Road traffic control method, device, equipment and storage medium

Abstract

The present application discloses a road traffic control method, device, equipment and storage medium, which relates to the field of traffic control technology. The method includes: 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 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. The application of the above technical solution solves 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.

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 levels, the number of private cars has increased significantly, greatly facilitating people's lives. However, with the rapid development of transportation and the increase in the number of vehicles, traffic volume has also increased rapidly. Traffic congestion during peak travel times 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 can alleviate the traffic pressure to a certain extent, it will greatly increase the difficulty of traffic diversion after the traffic congestion occurs, and the efficiency is also low.

[0004] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is 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 existing technology 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, which includes:

[0007] Acquiring 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;

[0008] Constructing 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] fusing the road physical entity and the image feature information in the image to be identified based on the three-dimensional geometric dimensions using virtual reality technology to obtain road information in the image to be identified, the road information including 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] Acquiring 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] 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 obtaining the road condition index information corresponding to the road to be controlled based on the multi-scale road surface disease 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] Acquiring pavement geometry data of the sample pavement using a laser radar;

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

[0022] fusing the road surface phase data, the road surface geometry data, and the road surface image data through 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 fused with the three-source heterogeneous data to obtain three-dimensional texture topography 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 based on 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 characteristics and the building distribution characteristics;

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

[0037] Determining a first number of pixels and a second number of pixels in the road driving image, where 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 lanes in the road driving image;

[0038] Determining a current spatial occupation 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 based on 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 a vehicle moving speed of vehicles on the road to be controlled based on the traffic flow information;

[0042] comparing the vehicle's moving speed with a 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 objectives, the present application also proposes a road traffic control device, which includes:

[0046] A road information acquisition module, configured 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;

[0047] A model building module, configured 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, configured 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;

[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 runnable 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. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the road traffic control method as described above are implemented.

[0052] The present application provides a road traffic control method, which discloses obtaining road traffic information in the 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 invention constructs a road traffic digital twin model based on the actual physical environment and road information of the road to be controlled, and simulates and emulates 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, it solves 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. 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

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

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

[0057] Figure 3 A flowchart of the third embodiment of the road traffic control method of this 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 explained 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 merely used to explain the technical solutions of the present application and are not intended 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] Since 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 control and the efficiency is also low when conducting traffic control after traffic congestion occurs.

[0065] The present application provides a solution that can construct 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 existing technology 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 execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions, road traffic control equipment, etc. The following uses road traffic control equipment as an example (hereinafter referred to as equipment) to illustrate this embodiment and the following embodiments.

[0067] Based on this, the embodiment of the present application 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: Obtaining 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. Accordingly, the above-mentioned road traffic information can be information that affects the traffic operation conditions 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: route information and damage degree information, wherein the route information can be information such as road surface width, minimum curve radius, maximum slope and annual average daytime and nighttime traffic volume; the damage degree information can be information used to measure the damage degree of 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, which 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 captured 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 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 road physical entity may be a model for digitally simulating physical objects on the road, wherein the physical objects on the road may include but are not limited to lanes, road signs, etc.

[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 is subjected to abnormal point elimination (e.g., fitting surface elimination based on the least squares method, which eliminates points far from the entity by classifying the point cloud data and fitting the surface using the least squares method), interpolation (e.g., cubic spline interpolation) to supplement missing data, point cloud data denoising (e.g., Gaussian filtering), and point cloud fitting 3D reconstruction (e.g., quadratic surface fitting method), thereby completing the 3D digital modeling of the road physical entity and controlling its accuracy within 5mm.

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

[0078] Step S103: 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, 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 topography data through precise modeling recognition and texture rendering technology, so that users can obtain a cinema-like immersive and realistic visual effect. In actual 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 road curb or physical boundary), thereby obtaining the above-mentioned road information.

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

[0081] It should be understood that millimeter-wave radar is a radar that operates in the millimeter-wave band. In this embodiment, the device can use millimeter-wave radar to collect information such as the current location, speed, and direction of all vehicles traveling on the controlled road to obtain the above-mentioned 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 lidar 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 identification, 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 identification model.

[0085] It should be noted that coded structured light technology simplifies the matching pixel search problem by projecting a specific texture onto the measured space. This technology can be applied to fields such as object information segmentation and recognition, somatosensory gesture recognition, and 3D scene reconstruction. In practical applications, structured light technology can be used for automated inspection of vehicles during road inspections. These vehicles are typically equipped with high-resolution linear array road image acquisition systems, laser structured light 3D measurement systems, onboard computers, and embedded integrated multi-sensor synchronous control units. This allows them to automatically and quickly collect key road condition indicators such as route information and damage severity at normal traffic speeds, thereby obtaining the aforementioned road condition indicator information.

[0086] It is understandable 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, shape, road conditions and other information of the sample road surface.

[0087] It should be noted that the above-mentioned road surface phase data can be data used to control traffic flows in different directions in a sample road surface; the above-mentioned road surface geometric 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 impose any restrictions on this; the above-mentioned road surface image data can be image information of a sample road surface obtained by an image acquisition device such as a camera. In this embodiment, the road surface image data can be obtained by binocular vision technology, wherein the binocular vision technology can be a method based on the parallax principle and obtaining three-dimensional geometric information of an object through multiple images.

[0088] It should be understood that the aforementioned image pair fused from three sources of 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 road surface phase data, road surface geometry data, and road surface image data to obtain an image pair fused from three sources of heterogeneous data. This data fusion in this embodiment can improve the comprehensiveness and accuracy of the image, facilitating subsequent image analysis.

[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 of the scene can be obtained by calculating the parallax of the same-name points in two images at different perspectives, and then the three-dimensional data of the scene can be reconstructed.

[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 not only include the geometric shape information of the sample road surface, but also include the texture details of the sample road surface, such as color, glossiness, roughness, etc. This embodiment does not limit this.

[0091] In specific implementations, the device can use three methods: coded structured light, lidar, and binocular vision to respectively acquire phase data, geometric data, and image data of the sample road surface. After acquiring this data, a convolutional neural network (e.g., U-net) can be used to perform phase unwrapping on the coded structured light phase data to achieve real-time acquisition of the continuous phase of the coded structured light. Then, an image fusion algorithm (e.g., NSCT image fusion algorithm) can be used to sequentially fuse the phase data, geometric data, and image data to obtain an image pair that fuses the three-source heterogeneous data. Finally, binocular reconstruction can be performed on the fused image pair to obtain digital 3D texture and topography data of the road surface, thereby achieving high-speed (i.e., real-time) and high-precision acquisition of the three-source heterogeneous data, with an accuracy of 0.2mm. After that, the equipment can carry out road crack identification (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 anti-skid performance evaluation based on high-precision road surface three-dimensional texture data. [The anti-skid performance mainly depends on the macro and micro textures. Among them, the macro texture 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 the fine structure with a wavelength 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 band-pass filtering. The separated texture data are used to calculate 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, average spacing of profile single peaks, hump degree, apparent anisotropy, etc.), thereby completing 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 road information, vehicle driving information, and road condition index information of the road to be controlled, this information can be combined to obtain 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 a road traffic digital twin model can be a digitized representation of physical objects such as road traffic facilities, vehicles, and pedestrians. Digital twin technology achieves real-time synchronization and interaction between digital and real-world information through real-time interaction with physical objects, thereby predicting, simulating, and optimizing real-world scenarios within the digital model. In practical applications, road traffic digital twin models can be used not only to detect and analyze traffic congestion but also to optimize traffic flow, providing more optimized tools and decision support for transportation planning, design, and operations.

[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 frequency of use 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 when diverting traffic 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 limit this.

[0101] In this embodiment, it is possible to determine whether traffic diversion is required 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 control road traffic on the road to be controlled.

[0102] It should be noted that, in order to provide a foundation 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 subsequent efficient application. On the other hand, integrated radar and vision technology (i.e., the integration of lidar and visual perception) can also be used 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 lidar), and quickly classify the perceived traffic signals in combination with digital encoding.

[0103] This 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 this 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 embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , Figure 2 A flow chart illustrating the second embodiment of the road traffic control method of this application.

[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 based on 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 that is 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, 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 based on 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 understood that the mapped road can be a road mapped from the actual physical environment of the road to be controlled. Accordingly, the target lane information and target road sign information are the lane information and road sign information of the mapped road, respectively. In this embodiment, since the road in the actual physical environment of the road to be controlled is directly mapped into 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 of the mapped road mapped to the virtual environment model, so as to obtain a mapped road with complete road information. 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 to construct the actual physical environment of the road to be controlled to determine the terrain characteristics and building distribution characteristics of the road to be controlled; construct a virtual environment model of the road to be controlled based on the terrain characteristics and building distribution characteristics; and construct a road traffic digital twin model 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 flow chart illustrating the third embodiment 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 road simulation results.

[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 using 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, and this embodiment does not impose any restrictions on this.

[0121] Step S303: Determine a first number of pixels and a second number of pixels in the road driving image, where 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 above-mentioned first number of pixels is the number of pixels in the cross section of a moving target (such as a pedestrian or vehicle) in a road driving image; the above-mentioned second number of pixels is the number of pixels in the cross section of a lane in a road driving image. In this embodiment, the calculation formula for the spatial lane occupancy ratio can be: spatial lane occupancy ratio = first number of pixels / second number of pixels. In practical applications, the moving targets and lanes in the road driving image can first be identified and segmented to obtain a moving object image corresponding to the moving object and a lane image corresponding to the lane, and the first number of pixels in the moving object image and the second number of pixels in 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 occupancy ratios can be calculated using the calculation formula for the spatial lane occupancy ratio. Finally, the average of these spatial lane occupancy ratios can be taken to obtain the current spatial lane occupancy ratio of the road to be controlled.

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

[0126] In this embodiment, the device can count the vehicle and pedestrian traffic during the predicted time period based on the current road occupation ratio, thereby determining the number of vehicles and pedestrians passing through per unit time during the predicted time period, thereby obtaining the traffic density of the road to be controlled during the predicted time period. After obtaining the traffic density, the cross-sectional width of the road to be controlled can be multiplied by the traffic density to obtain traffic flow information for the road to be controlled.

[0127] Furthermore, the step S40 includes: determining the vehicle moving speed of the vehicle 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 congestion 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 prediction through 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 impose any restrictions on this.

[0130] In actual 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 road simulation results; a road driving image corresponding to the road to be controlled at a target time is determined based on the road simulation results through a target detection algorithm; the current spatial road 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 spatial road 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 this application and do not constitute a limitation on the road traffic control method of this application. More simple transformations based on this technical concept are all within the scope of protection of this application.

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

[0134] A road information acquisition module 10 is 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;

[0135] A model building module 20 is 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 configured 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 configured 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 this application utilizes the road traffic control method of the aforementioned embodiment, and can address the technical issues in the prior art where on-site traffic diversion by personnel after a road traffic jam is difficult and inefficient. Compared to the prior art, the beneficial effects of the road traffic control device provided by this application are the same as those of the road traffic control method provided by the aforementioned embodiment, and the other technical features of the road traffic control device are the same as those disclosed in the aforementioned embodiment method, and are not further described 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 that can be executed 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 structural diagram of a road traffic control device suitable for implementing an 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), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The road traffic control equipment shown is merely an example and should not limit 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 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the road traffic control device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. The communication device 1009 can allow the road traffic control device to communicate with other devices wirelessly or wired 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 of the systems shown. More or fewer systems can be implemented or provided instead.

[0142] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising 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 via 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 in this application utilizes the road traffic control method described in the above-mentioned embodiments to solve the technical problems of road traffic control. Compared with the prior art, the beneficial effects of the road traffic control device provided in this application are the same as those of the road traffic control method described in the above-mentioned embodiments. Other technical features of the road traffic control device are the same as those disclosed in the above-mentioned embodiments and are not further described here.

[0144] It should be understood that the various parts disclosed in this application can be implemented using 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 description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0146] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., 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 this 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 thereof. 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 thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction 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 thereof.

[0148] The above-mentioned computer-readable storage medium may be included in the road traffic control device; or it may exist independently without being assembled into 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 is enabled to: obtain 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; construct a road traffic digital twin model based on the actual physical environment of the road to be controlled and the road information; 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; 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.

[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 stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving 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., through the Internet using an Internet service provider).

[0151] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of 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 box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0152] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0153] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned road traffic control method. This computer-readable storage medium can address the technical issues in the prior art where on-site traffic diversion by personnel after a road traffic jam is difficult and inefficient. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the road traffic control method provided in the aforementioned embodiments, and are not further elaborated here.

Claims

1. A road traffic control method, characterized in that: The method includes: Acquiring 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 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; 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; fusing the road physical entity and image feature information in the image to be identified based on the three-dimensional geometric dimensions using virtual reality technology to obtain road information in the image to be identified, the road information including lane information and road sign information, wherein the virtual reality technology is used to restore real road topography data based on precise modeling recognition and texture rendering technology; 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; The coded structured light technology is used to obtain the road surface phase data of the sample road surface; Acquiring pavement geometry data of the sample pavement using a laser radar; Acquiring road surface image data of the sample road surface using binocular vision technology; fusing the road surface phase data, the road surface geometry data, and the road surface image data through an image fusion algorithm to obtain an image pair of fused three-source heterogeneous data of the sample road surface; Determining the disparity of the same-name points in two images of the image pair of the fused three-source heterogeneous data at different viewing angles by binocular vision technology to obtain three-dimensional texture topography data of the sample road surface; Based on the three-dimensional texture morphology data, pavement crack identification, flatness analysis and pavement anti-skid performance evaluation are performed on the sample pavement to construct a multi-scale road surface disease identification model; Acquiring road condition index information corresponding to the road to be controlled based on the multi-scale road surface damage identification model, wherein the road condition index information includes: route information and damage degree information; Acquiring road traffic information in the road traffic environment based on the road information, the vehicle driving information, and the road condition index information; 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 based on 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 characteristics and the building distribution characteristics; 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.

2. The method according to claim 1, wherein 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 a road driving image corresponding to the road to be controlled at a target time based on the road simulation result by using a target detection algorithm; Determining a first number of pixels and a second number of pixels in the road driving image, where 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 lanes in the road driving image; Determining a current spatial occupation 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 based on the current road space occupation ratio.

3. The method according to claim 1, wherein The step of generating a road traffic control strategy for the road to be controlled based on the traffic flow information includes: determining a vehicle moving speed of vehicles on the road to be controlled based on the traffic flow information; comparing the vehicle's moving speed with a 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.

4. A road traffic control device, characterized in that: The device comprises: A road information acquisition module, configured 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; A model building module, configured 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, configured 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; A road traffic control module, configured 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; The road information acquisition module is also used to collect images corresponding to the road traffic environment of the road to be controlled, and determine the image as the image to be identified; extract the three-dimensional geometric dimensions of the road physical entity based on the digital model of the road physical entity, 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 obtained by a three-dimensional laser scanner; the image feature information of the road physical entity and the image to be identified is fused based on the three-dimensional geometric dimensions by virtual reality technology to obtain the road information in the image to be identified, the road information includes: lane information and road sign information, wherein the virtual reality technology is used to restore the real road landform data based on accurate modeling recognition and texture rendering technology; collect vehicle driving information on the road to be controlled by millimeter wave radar, the vehicle driving information includes: vehicle driving position, vehicle driving speed and vehicle driving direction; use coded structured light technology to obtain road surface phase data of the sample road surface; use laser Radar acquires pavement geometry data of the sample pavement; binocular vision technology is used to acquire pavement image data of the sample pavement; the pavement phase data, the pavement geometry data and the pavement image data are fused by an image fusion algorithm to obtain an image pair of fused three-source heterogeneous data of the sample pavement; binocular vision technology is used to determine the parallax of the same-name points in two images of the image pair of fused three-source heterogeneous data at different perspectives to obtain three-dimensional texture morphology data of the sample pavement; based on the three-dimensional texture morphology data, pavement crack identification, flatness analysis and pavement anti-skid performance evaluation are performed on the sample pavement to construct a multi-scale road surface disease recognition model; based on the multi-scale road surface disease recognition model, road condition index information corresponding to the road to be controlled is acquired, the road condition index information including: line information and disease degree information; 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; The model construction module is used to determine the terrain characteristics and building distribution characteristics of the road to be controlled based on the actual physical environment of the road to be controlled; construct a virtual environment model of the road to be controlled based on the terrain characteristics and the building distribution characteristics; map the road in the actual physical environment to the virtual environment model; determine the target lane information and target road sign information of the mapped road in the virtual environment model based on the road information; update the mapped road based on the target lane information and the target road sign information; and construct a road traffic digital twin model based on the updated mapped road.

5. A road traffic control device, characterized in that: The device includes: 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 3.

6. 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 3 are implemented.

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