatial lane ratio = first number of pixels / second number of pixels. In practical applications, the moving targets and lanes in the road driving image can be first identified and segmented to obtain the moving object image corresponding to the moving object and the lane image corresponding to the lane, and the first number of pixels in the moving object image and the second number of pixels corresponding to the lane image can be obtained respectively. Then, the first number of pixels and the second number of pixels in the road driving image corresponding to all frames in the prediction time period can be obtained in this way. At this time, several spatial lane ratios can be calculated by the calculation formula of the spatial lane ratio. Finally, the average of these spatial lane ratios can be taken to obtain the current spatial lane ratio of the road to be controlled.

[0125] Step S305: Determine the traffic flow information of the road to be controlled according to the current road space occupation ratio.

[0126] In this embodiment, the device can count the road vehicle flow and road pedestrian flow in the predicted time period according to the current space occupation ratio, so as to determine the number of vehicles and pedestrians passing through per unit time in the predicted time period, thereby obtaining the flow density of the road to be controlled in the predicted time period. After obtaining the flow density, the cross-sectional area width of the road to be controlled can be multiplied by the flow density to obtain the traffic flow information of the road to be controlled.

[0127] Furthermore, the step S40 includes: determining the vehicle moving speed of the vehicles in the road to be controlled based on the traffic flow information; comparing the vehicle moving speed with the road congestion moving speed; if the vehicle moving speed is lower than the road congestion moving speed, determining that the road to be controlled is in a congested state; and generating a road traffic control strategy for the road to be controlled based on the congested state.

[0128] It can be understood that the vehicle moving speed can be the average moving speed of vehicles on the road to be controlled in a certain period of time obtained by predicting the road traffic digital twin model.

[0129] It should be understood that the above-mentioned road congestion moving speed can be used to characterize the average moving speed of vehicles when there is congestion on the road to be controlled. In this embodiment, the road congestion moving speed can be set according to actual conditions, and this embodiment does not limit this.

[0130] In practical applications, a road congestion moving speed can be set in advance, and the predicted average moving speed of vehicles on the road to be controlled in a certain time period can be compared with the road congestion moving speed. If the average moving speed of vehicles is lower than the road congestion moving speed, it indicates that the road to be controlled is in a congested state in the corresponding time period, and road traffic control is required at this time. Therefore, the device can generate a road traffic control strategy corresponding to the road to be controlled to perform road traffic control.

[0131] In this embodiment, it is disclosed that vehicle driving information and road condition index information are input into a road traffic digital twin model for simulation to generate a road simulation result; a road driving image corresponding to the road to be controlled at a target time is determined based on the road simulation result through a target detection algorithm; the current space occupancy ratio of the road to be controlled is determined based on the road driving image; and the traffic flow information of the road to be controlled is determined according to the current space occupancy ratio, so that the traffic flow information of the road to be controlled can be accurately predicted through the road traffic digital twin model, thereby improving the accuracy of road traffic control.

[0132] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the road traffic control method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0133] This application also provides a road traffic control device, please refer to Figure 4 , the road traffic control device comprises:

[0134] A road information acquisition module 10 is used to acquire road traffic information in a road traffic environment of a road to be controlled, wherein the road traffic information includes: road information, vehicle driving information and road condition index information;

[0135] A model building module 20, used to build a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information;

[0136] A simulation module 30 is used to input the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled;

[0137] The road traffic control module 40 is used to generate a road traffic control strategy for the road to be controlled based on the traffic flow information, and perform road traffic control according to the road traffic control strategy.

[0138] The road traffic control device provided by the present application adopts the road traffic control method in the above embodiment, which can solve the technical problem that it is difficult and inefficient for staff to conduct on-site traffic diversion after road traffic congestion occurs in the prior art. Compared with the prior art, the beneficial effects of the road traffic control device provided by the present application are the same as the beneficial effects of the road traffic control method provided by the above embodiment, and other technical features in the road traffic control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0139] The present application provides a road traffic control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the road traffic control method in the above-mentioned embodiment one.

[0140] Reference below Figure 5 , which shows a schematic diagram of the structure of a road traffic control device suitable for implementing the embodiment of the present application. The road traffic control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The road traffic control equipment shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0141] like Figure 5As shown, the road traffic control device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the road traffic control device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the road traffic control device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a road traffic control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided alternatively.

[0142] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0143] The road traffic control device provided by the present application adopts the road traffic control method in the above embodiment to solve the technical problems of road traffic control. Compared with the prior art, the beneficial effects of the road traffic control device provided by the present application are the same as the beneficial effects of the road traffic control method provided by the above embodiment, and the other technical features in the road traffic control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0144] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0145] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0146] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the road traffic control method in the above-mentioned embodiment.

[0147] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0148] The above-mentioned computer-readable storage medium may be included in the road traffic control device; or it may exist independently without being installed in the road traffic control device.

[0149] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the road traffic control equipment, the road traffic control equipment: obtains road traffic information in the road traffic environment of the road to be controlled, and the road traffic information includes: road information, vehicle driving information and road condition index information; constructs a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information; inputs the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled; generates a road traffic control strategy for the road to be controlled based on the traffic flow information, and performs road traffic control according to the road traffic control strategy.

[0150] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0151] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0152] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0153] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned road traffic control method, and can solve the technical problem in the prior art that it is difficult and inefficient to conduct on-site traffic diversion by staff after road traffic congestion occurs. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the road traffic control method provided by the above-mentioned embodiment, and will not be repeated here.

Claims

1. A road traffic control method, characterized in that: The method includes: Acquire road traffic information in a road traffic environment of a road to be controlled, the road traffic information including: road information, vehicle driving information and road condition index information; Building a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information; Inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled; A road traffic control strategy for the road to be controlled is generated based on the traffic flow information, and road traffic control is performed according to the road traffic control strategy.

2. The method according to claim 1, characterized in that The step of obtaining road traffic information in the road traffic environment of the road to be controlled includes: Collecting an image corresponding to the road traffic environment of the road to be controlled, and determining the image as an image to be identified; Extracting three-dimensional geometric dimensions of a road physical entity based on a digital model of the road physical entity, wherein the digital model of the road physical entity is constructed based on point cloud data of the road object entity, wherein the point cloud data is acquired by a three-dimensional laser scanner; By using virtual reality technology, the road physical entity and the image feature information in the image to be identified are fused based on the three-dimensional geometric dimensions to obtain road information in the image to be identified, wherein the road information includes lane information and road sign information; Collecting vehicle driving information on the road to be controlled by millimeter wave radar, wherein the vehicle driving information includes: vehicle driving position, vehicle driving speed and vehicle driving direction; Acquire road condition index information corresponding to the road to be controlled based on a multi-scale road surface damage recognition model, wherein the road condition index information includes: route information and damage degree information; The road traffic information in the road traffic environment is acquired based on the road information, the vehicle driving information and the road condition index information.

3. The method according to claim 2, characterized in that Before the step of acquiring the road condition index information corresponding to the road to be controlled based on the multi-scale road surface disease recognition model, the method further includes: The coded structured light technology is used to obtain the road surface phase data of the sample road surface; Using laser radar to obtain road surface geometry data of the sample road surface; Acquiring road surface image data of the sample road surface by using binocular vision technology; The road surface phase data, the road surface geometry data and the road surface image data are fused by an image fusion algorithm to obtain an image pair of fused three-source heterogeneous data of the sample road surface; Performing binocular reconstruction on the image pair of the fused three-source heterogeneous data to obtain three-dimensional texture morphology data of the sample road surface; Based on the three-dimensional texture morphology data, road surface crack identification, flatness analysis and road surface anti-skid performance evaluation are performed on the sample road surface to construct a multi-scale road surface disease identification model.

4. The method according to claim 1, characterized in that The step of constructing a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information includes: Determining the terrain characteristics and building distribution characteristics of the road to be controlled according to the actual physical environment of the road to be controlled; Constructing a virtual environment model of the road to be controlled based on the terrain features and the building distribution features; A road traffic digital twin model is constructed based on the actual physical environment, the virtual environment model and the road information.

5. The method according to claim 4, characterized in that The step of constructing a road traffic digital twin model based on the actual physical environment, the virtual environment model and the road information comprises: Mapping the roads in the actual physical environment to the virtual environment model; Determining target lane information and target road sign information of the mapped road in the virtual environment model according to the road information; updating the mapped road based on the target lane information and the target road sign information; Build a road traffic digital twin model based on the updated mapped roads.

6. The method according to any one of claims 1 to 5, characterized in that The step of inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to determine the traffic flow information of the road to be controlled includes: Inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation to generate a road simulation result; Determine the road driving image corresponding to the road to be controlled at the target time based on the road simulation result by using a target detection algorithm; Determine a first number of pixels and a second number of pixels in the road driving image, wherein the first number of pixels is the number of pixels of the moving object in the road driving image, and the second number of pixels is the number of pixels of the lane in the road driving image; Determine the current spatial ratio of the road to be controlled based on the first number of pixels and the second number of pixels; The traffic flow information of the road to be controlled is determined according to the current road space occupation ratio.

7. The method according to any one of claims 1 to 5, characterized in that The step of generating the road traffic control strategy for the road to be controlled based on the traffic flow information includes: Determining the vehicle moving speed of the vehicle on the road to be controlled according to the traffic flow information; comparing the vehicle moving speed with the road congestion moving speed; If the vehicle moving speed is lower than the road congestion moving speed, it is determined that the road to be controlled is in a congested state; A road traffic control strategy for the road to be controlled is generated based on the congestion status.

8. A road traffic control device, characterized in that: The device comprises: A road information acquisition module, used to acquire road traffic information in the road traffic environment of the road to be controlled, wherein the road traffic information includes: road information, vehicle driving information and road condition index information; A model building module, used to build a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information; A simulation module, used for inputting the vehicle driving information and the road condition index information into the road traffic digital twin model for simulation, so as to determine the traffic flow information of the road to be controlled; A road traffic control module is used to generate a road traffic control strategy for the road to be controlled based on the traffic flow information, and perform road traffic control according to the road traffic control strategy.

9. A road traffic control device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the road traffic control method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the road traffic control method according to any one of claims 1 to 7 are implemented.

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