Road pavement simulation method, device, equipment, medium and program product

Abstract

The application provides a road pavement simulation method, device, equipment, medium and program product. In the method, the initial grid pavement model of the pavement grid is generated through the road description data and the pavement material of each pavement grid in the pavement simulation data, and then the initial grid pavement model is optimized according to the wear degree and the pavement material of the pavement grid, so as to obtain the target grid pavement model of the pavement grid. After the target roughness, the target metallic degree and the target albedo of each pavement grid are determined according to the environmental parameters, the pavement material and the friction coefficient of each pavement grid, the target grid pavement model of each pavement grid is combined for rendering to obtain the simulation pavement. According to the present scheme, the pavement model is updated through the wear degree and the pavement material, and the parameters of the simulation are determined through the environmental parameters, the pavement material and the friction coefficient, so that the similarity between the simulation pavement and the real pavement is improved.

Description

Technical Field

[0001] This application relates to the field of simulation technology, and in particular to a method, apparatus, equipment, medium, and program product for simulating road surfaces. Background Technology

[0002] In the development and testing of autonomous driving algorithms, high-fidelity road simulation is crucial for simulating real-world driving environments. Obtaining a high-fidelity road surface through simulation is a critical step in creating a simulated road.

[0003] In existing technologies, road surface simulation usually involves first generating a road model, then having staff create road surface textures. Each road surface material corresponds to a different texture. The road surface textures and the road model are then imported into a rendering engine for rendering to generate a simulated road surface.

[0004] In summary, the existing technology uses texture mapping for road surface simulation, resulting in a low similarity between the simulated road surface and the real road surface. Summary of the Invention

[0005] The road surface simulation method, apparatus, equipment, medium, and program products provided in this application are intended to solve the problem that the simulation road surface has low similarity to the real road surface due to the use of texture mapping in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for simulating road surface, comprising:

[0007] Obtain road surface simulation data, which includes environmental parameters, as well as road description data of multiple road surface grids, road surface material, wear degree and friction coefficient;

[0008] For each road surface grid, an initial grid road surface model is generated based on the road description data and road surface material of the road surface grid.

[0009] For each road surface grid, the initial grid road surface model of the road surface grid is optimized based on the wear degree and road surface material of the road surface grid to obtain the target grid road surface model of the road surface grid.

[0010] For each road surface grid, the target roughness, target metallicity, and target albedo of the road surface grid are determined based on the environmental parameters, the road surface material, and the friction coefficient of the road surface grid.

[0011] The simulated road surface is generated by rendering based on the initial grid road surface model, target roughness, target metallicity, and target albedo of each road surface grid.

[0012] In one possible implementation, optimizing the initial grid pavement model of the pavement grid based on its wear degree and pavement material to obtain the target grid pavement model includes:

[0013] Based on the preset correspondence between materials and noise types, the target noise type corresponding to the road surface material of the road surface mesh is determined;

[0014] Based on the wear degree of the road surface grid, noise data for each target noise type is determined;

[0015] Based on the noise data for each target noise type, the initial grid road surface model of the road surface grid is updated to obtain the target grid road surface model of the road surface grid.

[0016] In one possible implementation, the noise data for each target noise type includes the coordinate increment of each coordinate point in the initial grid road surface model;

[0017] The step of updating the initial grid road surface model of the road surface grid based on noise data for each target noise type to obtain the target grid road surface model of the road surface grid includes:

[0018] For each coordinate point in the initial grid road surface model, the coordinate point is added to the coordinate increment of the coordinate point in the noise data of each type of target noise to obtain the updated coordinate point;

[0019] Based on each updated coordinate point, a target grid road surface model is generated to obtain the road surface grid.

[0020] In one possible implementation, the environmental parameter is rainfall intensity, and determining the target roughness, target metallicity, and target albedo of the road surface mesh based on the environmental parameter, the road surface material, and the friction coefficient of the road surface mesh includes:

[0021] Based on the preset correspondence between friction coefficient and roughness, the reference roughness corresponding to the friction coefficient of the road surface mesh is determined;

[0022] The target roughness of the road surface mesh is calculated based on the reference roughness and the rainfall intensity.

[0023] The target metallicity of the road surface mesh is calculated based on the rainfall intensity and the first preset ratio;

[0024] Based on the preset correspondence between material and albedo, the target albedo corresponding to the road surface material of the road surface grid is determined.

[0025] In one possible implementation, before updating the initial grid pavement model of the pavement grid based on noise data for each target noise type to obtain the target grid pavement model of the pavement grid, the method further includes:

[0026] Based on the road description data of the road surface grid, determine whether the road surface grid is within the road edge range;

[0027] The step of updating the initial grid road surface model of the road surface grid based on noise data for each target noise type to obtain the target grid road surface model of the road surface grid includes:

[0028] If the road surface grid is not located within the road edge range, the initial grid road surface model of the road surface grid is updated based on the noise data of each target noise type to obtain the target grid road surface model of the road surface grid.

[0029] In one possible implementation, the method further includes:

[0030] If the road surface grid is located within the road edge range, the initial grid road surface model of the road surface grid is updated based on the preset edge noise data and the noise data of each target noise type to obtain the target grid road surface model of the road surface grid.

[0031] In one possible implementation, the method further includes:

[0032] Calculate the saturation increment based on the environmental parameters and the second preset ratio;

[0033] The simulated road surface is updated based on the saturation increment to obtain the updated simulated road surface.

[0034] Secondly, embodiments of this application provide a road surface simulation device, comprising:

[0035] The acquisition module is used to acquire road surface simulation data, which includes environmental parameters, road description data of multiple road surface grids, road surface material, wear degree and friction coefficient;

[0036] Processing module, used for:

[0037] For each road surface grid, an initial grid road surface model is generated based on the road description data and road surface material of the road surface grid.

[0038] For each road surface grid, the initial grid road surface model of the road surface grid is optimized based on the wear degree and road surface material of the road surface grid to obtain the target grid road surface model of the road surface grid.

[0039] For each road surface grid, the target roughness, target metallicity, and target albedo of the road surface grid are determined based on the environmental parameters, the road surface material, and the friction coefficient of the road surface grid.

[0040] The simulation module is used to render and generate simulated road surfaces based on the initial grid road surface model, target roughness, target metallicity, and target albedo for each road surface grid.

[0041] Thirdly, embodiments of this application provide an electronic device, including:

[0042] Processor, memory, communication interface;

[0043] The memory is used to store the executable instructions of the processor;

[0044] The processor is configured to execute the road surface simulation method described in any of the first aspects by executing the executable instructions.

[0045] Fourthly, embodiments of this application provide a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the road surface simulation method described in any of the first aspects.

[0046] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the road surface simulation method described in any of the first aspects.

[0047] The road surface simulation method, apparatus, equipment, medium, and program products provided in this application generate an initial grid road surface model for each road surface grid using road description data and road surface material from the road surface simulation data. Then, based on the wear degree and road surface material of each grid, the initial grid road surface model is optimized to obtain a target grid road surface model. After determining the target roughness, target metallicity, and target albedo for each grid based on environmental parameters, road surface material, and friction coefficient, the simulated road surface is rendered using the target grid road surface model for each grid. This solution improves the similarity between the simulated road surface and the real road surface by updating the road surface model through wear degree and road surface material, and by determining the simulation parameters through environmental parameters, road surface material, and friction coefficient. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] Figure 1aA flowchart illustrating an embodiment of the road surface simulation method provided in this application;

[0050] Figure 1b A schematic diagram illustrating the process of generating the target mesh road surface model provided in this application;

[0051] Figure 2 A flowchart illustrating Embodiment 2 of the road surface simulation method provided in this application;

[0052] Figure 3 A flowchart illustrating Embodiment 3 of the road surface simulation method provided in this application;

[0053] Figure 4 A schematic diagram of the structure of an embodiment of the road surface simulation device provided in this application;

[0054] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application.

[0055] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0056] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0057] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0058] With the continuous development of technology, autonomous driving technology is becoming increasingly mature. In the development and testing of autonomous driving algorithms, high-fidelity road simulation is key to simulating real driving environments, and obtaining high-fidelity road surface simulation is an important step in obtaining simulated roads.

[0059] In existing technologies, road surface simulation typically involves first creating a road model, then having staff create road surface textures, with each road surface material corresponding to a specific texture. These textures and the road model are then imported into a rendering engine for rendering to generate a simulated road surface. Because this texture-based approach to road surface simulation results in a low degree of similarity between the simulated and real road surfaces.

[0060] To address the problems existing in the prior art, the inventors, during their research on road surface simulation methods, discovered that to improve the similarity between simulated roads and real roads, an initial grid road surface model can be generated based on the road description data and road surface material of each road surface grid in the road surface simulation data. This initial model is then optimized based on the wear degree and road surface material of the grid to obtain a target grid road surface model. Furthermore, based on environmental parameters, the road surface material of each grid, and the friction coefficient, the target roughness, target metallicity, and target albedo of each grid are determined. Finally, the simulated road surface is rendered using the target grid road surface model for each grid. Based on the above inventive concept, the road surface simulation scheme of this application was designed.

[0061] The simulation method for road surface in this application can be executed by a computer, or by a server, terminal device, etc. This application does not limit it. The following explanation uses a computer as an example.

[0062] The following examples illustrate the application scenarios of the road surface simulation method provided in this application.

[0063] For example, in this application scenario, after developers develop an autonomous driving algorithm, they need to generate a simulated road surface in order to determine the effectiveness and stability of the algorithm.

[0064] Testers use terminal devices to send road surface simulation data to a computer, which then receives the road surface simulation data.

[0065] Then, for each road surface grid, the computer generates an initial grid road surface model based on the road description data and road surface material of that grid; then, based on the wear degree and road surface material of that grid, the computer optimizes the initial grid road surface model to obtain the target grid road surface model.

[0066] The computer then determines the target roughness, target metallicity, and target albedo for each road surface grid based on environmental parameters, the road surface material, and the friction coefficient of that grid.

[0067] Finally, the simulated road surface is generated by rendering based on the initial grid road surface model, target roughness, target metallicity, and target albedo of each road surface grid.

[0068] Testers can then use simulated road surfaces to conduct tests to determine the effectiveness and stability of the autonomous driving algorithm.

[0069] It should be noted that the above scenario is only an example of an application scenario provided by the embodiments of this application. The embodiments of this application do not limit the actual form of the various devices included in the scenario, nor do they limit the interaction method between devices. In the specific application of the solution, it can be set according to actual needs.

[0070] The technical solution of this application will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0071] Figure 1a This is a flowchart illustrating an embodiment of the road surface simulation method provided in this application. This embodiment optimizes the generated road surface model using computer-generated data on wear and road surface material, and determines simulation parameters based on environmental parameters, road surface material, and friction coefficient. Then, it renders the simulated road surface based on the optimized model and simulation parameters. The method in this embodiment can be implemented using software, hardware, or a combination of both. Figure 1a As shown, the simulation method for this road surface specifically includes the following steps:

[0072] S101: Obtain road surface simulation data.

[0073] In this step, the computer first acquires road surface simulation data in order to generate a simulated road surface. The road surface simulation data includes environmental parameters, as well as road description data of multiple road surface grids, road surface material, wear degree and friction coefficient.

[0074] It should be noted that road grid refers to dividing the road into grids, with each grid being a road grid.

[0075] It should be noted that the road description data, road material, and friction coefficient for each road surface grid can be obtained by parsing at least one road data file, or by identifying road surface point cloud data or road surface images.

[0076] Road data files can be Curved Regular Grid (CRG) files, Road Data Files (RDF), OpenDRIVE files, etc. This application does not limit the type of road data file; it can be determined based on actual circumstances.

[0077] It should be noted that when parsing multiple road data files to obtain the road description data, road surface material, and friction coefficient for each road surface grid, the units can be standardized for subsequent processing.

[0078] It should be noted that the acquisition of environmental parameters and the wear degree of each pavement grid can be achieved in several ways: First, workers input the environmental parameters and the wear degree of each pavement grid into the computer, which then retrieves these parameters. Second, workers can use an interrupt device to send the environmental parameters and the wear degree of each pavement grid to the computer, which then retrieves these parameters. Third, the data management platform stores the environmental parameters and the wear degree of each pavement grid, and the computer retrieves these parameters from the data management platform.

[0079] This application does not limit the method of obtaining road surface simulation data, and can be determined according to the actual situation.

[0080] It should be noted that road description data describes the shape and structure of a road, including width, height, and lateral accuracy. This application does not limit the road description data, which can be determined according to the actual situation.

[0081] It should be noted that the road surface material can be asphalt, gravel, concrete, bricks, soil, etc. This application does not limit the road surface material and can be determined according to the actual situation.

[0082] S102: For each road surface grid, generate an initial grid road surface model for that grid based on the road description data and road surface material of that grid.

[0083] In this step, after the computer obtains the road surface simulation data, since the road description data describes the shape and structure of the road, it can render the initial grid road surface model of each road surface grid based on the road description data and road surface material of that grid.

[0084] S103: For each road surface grid, the initial grid road surface model of the road surface grid is optimized according to the wear degree and road surface material of the road surface grid to obtain the target grid road surface model of the road surface grid.

[0085] In this step, after the computer obtains the initial grid road surface model, in order to improve realism, for each road surface grid, the initial grid road surface model of the road surface grid is optimized according to the wear degree and road surface material of the road surface grid to obtain the target grid road surface model of the road surface grid.

[0086] Abrasion degree is used to characterize the degree of wear on the road surface. The higher the abrasion degree, the greater the degree of wear. The abrasion degree value is between 0 and 1.

[0087] Specifically, based on the preset correspondence between materials and noise types, the target noise type corresponding to the road surface material of the road surface grid is determined.

[0088] For example, the noise types corresponding to asphalt are high-frequency noise, mid-frequency noise, and crack noise. The noise types corresponding to gravel are high-frequency noise and particle noise. The noise types corresponding to dirt pavement are low-frequency noise and agglomeration noise.

[0089] Based on the wear degree of the road surface grid, noise data for each target noise type is determined.

[0090] Each noise type has corresponding baseline noise data, which consists of multiple baseline coordinate increments. The difference between 1 and the wear degree is used as the retention rate. Then, for each target noise type, each baseline coordinate increment in the baseline noise data for that target noise type is multiplied by the retention rate to obtain the wear coordinate increment. Next, for each coordinate point in the initial grid pavement model of that pavement, one of all wear coordinate increments is selected as the coordinate increment for that point, thus obtaining the noise data for that target noise type. The greater the wear degree, the greater the change in coordinate increment, consistent with the laws of the real physical world.

[0091] After obtaining the noise data, the initial grid pavement model of the pavement grid is updated according to the noise data of each target noise type to obtain the target grid pavement model of the pavement grid.

[0092] Since the noise data for each type of target noise includes the coordinate increment of each coordinate point in the initial grid road surface model, the updated coordinate point can be obtained by adding the coordinate increment of that coordinate point in the noise data for each type of target noise to the original coordinate data for each coordinate point in the initial grid road surface model.

[0093] Based on each updated coordinate point, a target grid road surface model is generated to obtain the road surface grid.

[0094] The initial grid road surface model is composed of three-dimensional coordinate points, so after the coordinate points are updated, the target grid road surface model can be obtained.

[0095] For example, Figure 1bThis application provides a flowchart illustrating the process of generating the target mesh road surface model. Figure 1b As shown, based on the noise data of each target noise type, the initial grid pavement model of the pavement grid is updated to obtain the target grid pavement model of the pavement grid.

[0096] S104: For each road surface grid, determine the target roughness, target metallicity, and target albedo of the road surface grid based on environmental parameters, the road surface material, and the friction coefficient of the road surface grid.

[0097] In this step, after the computer acquires the road surface simulation data, in order to make the simulated road surface rendered later more realistic, for each road surface mesh, the target roughness, target metallicity, and target albedo are determined based on environmental parameters, the road surface material, and the friction coefficient of the road surface mesh.

[0098] Specifically, the environmental parameter is rainfall intensity. Rainfall intensity is used to characterize the amount of rainfall; the greater the rainfall intensity, the greater the amount of rainfall. Rainfall intensity is greater than or equal to 0 and less than 1.

[0099] Based on the preset correspondence between friction coefficient and roughness, the reference roughness corresponding to the friction coefficient of the road surface mesh is determined.

[0100] It should be noted that the coefficient of friction is directly proportional to the surface roughness; the higher the coefficient of friction, the higher the surface roughness. The surface roughness value ranges from 0 to 1.

[0101] Then, based on the baseline roughness and rainfall intensity, the target roughness of the road surface grid is calculated.

[0102] In other words, the difference between 1 and the rainfall intensity is used as the rainfall correction factor; then, the product of the baseline roughness and the baseline rainfall correction factor is used as the target roughness of the road surface grid. The greater the rainfall intensity, the smaller the roughness, which conforms to the real physical world.

[0103] The target metallicity of the road surface mesh is calculated based on the rainfall intensity and a first preset ratio. Since rainfall intensity also affects metallicity—the greater the rainfall intensity, the greater the metallicity—the product of the rainfall intensity and the first preset ratio can be used as the target metallicity of the road surface mesh.

[0104] It should be noted that the first preset ratio is used to characterize the corresponding proportional relationship between rainfall intensity and metallicity. The first preset ratio can be 0.5, 1.2, 2, etc. The embodiments of this application do not limit the first preset ratio, and it can be determined according to the actual situation.

[0105] Based on the preset correspondence between material and albedo, the target albedo corresponding to the road surface material of the road surface grid is determined.

[0106] Albedo is a vector composed of the sub-albedo of the three channels: R (red), G (green), and B (blue). For example, the albedo for asphalt is (0.18, 0.17, 0.16), and the albedo for gravel is (0.75, 0.75, 0.75). This application does not limit the preset correspondence between materials and albedo; it can be determined according to actual conditions.

[0107] It should be noted that the target albedo can also be updated based on the friction coefficient of the road surface mesh. The difference between the reference friction coefficient and the friction coefficient of the road surface mesh is taken as the friction coefficient difference; the product of the difference in the number of friction cycles and a preset increment ratio is taken as the albedo increment; the albedo increment is added to the target albedo to complete the update. The smaller the friction coefficient, the larger the albedo, which conforms to the real physical world. The preset increment ratio can be 0.2, 0.5, 0.7, etc., and this application embodiment does not limit the preset increment ratio, which can be determined according to the actual situation.

[0108] In one implementation, the computer can determine the target roughness, target metallicity, and target albedo corresponding to the rainfall intensity, road surface material, friction coefficient, roughness, metallicity, and albedo based on the correspondence between rainfall intensity, road surface material, friction coefficient, and roughness. This can improve simulation efficiency.

[0109] It should be noted that the execution order of steps S102-S103 and S104 can be: first execute steps S102-S103, then execute step S104; or first execute step S104, then execute steps S102-S103; or steps S102-S103 and S104 can be executed simultaneously. This embodiment does not limit the execution order of steps S102-S103 and S104, and it can be determined according to the actual situation.

[0110] S105: Render the simulated road surface based on the initial grid road surface model, target roughness, target metallicity, and target albedo for each road surface grid.

[0111] In this step, after the computer obtains the initial grid pavement model, target roughness, target metallicity, and target albedo for each pavement grid, it can use the physically based rendering (PBR) algorithm to render and generate a simulated pavement.

[0112] It should be noted that steps S101-S105 can be executed by the central processing unit (CPU) of the computer, or by the graphics processing unit (GPU), or by both the CPU and the GPU.

[0113] The road surface simulation method provided in this embodiment generates an initial grid road surface model from the road description data and road surface material of each road surface grid in the road surface simulation data. Then, based on the wear degree and road surface material of each grid, the initial grid road surface model is optimized to obtain a target grid road surface model. After determining the target roughness, target metallicity, and target albedo of each grid based on environmental parameters, road surface material, and friction coefficient, the simulated road surface is rendered using the target grid road surface model for each grid. This scheme improves the similarity between the simulated road surface and the real road surface by updating the road surface model through wear degree and road surface material, and by determining the simulation parameters through environmental parameters, road surface material, and friction coefficient. Furthermore, this scheme eliminates the need for manual creation of road surface textures, improving simulation efficiency and reducing costs.

[0114] Figure 2 This is a flowchart illustrating a second embodiment of the road surface simulation method provided in this application. Based on the above embodiments, this embodiment describes how, after the computer obtains an initial road surface mesh model, the road surface mesh is updated according to whether it is within the road edge range. For example... Figure 2 As shown, the simulation method for this road surface specifically includes the following steps:

[0115] S201: Based on the road description data of the road surface grid, determine whether the road surface grid is within the road edge range; if the road surface grid is not within the road edge range, proceed to step S202; if the road surface grid is within the road edge range, proceed to step S203.

[0116] In this step, after the computer obtains the initial grid road surface model, to further improve the realism of the simulated road surface, it can determine whether the road surface grid is located within the road edge range based on the road description data of the grid. The road surface at the road edge is rougher to facilitate drainage and increase anti-skid performance.

[0117] The road description data also includes coordinate data representing the location of the road surface grid. If the coordinate data is within the preset edge coordinate range, it is determined that the road surface grid is within the road edge range; if the coordinate data is not within the preset edge coordinate range, it is determined that the road surface grid is not within the road edge range.

[0118] S202: Based on the noise data for each type of target noise, update the initial grid pavement model of the pavement grid to obtain the target grid pavement model of the pavement grid.

[0119] In this step, if the computer determines that the road surface grid is not within the road edge range, no additional processing is required. Based on the noise data of each target noise type, the initial grid road surface model of the road surface grid is updated to obtain the target grid road surface model of the road surface grid.

[0120] The noise data for each type of target noise includes the coordinate increment of each coordinate point in the initial grid road surface model. For each coordinate point in the initial grid road surface model, the coordinate point is added to the coordinate increment of the same coordinate point in the noise data for each type of target noise to obtain the updated coordinate point. The updated coordinate points constitute the target grid road surface model.

[0121] S203: Based on the preset edge noise data and the noise data of each target noise type, update the initial grid road surface model of the road surface grid to obtain the target grid road surface model of the road surface grid.

[0122] In this step, if the computer determines that the road surface grid is within the road edge range, in order to improve realism, the initial grid road surface model of the road surface grid is updated according to the preset edge noise data and the noise data of each target noise type, so as to obtain the target grid road surface model of the road surface grid.

[0123] The noise data for each target noise type includes the coordinate increment of each coordinate point in the initial grid road surface model, and the preset edge noise data includes the edge coordinate increment of each coordinate point in the initial grid road surface model.

[0124] For each coordinate point in the initial grid road surface model, the coordinate point is added to the coordinate increment of that coordinate point in the noise data of each type of target noise, and to the edge coordinate increment in the preset edge noise data to obtain the updated coordinate point. The updated coordinate point constitutes the target grid road surface model.

[0125] The road surface simulation method provided in this embodiment updates the initial grid road surface model by checking whether the road surface grid is within the road edge range, thereby obtaining the target grid road surface model, which can improve the realism of the target grid road surface model.

[0126] Figure 3 This is a flowchart illustrating a third embodiment of the road surface simulation method provided in this application. Based on the above embodiments, this application describes the situation where the computer updates the simulated road surface again according to environmental parameters. For example... Figure 3 As shown, the simulation method for this road surface specifically includes the following steps:

[0127] S301: Calculate the saturation increment based on environmental parameters and the second preset ratio.

[0128] In this step, after the computer generates the simulated road surface, in order to further improve the similarity with the real road surface, the saturation increment is calculated based on environmental parameters and a second preset ratio.

[0129] The environmental parameter is rainfall intensity; the greater the rainfall intensity, the greater the saturation.

[0130] The product of rainfall intensity and the second preset ratio is used as the saturation increment coefficient; the saturation increment coefficient is multiplied by the saturation of the simulated road surface to obtain the saturation increment.

[0131] S302: Update the simulated road surface based on the saturation increment to obtain the updated simulated road surface.

[0132] In this step, after obtaining the saturation increment, the computer adds the saturation increment to the saturation of the simulated road surface to obtain the updated saturation. Then, based on the updated saturation, the new saturation of the simulated road surface is obtained, resulting in the updated simulated road surface.

[0133] The road surface simulation method provided in this embodiment updates the simulated road surface by adjusting the rainfall intensity, thereby further improving the similarity between the simulated road surface and the real road surface.

[0134] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0135] Figure 4 This is a schematic diagram of the structure of an embodiment of the road surface simulation device provided in this application; as shown below. Figure 4 As shown, the road surface simulation device 40 includes:

[0136] The acquisition module 41 is used to acquire road surface simulation data, which includes environmental parameters, as well as road description data of multiple road surface grids, road surface material, wear degree and friction coefficient;

[0137] Processing module 42 is used for:

[0138] For each road surface grid, an initial grid road surface model is generated based on the road description data and road surface material of the road surface grid.

[0139] For each road surface grid, the initial grid road surface model is optimized based on the wear degree and road surface material to obtain the target grid road surface model.

[0140] For each pavement grid, the target roughness, target metallicity, and target albedo of the pavement grid are determined based on environmental parameters, pavement material, and friction coefficient.

[0141] Simulation module 43 is used to render and generate simulated road surfaces based on the initial grid road surface model, target roughness, target metallicity, and target albedo of each road surface grid.

[0142] Furthermore, processing module 42 is specifically used for:

[0143] Based on the preset correspondence between materials and noise types, determine the target noise type corresponding to the road surface material of the road surface grid;

[0144] Based on the wear degree of the road surface mesh, determine the noise data for each target noise type;

[0145] Based on the noise data for each type of target noise, the initial grid road model of the road surface is updated to obtain the target grid road surface model.

[0146] Furthermore, the noise data for each target noise type includes the coordinate increment of each coordinate point in the initial grid road surface model; processing module 42 is specifically used for:

[0147] For each coordinate point in the initial grid road surface model, the coordinate point is added to the coordinate increment of the coordinate point in the noise data for each type of target noise to obtain the updated coordinate point;

[0148] Based on each updated coordinate point, a target grid road surface model is generated, resulting in a road surface grid.

[0149] Furthermore, the environmental parameter is rainfall intensity, and processing module 42 is specifically used for:

[0150] Based on the preset correspondence between friction coefficient and roughness, the reference roughness corresponding to the friction coefficient of the road surface mesh is determined;

[0151] The target roughness of the road surface mesh is calculated based on the baseline roughness and rainfall intensity.

[0152] Calculate the target metallicity of the road surface mesh based on the rainfall intensity and the first preset ratio;

[0153] Based on the preset correspondence between material and albedo, the target albedo corresponding to the road surface material of the road surface grid is determined.

[0154] Furthermore, before updating the initial grid pavement model of the pavement grid based on the noise data for each target noise type to obtain the target grid pavement model, the processing module 42 is also used for:

[0155] Based on the road description data of the road surface grid, determine whether the road surface grid is within the road edge range;

[0156] The processing module 42 is further used to update the initial grid road model of the road surface grid based on the noise data of each target noise type if the road surface grid is not within the road edge range, so as to obtain the target grid road model of the road surface grid.

[0157] Furthermore, the processing module 42 is also used to update the initial grid road model of the road grid according to the preset edge noise data and the noise data of each target noise type if the road grid is within the road edge range, so as to obtain the target grid road model of the road grid.

[0158] Furthermore, the processing module 42 is also used for:

[0159] Calculate the saturation increment based on environmental parameters and the second preset ratio;

[0160] The simulated road surface is updated based on the saturation increment to obtain the updated simulated road surface.

[0161] The road surface simulation device provided in this embodiment is used to execute the technical solution in any of the aforementioned method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0162] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 5 As shown, the electronic device 50 includes:

[0163] Processor 51, memory 52, and communication interface 53;

[0164] Memory 52 is used to store executable instructions of processor 51;

[0165] The processor 51 is configured to execute the technical solutions in any of the foregoing method embodiments by executing executable instructions.

[0166] Optionally, the memory 52 can be either standalone or integrated with the processor 51.

[0167] Optionally, when the memory 52 is a device independent of the processor 51, the electronic device 50 may further include:

[0168] Bus 54, memory 52 and communication interface 53 are connected to processor 51 through bus 54 and complete communication with each other. Communication interface 53 is used to communicate with other devices.

[0169] Optionally, the communication interface 53 can be implemented using a transceiver. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write databases, and read-only databases). The memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0170] Bus 54 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0171] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0172] The electronic device is used to execute the technical solutions in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0173] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solutions provided in any of the foregoing method embodiments.

[0174] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the technical solutions provided in any of the foregoing method embodiments.

[0175] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for simulating road surface, characterized in that, include: Obtain road surface simulation data, which includes environmental parameters, as well as road description data of multiple road surface grids, road surface material, wear degree and friction coefficient; For each road surface grid, an initial grid road surface model is generated based on the road description data and road surface material of the road surface grid. For each road surface grid, the initial grid road surface model of the road surface grid is optimized based on the wear degree and road surface material of the road surface grid to obtain the target grid road surface model of the road surface grid. For each road surface grid, the target roughness, target metallicity, and target albedo of the road surface grid are determined based on the environmental parameters, the road surface material, and the friction coefficient of the road surface grid. The simulated road surface is generated by rendering based on the target grid road surface model, target roughness, target metallicity, and target albedo for each road surface grid. The optimization process, based on the wear degree and road material of the road surface mesh, optimizes the initial mesh road surface model to obtain the target mesh road surface model, including: Based on the preset correspondence between materials and noise types, the target noise type corresponding to the road surface material of the road surface mesh is determined; Based on the wear degree of the road surface grid, noise data for each target noise type is determined; Based on the noise data for each target noise type, the initial grid road surface model of the road surface grid is updated to obtain the target grid road surface model of the road surface grid.

2. The method according to claim 1, characterized in that, Noise data for each target noise type includes the coordinate increment of each coordinate point in the initial grid road surface model; The step of updating the initial grid road surface model of the road surface grid based on noise data for each target noise type to obtain the target grid road surface model of the road surface grid includes: For each coordinate point in the initial grid road surface model, the coordinate point is added to the coordinate increment of the coordinate point in the noise data of each type of target noise to obtain the updated coordinate point; Based on each updated coordinate point, a target grid road surface model is generated to obtain the road surface grid.

3. The method according to claim 1, characterized in that, The environmental parameter is rainfall intensity. The determination of the target roughness, target metallicity, and target albedo of the road surface mesh based on the environmental parameter, the road surface material, and the friction coefficient includes: Based on the preset correspondence between friction coefficient and roughness, the reference roughness corresponding to the friction coefficient of the road surface mesh is determined; The target roughness of the road surface mesh is calculated based on the reference roughness and the rainfall intensity. The target metallicity of the road surface mesh is calculated based on the rainfall intensity and the first preset ratio; Based on the preset correspondence between material and albedo, the target albedo corresponding to the road surface material of the road surface grid is determined.

4. The method according to claim 1, characterized in that, Before updating the initial grid road surface model of the road surface grid based on noise data for each target noise type to obtain the target grid road surface model of the road surface grid, the method further includes: Based on the road description data of the road surface grid, determine whether the road surface grid is within the road edge range; The step of updating the initial grid road surface model of the road surface grid based on noise data for each target noise type to obtain the target grid road surface model of the road surface grid includes: If the road surface grid is not located within the road edge range, the initial grid road surface model of the road surface grid is updated based on the noise data of each target noise type to obtain the target grid road surface model of the road surface grid.

5. The method according to claim 4, characterized in that, The method further includes: If the road surface grid is located within the road edge range, the initial grid road surface model of the road surface grid is updated based on the preset edge noise data and the noise data of each target noise type to obtain the target grid road surface model of the road surface grid.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Calculate the saturation increment based on the environmental parameters and the second preset ratio; The simulated road surface is updated based on the saturation increment to obtain the updated simulated road surface.

7. A road surface simulation device, characterized in that, include: The acquisition module is used to acquire road surface simulation data, which includes environmental parameters, road description data of multiple road surface grids, road surface material, wear degree and friction coefficient; Processing module, used for: For each road surface grid, an initial grid road surface model is generated based on the road description data and road surface material of the road surface grid. For each road surface grid, the initial grid road surface model of the road surface grid is optimized based on the wear degree and road surface material of the road surface grid to obtain the target grid road surface model of the road surface grid. For each road surface grid, the target roughness, target metallicity, and target albedo of the road surface grid are determined based on the environmental parameters, the road surface material, and the friction coefficient of the road surface grid. The simulation module is used to render and generate simulated road surfaces based on the target grid road surface model, target roughness, target metallicity, and target albedo for each road surface grid. The processing module is specifically used for: Based on the preset correspondence between materials and noise types, the target noise type corresponding to the road surface material of the road surface mesh is determined; Based on the wear degree of the road surface grid, noise data for each target noise type is determined; Based on the noise data for each target noise type, the initial grid road surface model of the road surface grid is updated to obtain the target grid road surface model of the road surface grid.

8. An electronic device, characterized in that, include: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the road surface simulation method according to any one of claims 1 to 6 by executing the executable instructions.

9. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the road surface simulation method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, is used to implement the road surface simulation method according to any one of claims 1 to 6.

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