Virtual traffic scene establishment method and system based on natural driving

Abstract

The invention relates to the technical field of vehicle virtual simulation, and provides a virtual traffic scene establishment method and system based on natural driving in order to solve the problem that a virtual traffic scene lacks intelligent control and cannot be adjusted in time, and the method comprises the steps: collecting the environment information of a vehicle driving route and the motion information of a driving vehicle; performing information preprocessing on the environment information and the motion information of the running vehicle; establishing a virtual traffic scene of a vehicle driving route, and establishing a vehicle driving feature model by using the preprocessed motion information of the driving vehicle; establishing a vehicle stereoscopic model, and performing feature control on the vehicle stereoscopic model according to the vehicle driving feature model so as to establish a virtual traffic scene based on the natural driving platform; carrying out dynamic simulation and estimation on a real road condition on a natural driving platform through physical simulation and a deep learning algorithm; and optimizing the virtual traffic scene based on the natural driving platform by using dynamic simulation and a pre-estimation result.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle virtual simulation, and particularly to a method for establishing a virtual traffic scene based on natural driving and a system for establishing a virtual traffic scene based on natural driving. Background Art

[0002] In recent years, due to the rapid popularization of driverless vehicles, restrictions on sensors, map navigation, and traffic regulations of driverless vehicles often require a large number of experiments to plan driverless vehicles. At present, on the one hand, the cost of conducting experiments with physical driverless vehicles is relatively high, the verification time is relatively long, and there are also safety risks. On the other hand, the existing virtual traffic scenes have poor scalability in data processing for the collected data, and lack the functions of intelligent control and timely adjustment. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a method and a system for establishing a virtual traffic scene based on natural driving, which can perform intelligent control on the virtual traffic scene, make timely adjustments to the changes in the real scene, and have relatively low costs and small safety risks.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A method for establishing a virtual traffic scene based on natural driving includes the following steps: collecting environmental information of a vehicle driving route and motion information of a driving vehicle, where the motion information of the driving vehicle includes three-dimensional point cloud information and driving feature information; performing information preprocessing on the environmental information and the motion information of the driving vehicle; establishing a virtual traffic scene of the vehicle driving route by using the preset positions of sensors and the preprocessed environmental information, and establishing a vehicle driving feature model by using the preprocessed motion information of the driving vehicle; establishing a vehicle three-dimensional model by using the three-dimensional point cloud information, and performing feature control on the vehicle three-dimensional model in the virtual traffic scene according to the vehicle driving feature model to establish a virtual traffic scene based on a natural driving platform; based on the virtual traffic scene of the natural driving platform, performing dynamic simulation and prediction on the real road conditions on the natural driving platform through physical simulation and deep learning algorithms; optimizing the virtual traffic scene based on the natural driving platform by using the results of the dynamic simulation and prediction.

[0006] In an embodiment of the present invention, the performing information preprocessing on the environmental information and the motion information of the driving vehicle specifically includes: preprocessing the collected environmental information and motion information of the driving vehicle, including information screening, abnormal information inspection, and initial centralized processing; sending the preprocessed information to the natural driving platform, and coordinating and amplifying the preprocessed information through a multi-channel information integration algorithm.

[0007] In an embodiment of the present invention, the method for establishing the vehicle driving characteristic model is as follows: randomly select a number of pieces of information from the driving characteristic information, and perform non-normal information inspection on the selected pieces of information to obtain useful information, and use the useful information to establish the vehicle driving characteristic model.

[0008] In an embodiment of the present invention, the dynamics simulation and prediction of the real road conditions on the natural driving platform through physical simulation and deep learning algorithms specifically include: according to the physical simulation unit preset on the natural driving platform, collect the coordinated information, and establish a simulation model of the virtual traffic scene according to the environment in the real road conditions; simulate the simulation model of the virtual traffic scene by means of mathematical approximation, and calculate the changes in the environment in the vehicle driving route within a preset time period; when simulating the virtual traffic scene, perform storage and analysis processing on each time period, and compare the simulated virtual traffic scene with the real road conditions, and adjust the variable coefficient of the simulation model of the virtual traffic scene according to the comparison result; based on the deep learning algorithm of the natural driving platform, perform training processing on the coordinated time frame set information through the recurrent neural network algorithm; predict the environment in the vehicle driving route of the subsequent virtual traffic scene through the trained deep learning algorithm; compare and coordinate the results of the physical simulation and the deep learning algorithm, and obtain the dynamics simulation and prediction values of the virtual traffic scene according to the accuracy of the physical simulation and the prediction function of the deep learning algorithm; perform information conversion and display processing on the dynamics simulation and prediction values of the virtual traffic scene.

[0009] In an embodiment of the present invention, the optimization of the virtual traffic scene based on the natural driving platform by using the dynamics simulation and prediction results specifically includes: continuously collect the real road conditions of the vehicle driving route transmitted by the sensor, and monitor the environmental changes in the vehicle driving route and the working conditions of the sensor; compare the collected real road condition information with the simulated road condition information at preset time intervals, and predict the accuracy of the simulation model of the virtual traffic scene, so as to identify the error between the real road condition information and the simulation result, wherein the comparison of the collected real road condition information and the simulated road condition information includes the trend and the extreme value of the change amount of the real road condition information and the simulation result; perform variable coefficient optimization processing on the simulation model of the virtual traffic scene through the past information and the real-time information according to the predicted result; set a rectification unit on the natural driving platform, and use the gradient descent algorithm to rectify the simulation model of the virtual traffic scene; test the rectified simulation model of the virtual traffic scene, compare the simulation results before and after rectification with the real road condition information, and adjust the rectification and the optimization method of the variable coefficient according to the test result.

[0010] A virtual traffic scenario establishment system based on natural driving, comprising: a collection module for collecting environmental information of a vehicle driving route and motion information of a driving vehicle, where the motion information of the driving vehicle includes three-dimensional point cloud information and driving feature information; an information processing module for performing information preprocessing on the environmental information and the motion information of the driving vehicle; a first model establishment module for establishing a virtual traffic scenario of the vehicle driving route by using a preset position of a sensor and the preprocessed environmental information, and establishing a vehicle driving feature model by using the preprocessed motion information of the driving vehicle; a second model establishment module for establishing a vehicle solid model by using the three-dimensional point cloud information, and performing feature control on the vehicle solid model according to the vehicle driving feature model in the virtual traffic scenario to establish a virtual traffic scenario based on a natural driving platform; a simulation module for performing dynamic simulation and prediction of a real road condition on the natural driving platform through physical simulation and deep learning algorithms based on the virtual traffic scenario of the natural driving platform; and an optimization module for optimizing the virtual traffic scenario based on the natural driving platform by using the dynamic simulation and prediction results.

[0011] In an embodiment of the present invention, the information processing module is specifically configured to: perform preprocessing on the collected environmental information and the motion information of the driving vehicle, including information screening, abnormal information inspection, and initial centralized processing; send the preprocessed information to the natural driving platform, and coordinate and amplify the preprocessed information through a multi-channel information integration algorithm.

[0012] In an embodiment of the present invention, the method for establishing the vehicle driving feature model is: randomly select a number of pieces of information from the driving feature information, and perform abnormal information inspection on the selected number of pieces of information to obtain useful information, and establish the vehicle driving feature model by using the useful information.

[0013] In one embodiment of the present invention, the simulation module is specifically configured to: collect the coordinated information according to the physical simulation unit preset on the natural driving platform, and establish a simulation model of the virtual traffic scene according to the environment in the real road conditions; simulate the simulation model of the virtual traffic scene by means of mathematical approximation, and calculate the changes in the environment in the vehicle driving route for a preset duration; when simulating the virtual traffic scene, store and analyze each duration, compare the simulated virtual traffic scene with the real road conditions, and adjust the variable coefficient of the simulation model of the virtual traffic scene according to the comparison result; based on the deep learning algorithm of the natural driving platform, perform training processing on the coordinated duration frame set information through the recurrent neural network algorithm; estimate the environment in the vehicle driving route of the subsequent virtual traffic scene through the trained deep learning algorithm; compare and coordinate the results of the physical simulation and the deep learning algorithm, and obtain the dynamic simulation and prediction value of the virtual traffic scene according to the accuracy of the physical simulation and the prediction function of the deep learning algorithm; perform information conversion and display processing on the dynamic simulation and prediction value of the virtual traffic scene.

[0014] In one embodiment of the present invention, the optimization module is specifically configured to: continuously collect the real road conditions of the vehicle driving route transmitted by the sensor, and monitor the environmental changes in the vehicle driving route and the working conditions of the sensor; compare the collected real road condition information with the simulated road condition information at a preset time interval, and estimate the accuracy of the simulation model of the virtual traffic scene, so as to identify the error between the real road condition information and the simulation result, wherein the comparison of the collected real road condition information and the simulated road condition information includes the trend of the real road condition information and the simulated result and the extreme value of the change amount; perform variable coefficient optimization processing on the simulation model of the virtual traffic scene according to the estimated result through the past information and the real-time information; set a rectification unit on the natural driving platform, and rectify the simulation model of the virtual traffic scene by using the gradient descent algorithm; test the rectified simulation model of the virtual traffic scene, compare the simulation results before and after rectification with the real road condition information, and adjust the rectification and the optimization method of the variable coefficient according to the test result.

[0015] Advantages of the present invention:

[0016] The present invention collects the environmental information of the vehicle driving route and the motion information of the driving vehicle, preprocesses the environmental information and the motion information of the driving vehicle, then uses the preprocessed environmental information and motion information to establish a virtual traffic scene and a vehicle driving feature model of the vehicle driving route, and uses three-dimensional point cloud information to establish a vehicle three-dimensional model, and controls the features of the vehicle three-dimensional model according to the vehicle driving feature model in the virtual traffic scene, so as to establish a virtual traffic scene based on the natural driving platform. Finally, through physical simulation and deep learning algorithms, dynamic simulation and prediction of the real road conditions are carried out on the natural driving platform, and the virtual traffic scene based on the natural driving platform is optimized by using the results of the dynamic simulation and prediction. Therefore, the virtual traffic scene can be intelligently controlled, the changes in the real scene can be adjusted in time, and the cost is low and the safety risk is small. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of a method for establishing a virtual traffic scene based on natural driving according to an embodiment of the present invention;

[0018] Figure 2 is a block diagram of a system for establishing a virtual traffic scene based on natural driving according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Figure 1 is a flowchart of a method for establishing a virtual traffic scene based on natural driving according to an embodiment of the present invention.

[0021] As Figure 1 shown, the method for establishing a virtual traffic scene based on natural driving according to an embodiment of the present invention includes the following steps:

[0022] S1, collect the environmental information of the vehicle driving route and the motion information of the driving vehicle, and the motion information of the driving vehicle includes three-dimensional point cloud information and driving feature information.

[0023] In an embodiment of the present invention, the environmental information in the vehicle driving route and the motion information of the driving vehicle can be collected through natural driving. Among them, the environmental information may include the distribution of roads and the construction conditions beside the roads, and the motion information of the driving vehicle may include driving feature information such as the three-dimensional point cloud information of the vehicle, the position of the vehicle, and the driving speed of the driving vehicle.

[0024] S2, perform information preprocessing on the environmental information and the motion information of the moving vehicle.

[0025] In an embodiment of the present invention, performing information preprocessing on the environmental information and the motion information of the moving vehicle may specifically include: preprocessing the collected environmental information and the motion information of the moving vehicle, including information screening, abnormal information inspection, and initial centralized processing; sending the preprocessed information to the natural driving platform, and coordinating and amplifying the preprocessed information through a multi-channel information integration algorithm.

[0026] Specifically, first, the environmental information of the vehicle driving route and the motion information of the moving vehicle collected may be preliminarily processed by a preset information screening unit in the cloud computing device. The process of preliminary processing may include information denoising and retaining useful information, so that the information can be made clearer. Among them, the screened information may be subjected to abnormal information inspection in the cloud computing device through an abnormal information inspection method, so that abnormal information points different from the distribution of normal information can be identified. Then, the detected abnormal information may be marked in the cloud computing device, and the normal information and the abnormal information may be stored separately, and the screened and abnormally information-inspected information may be subjected to initial centralized processing, classifying and measuring the information based on time units and information categories to obtain initial centralized information. Secondly, an initial parsing operation may be performed on the initial centralized information in the cloud computing device, and finally, the initially parsed information may be uploaded to the natural driving platform.

[0027] Further, a multi-channel information coordination unit may be preset in the natural driving platform to receive information transmitted by multiple sensors. Among them, when receiving information transmitted by multiple sensors, it is necessary to ensure the consistency of the information in time and space, and coordinate the multi-channel information according to the weighted average method. Secondly, the coordinated information may be processed by a dimensionality reduction method, so as to select information characteristics and perform information amplification processing on the processed information. Among them, during the process of information amplification, it is necessary to ensure the consistency of the information during information coordination and amplification. Finally, the coordinated and amplified information may be uploaded to the natural driving platform.

[0028] S3, establish a virtual traffic scene of the vehicle driving route by using the preset positions of the sensors and the preprocessed environmental information, and establish a vehicle driving feature model by using the preprocessed motion information of the moving vehicle.

[0029] In an embodiment of the present invention, the method for establishing a vehicle driving feature model is: randomly select several pieces of information from the driving feature information, and perform abnormal information inspection on the selected several pieces of information to obtain useful information, and establish a vehicle driving feature model by using the useful information.

[0030] Among them, during the process of obtaining useful information, it is possible to determine whether the driving characteristic information is useful and compliant, that is, set extreme values within the range of being useful and compliant. When all the driving characteristic information conforms to the extreme values within the range, it can be determined that the driving characteristic information is useful and compliant. Secondly, an uninterrupted inspection operation can be performed on the driving characteristic information within the preset duration. That is, if some driving characteristic information is missing within the preset duration, the missing part can be supplemented to obtain secondary sampling information. Finally, the secondary sampling information can be screened to obtain useful information.

[0031] S4. Establish a vehicle solid model using three-dimensional point cloud information, and perform feature control on the vehicle solid model according to the vehicle driving characteristic model in the virtual traffic scene to establish a virtual traffic scene based on the natural driving platform.

[0032] In an embodiment of the present invention, performing feature control on the vehicle solid model may include controlling the spatial path and motion state of the moving vehicle. Among them, the motion state of the moving vehicle may include states such as the vehicle starting, turning, and stopping.

[0033] S5. Based on the virtual traffic scene of the natural driving platform, perform dynamic simulation and prediction on the real road conditions on the natural driving platform through physical simulation and deep learning algorithms.

[0034] In an embodiment of the present invention, performing dynamic simulation and prediction on the real road conditions on the natural driving platform through physical simulation and deep learning algorithms may specifically include: collecting the coordinated information according to the physical simulation unit preset on the natural driving platform, and establishing a simulation model of the virtual traffic scene according to the environment in the real road conditions; simulating the simulation model of the virtual traffic scene by means of mathematical approximation, and calculating the changes in the environment in the vehicle driving route within the preset duration; when simulating the virtual traffic scene, performing storage and analysis processing on each duration, comparing the simulated virtual traffic scene with the real road conditions, and adjusting the variable coefficients of the simulation model of the virtual traffic scene according to the comparison results; based on the deep learning algorithm of the natural driving platform, performing training processing on the coordinated duration frame set information through the recurrent neural network algorithm; predicting the environment in the vehicle driving route of the subsequent virtual traffic scene through the trained deep learning algorithm; comparing and coordinating the results of the physical simulation and the deep learning algorithm, and obtaining the dynamic simulation and prediction values of the virtual traffic scene according to the accuracy of the physical simulation and the prediction function of the deep learning algorithm; performing information conversion and display processing on the dynamic simulation and prediction values of the virtual traffic scene.

[0035] S6. Optimize the virtual traffic scene based on the natural driving platform using the dynamic simulation and prediction results.

[0036] Specifically, it may include: continuously collecting the actual road conditions of the vehicle driving route transmitted by the sensor, and monitoring the environmental changes and the working conditions of the sensor in the vehicle driving route; comparing the collected actual road conditions information and the simulated road conditions information at preset time intervals, and estimating the accuracy of the simulation model of the virtual traffic scenario, so as to identify the errors between the actual road conditions information and the simulation results, where the comparison of the collected actual road conditions information and the simulated road conditions information includes the trends and the extreme values of the change amounts of the actual road conditions information and the simulation results; optimizing the variable coefficients of the simulation model of the virtual traffic scenario through the past information and the real-time information according to the estimated results; setting a rectifying unit on the natural driving platform and rectifying the simulation model of the virtual traffic scenario by using the gradient descent algorithm; testing the rectified simulation model of the virtual traffic scenario, comparing the simulation results before and after rectification with the actual road conditions information, and adjusting the rectifying and variable coefficient optimization methods according to the test results.

[0037] According to the method for establishing a virtual traffic scenario based on natural driving in an embodiment of the present invention, by collecting the environmental information of the vehicle driving route and the motion information of the driving vehicle, preprocessing the environmental information and the motion information of the driving vehicle, then using the preprocessed environmental information and motion information to establish a virtual traffic scenario of the vehicle driving route and a vehicle driving characteristic model, and using three-dimensional point cloud information to establish a vehicle three-dimensional model, and performing feature control on the vehicle three-dimensional model according to the vehicle driving characteristic model in the virtual traffic scenario, so as to establish a virtual traffic scenario based on a natural driving platform, and finally performing dynamic simulation and prediction on the actual road conditions on the natural driving platform through physical simulation and deep learning algorithms, and optimizing the virtual traffic scenario based on the natural driving platform by using the dynamic simulation and prediction results. Thus, intelligent control of the virtual traffic scenario can be performed, timely adjustment of the changes in the actual scenario can be made, and the cost is low and the safety risk is small.

[0038] To implement the method for establishing a virtual traffic scenario based on natural driving in the above embodiment, the present invention also proposes a system for establishing a virtual traffic scenario based on natural driving.

[0039] Such as Figure 2As shown in the figure, the virtual traffic scenario establishment system based on natural driving according to the embodiment of the present invention includes: a collection module 100, an information processing module 200, a first model establishment module 300, a second model establishment module 400, a simulation module 500, and an optimization module 600. Among them, the collection module 100 is used to collect the environmental information of the vehicle driving route and the motion information of the driving vehicle. The motion information of the driving vehicle includes three-dimensional point cloud information and driving feature information; the information processing module 200 is used to perform information preprocessing on the environmental information and the motion information of the driving vehicle; the first model establishment module 300 is used to establish a virtual traffic scenario of the vehicle driving route by using the preset positions of the sensors and the preprocessed environmental information, and establish a vehicle driving feature model by using the preprocessed motion information of the driving vehicle; the second model establishment module 400 is used to establish a vehicle three-dimensional model by using the three-dimensional point cloud information, and perform feature control on the vehicle three-dimensional model according to the vehicle driving feature model in the virtual traffic scenario to establish a virtual traffic scenario based on the natural driving platform; the simulation module 500 is used to perform dynamic simulation and prediction of the real road conditions on the natural driving platform through physical simulation and deep learning algorithms based on the virtual traffic scenario of the natural driving platform; the optimization module 600 is used to optimize the virtual traffic scenario based on the natural driving platform by using the dynamic simulation and prediction results.

[0040] In an embodiment of the present invention, the collection module 100 can collect the environmental information in the vehicle driving route and the motion information of the driving vehicle through natural driving. Among them, the environmental information can include the distribution of the road and the construction situation beside the road, and the motion information of the driving vehicle can include driving feature information such as the three-dimensional point cloud information of the vehicle, the position of the vehicle, and the motion speed of the driving vehicle.

[0041] In an embodiment of the present invention, the information processing module 200 is specifically used for: preprocessing the collected environmental information and the motion information of the driving vehicle, including information screening, abnormal information inspection, and initial centralized processing; sending the preprocessed information to the natural driving platform, and coordinating and amplifying the preprocessed information through a multi-channel information integration algorithm.

[0042] Specifically, first, the environmental information of the vehicle driving route and the motion information of the driving vehicle collected can be preliminarily processed by the information screening unit preset in the cloud computing device. The preliminary processing process may include information denoising and retaining useful information, so that the information can be made clearer. Among them, the screened information can be checked for abnormal information in the cloud computing device through an abnormal information checking method, so that abnormal information points with different distributions from normal information can be identified. Then, the detected abnormal information can be marked in the cloud computing device, and the normal information and abnormal information can be stored separately, and the information after screening and abnormal information checking can be initially processed centrally, classifying and measuring the information based on time units and information categories to obtain initial centralized information. Secondly, the initial centralized information can be initially parsed in the cloud computing device, and finally the initially parsed information can be uploaded to the natural driving platform.

[0043] Further, a multi-channel information coordination unit can be preset in the natural driving platform to receive information transmitted by multiple sensors. Among them, when receiving information transmitted by multiple sensors, it is necessary to ensure the consistency of the information in time and space, and coordinate the multi-channel information according to the weighted average method. Secondly, the coordinated information can be processed by means of dimensionality reduction, so as to select information characteristics and perform information amplification processing on the processed information. Among them, during the process of information amplification, it is necessary to ensure the consistency of the information during information coordination and amplification. Finally, the coordinated and amplified information can be uploaded to the natural driving platform.

[0044] In an embodiment of the present invention, the method for establishing a vehicle driving feature model is as follows: Several pieces of information can be randomly selected from the driving feature information, and the selected several pieces of information can be checked for abnormal information to obtain useful information, and the vehicle driving feature model can be established using the useful information.

[0045] Among them, during the process of obtaining useful information, it can be determined whether the driving feature information is useful and compliant, that is, extreme values are set within the range of being useful and compliant. When all the driving feature information meets the extreme values within the range, it can be determined that the driving feature information is useful and compliant. Secondly, the driving feature information within the preset time period can be continuously checked, that is, if some of the driving feature information is missing within the preset time period, the missing part can be supplemented to obtain secondary sampling information. Finally, the secondary sampling information can be screened to obtain useful information.

[0046] In an embodiment of the present invention, the feature control of the vehicle three-dimensional model may include controlling the spatial path and motion state of the driving vehicle. Among them, the motion state of the driving vehicle may include states such as starting, turning, and stopping of the vehicle.

[0047] In an embodiment of the present invention, the simulation module 500 may specifically be configured to: collect the coordinated information according to the physical simulation units preset on the natural driving platform, and establish a simulation model of the virtual traffic scenario based on the environment in the real road conditions; simulate the simulation model of the virtual traffic scenario by means of mathematical approximation, and calculate the changes in the environment in the vehicle driving route for a preset duration; when simulating the virtual traffic scenario, store and analyze each duration, compare the simulated virtual traffic scenario with the real road conditions, and adjust the variable coefficients of the simulation model of the virtual traffic scenario according to the comparison result; based on the deep learning algorithm of the natural driving platform, perform training processing on the coordinated duration frame set information through the recurrent neural network algorithm; estimate the environment in the vehicle driving route of the subsequent virtual traffic scenario through the trained deep learning algorithm; compare and coordinate the results of the physical simulation and the deep learning algorithm, and obtain the dynamic simulation and prediction value of the virtual traffic scenario according to the accuracy of the physical simulation and the prediction function of the deep learning algorithm; perform information conversion and display processing on the dynamic simulation and prediction value of the virtual traffic scenario.

[0048] In an embodiment of the present invention, the optimization module 600 may specifically be configured to: continuously collect the real road conditions of the vehicle driving route transmitted by the sensors, and monitor the environmental changes in the vehicle driving route and the working conditions of the sensors; compare the collected real road condition information with the simulated road condition information at preset time intervals, and estimate the accuracy of the simulation model of the virtual traffic scenario, so as to identify the errors between the real road condition information and the simulation results, wherein the comparison of the collected real road condition information and the simulated road condition information includes the trend and the extreme value of the change amount between the real road condition information and the simulation results; perform variable coefficient optimization processing on the simulation model of the virtual traffic scenario according to the estimated result through the past information and the real-time information; perform rectification on the simulation model of the virtual traffic scenario by using the gradient descent algorithm by setting a rectification unit on the natural driving platform; test the rectified simulation model of the virtual traffic scenario, compare the simulation results before and after rectification with the real road condition information, and adjust the rectification and the optimization method of the variable coefficients according to the test results.

[0049] In summary, the present invention collects environmental information of the vehicle driving route and motion information of the driving vehicle through a collection module, pre-processes the environmental information and the motion information of the driving vehicle through an information processing module, then establishes a vehicle driving feature model and a vehicle three-dimensional model through a first model establishment module and a second model establishment module, and controls the features of the vehicle three-dimensional model according to the vehicle driving feature model in a virtual traffic scene to establish a virtual traffic scene based on a natural driving platform. Finally, a dynamics simulation and prediction are performed through a simulation module, and the virtual traffic scene based on the natural driving platform is optimized by an optimization module. Thus, the virtual traffic scene can be intelligently controlled, the changes in the real scene can be adjusted in a timely manner, and the cost is relatively low and the safety risk is small.

[0050] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more unless otherwise specifically defined.

[0051] In the present invention, unless otherwise clearly specified and defined, the terms such as "installed", "connected", "connected to", "fixed" and the like should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal connection of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] In the present invention, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "below" and "beneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0053] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0054] Any process or method description represented in a flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a manner that may not be shown or discussed in the order, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0055] The logic and / or steps represented in a flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0056] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0057] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0058] In addition, each functional unit in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0059] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for establishing a virtual traffic scene based on natural driving, characterized in that Including the following steps: Collect the environmental information of the vehicle driving route and the movement information of the driving vehicle, where the movement information of the driving vehicle includes three-dimensional point cloud information and driving feature information; Conduct information preprocessing on the environmental information and the movement information of the driving vehicle; Establish a virtual traffic scene of the vehicle driving route using the preset position of the sensor and the preprocessed environmental information, and establish a vehicle driving feature model using the preprocessed movement information of the driving vehicle; Establish a vehicle three-dimensional model using the three-dimensional point cloud information, and perform feature control on the vehicle three-dimensional model according to the vehicle driving feature model in the virtual traffic scene to establish a virtual traffic scene based on the natural driving platform; Based on the virtual traffic scene of the natural driving platform, conduct dynamic simulation and prediction of the real road conditions on the natural driving platform through physical simulation and deep learning algorithms; Optimize the virtual traffic scene based on the natural driving platform using the results of the dynamic simulation and prediction; 2. The method for establishing a virtual traffic scene based on natural driving according to claim 1, wherein The information preprocessing of the environmental information and the movement information of the driving vehicle specifically includes: Perform preprocessing on the collected environmental information and the movement information of the driving vehicle, including information screening, abnormal information inspection, and initial centralized processing; Send the preprocessed information to the natural driving platform, and coordinate and amplify the preprocessed information through a multi-channel information integration algorithm; 3. The method for establishing a virtual traffic scene based on natural driving according to claim 2, wherein The method for establishing the vehicle driving feature model is: randomly select several pieces of information from the driving feature information, and conduct abnormal information inspection on the selected several pieces of information to obtain useful information, and establish the vehicle driving feature model using the useful information; 4. The method for establishing a virtual traffic scene based on natural driving according to claim 3, wherein The dynamic simulation and prediction of the real road conditions on the natural driving platform through physical simulation and deep learning algorithms specifically include: According to the physical simulation unit preset on the natural driving platform, collect the coordinated information, and establish a simulation model of the virtual traffic scene according to the environment in the real road conditions; Simulate the simulation model of the virtual traffic scene through a mathematical approximation method, and calculate the changes in the environment in the vehicle driving route for a preset duration; When simulating the virtual traffic scene, perform storage and analysis processing on each duration, compare the simulated virtual traffic scene with the real road conditions, and adjust the variable coefficients of the simulation model of the virtual traffic scene according to the comparison results; Based on the deep learning algorithm of the natural driving platform, perform training processing on the coordinated duration frame set information through a recurrent neural network algorithm; Estimate the environment in the vehicle driving route of the subsequent virtual traffic scene through the trained deep learning algorithm; Compare and coordinate the results of the physical simulation and the deep learning algorithm, and obtain the dynamic simulation and prediction values of the virtual traffic scene according to the accuracy of the physical simulation and the prediction function of the deep learning algorithm; Perform information conversion and display processing on the dynamic simulation and prediction values of the virtual traffic scene; 5. The method for establishing a virtual traffic scenario based on natural driving according to claim 4, wherein The optimization of the virtual traffic scene based on the natural driving platform using the results of the dynamic simulation and prediction specifically includes: Continuously collect the actual road conditions of the vehicle driving route transmitted by the sensor, and monitor the environmental changes and the working conditions of the sensor in the vehicle driving route; Compare the collected actual road condition information with the simulated road condition information at a preset time interval, and estimate the accuracy of the simulation model of the virtual traffic scenario, so as to identify the errors between the actual road condition information and the simulation results. Among them, the comparison of the collected actual road condition information and the simulated road condition information includes the trend of the actual road condition information and the simulation results and the extreme value of the change amount; Perform variable coefficient optimization processing on the simulation model of the virtual traffic scenario according to the estimated results through past information and real-time information; Set a rectification unit on the natural driving platform and use the gradient descent algorithm to rectify the simulation model of the virtual traffic scenario; Test the rectified simulation model of the virtual traffic scenario, compare the simulation results before and after rectification with the actual road condition information, and adjust the rectification and variable coefficient optimization methods according to the test results.

6. A virtual traffic scenario establishment system based on natural driving, characterized in that, It includes: A collection module, which is used to collect the environmental information of the vehicle driving route and the motion information of the driving vehicle. The motion information of the driving vehicle includes three-dimensional point cloud information and driving feature information; An information processing module, which is used to perform information preprocessing on the environmental information and the motion information of the driving vehicle; A first model establishment module, which is used to establish a virtual traffic scenario of the vehicle driving route by using the preset position of the sensor and the preprocessed environmental information, and establish a vehicle driving feature model by using the preprocessed motion information of the driving vehicle; A second model establishment module, which is used to establish a vehicle three-dimensional model by using the three-dimensional point cloud information, and perform feature control on the vehicle three-dimensional model according to the vehicle driving feature model in the virtual traffic scenario, so as to establish a virtual traffic scenario based on the natural driving platform; A simulation module, which is used to perform dynamic simulation and prediction of the actual road conditions on the natural driving platform based on the virtual traffic scenario of the natural driving platform through physical simulation and deep learning algorithms; An optimization module, which is used to optimize the virtual traffic scenario based on the natural driving platform by using the dynamic simulation and prediction results.

7. The system for establishing a virtual traffic scenario based on natural driving according to claim 6, wherein The information processing module is specifically used for: Perform preprocessing on the collected environmental information and the motion information of the driving vehicle, including information screening, abnormal information inspection, and initial centralized processing; Send the preprocessed information to the natural driving platform, and coordinate and amplify the preprocessed information through a multi-channel information integration algorithm.

8. The virtual traffic scenario establishment system based on natural driving according to claim 7, characterized in that, The method for establishing the vehicle driving feature model is: randomly select several pieces of information from the driving feature information, and perform abnormal information inspection on the selected several pieces of information to obtain useful information, and use the useful information to establish the vehicle driving feature model.

9. The system for establishing a virtual traffic scene based on natural driving according to claim 8, wherein, The simulation module is specifically used for: According to the physical simulation unit preset on the natural driving platform, collect the coordinated information, and establish a simulation model of the virtual traffic scenario according to the environment in the actual road conditions; Simulate the simulation model of the virtual traffic scenario through mathematical approximation, and calculate the changes in the environment in the vehicle driving route for a preset duration; When simulating the virtual traffic scenario, store and analyze each duration, compare the simulated virtual traffic scenario with the real road conditions, and adjust the variable coefficients of the simulation model of the virtual traffic scenario according to the comparison results; Based on the deep learning algorithm of the natural driving platform, train the coordinated duration frame set information through the recurrent neural network algorithm; Estimate the environment in the vehicle driving route of the subsequent virtual traffic scenario through the trained deep learning algorithm; Compare and coordinate the results of physical simulation and deep learning algorithm, and obtain the dynamic simulation and estimated values of the virtual traffic scenario according to the accuracy of physical simulation and the estimation function of deep learning algorithm; Perform information conversion and display processing on the dynamic simulation and estimated values of the virtual traffic scenario.

10. The system for establishing a virtual traffic scenario based on natural driving according to claim 9, characterized in that The optimization module is specifically used for: continuously collecting the real road conditions of the vehicle driving route transmitted by the sensor, and monitoring the environmental changes in the vehicle driving route and the working conditions of the sensor; Compare the collected real road condition information with the simulated road condition information at preset time intervals, and estimate the accuracy of the simulation model of the virtual traffic scenario, so as to identify the errors between the real road condition information and the simulation results. Among them, the comparison of the collected real road condition information and the simulated road condition information includes the trend of the real road condition information and the simulation results and the extreme values of the change amount; Optimize the variable coefficients of the simulation model of the virtual traffic scenario according to the estimated results through past information and real-time information; Set a rectification unit on the natural driving platform, and rectify the simulation model of the virtual traffic scenario by using the gradient descent algorithm; Test the rectified simulation model of the virtual traffic scenario, compare the simulation results before and after rectification with the real road condition information, and adjust the rectification and variable coefficient optimization methods according to the test results.

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