CN115422836A - Method and device for generating automobile driving simulation scene, computer and storage medium - Google Patents
Method and device for generating automobile driving simulation scene, computer and storage medium Download PDFInfo
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Abstract
The invention provides a method and a device for generating an automobile driving simulation scene, a computer and a storage medium, and belongs to the technical field of automobile driving simulation. The method comprises the following steps: acquiring real scene data, and determining real scene key elements based on the real scene data; generating a preliminary simulation scene according to the key elements of the real scene, and determining the key elements of the preliminary simulation scene based on the simulation scene data; obtaining preliminary scene category data according to the preliminary simulation scene key elements and the preset mapping relation between the key elements and the scene category data; comparing the preliminary scene category data with preset target scene category data to obtain lacking scene category data, and generating a supplementary simulation scene based on the lacking scene category data; and combining the primary simulation scene and the supplementary simulation scene to obtain a final simulation scene. The method and the device have the advantages that the generation target is clear according to the lack of the scene category data, the randomness of the generation of the simulation scene is reduced, and the generation efficiency is improved.
Description
Technical Field
The invention relates to the technical field of automobile driving simulation, in particular to a method and a device for generating an automobile driving simulation scene, a computer and a storage medium.
Background
With the rapid development of computer technology, automatic driving is widely concerned by people, and in order to improve the safety and reliability of an automatic driving system, the automatic driving process is often required to be simulated and operated through the automatic driving system. In the simulation operation process, the automatic driving automobile is tested and verified in a virtual scene, so that potential safety risks can be avoided, and the research and development test cost can be reduced. However, the existing simulation scene building usually adopts manual work to input the data of the natural driving scene into the simulation software for building, so that the labor input cost is high, the efficiency is low, and the problems that the output scene is different from the actual scene due to manual data input errors and the like are easily caused. Therefore, at present, a computer is usually adopted to automatically generate a simulation scene based on a real scene, so as to solve the problems of high cost, low efficiency and the like of manually building the simulation scene in the prior art. However, when a simulation scene is automatically generated by a computer at present, the simulation scene has high randomness, and a large amount of generation is needed to enable the generated scene to cover the requirements of each scene, so that the generation process of the simulation scene is long, and the efficiency is low.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, an object of the present invention is to provide a method, an apparatus, a computer and a storage medium for generating a simulation scene of driving of an automobile, which are used to solve the problems of high randomness, low generation efficiency and the like when a simulation scene is automatically generated in the prior art.
In order to achieve the above objects and other related objects, the present invention provides a method for generating a simulation scene of driving of a vehicle, including:
acquiring real scene data, and determining real scene key elements based on the real scene data;
generating a preliminary simulation scene according to the key elements of the real scene, and determining the key elements of the preliminary simulation scene based on the simulation scene data;
obtaining preliminary scene category data according to the preliminary simulation scene key elements and a preset mapping relation between the key elements and the scene category data;
comparing the preliminary scene category data with preset target scene category data to obtain lacking scene category data, and generating a supplementary simulation scene based on the lacking scene category data;
and combining the preliminary simulation scene and the supplementary simulation scene to obtain a final simulation scene.
Optionally, determining the real scene key elements based on the real scene data comprises:
collecting automatic driving data of a real automobile, and determining the automatic driving data as training data;
establishing an analysis model, and training the analysis model by adopting the training data;
and analyzing the real scene data by adopting the trained analysis model to obtain the key elements of the real scene.
Optionally, the automatic driving data of the real automobile comprises at least one of a live-action video stream, a live-action point cloud stream, a millimeter wave radar detection target, a vehicle speed of the vehicle and a steering angle of the vehicle.
Optionally, the simulation scenario includes at least one of traffic elements, vehicle control strategies, traffic flows, and configuration information; the vehicle control strategy includes at least one of vehicle parameter information and vehicle behavior information.
Optionally, the vehicle parameter information includes at least one of driver information, vehicle type, vehicle name, vehicle starting position and vehicle starting speed; the vehicle behavior information includes at least one of a vehicle speed change, a vehicle lane change behavior, a lane change duration, a lane change trigger time, and a lane change trigger range.
Optionally, the key elements include at least one of lane lines, curbs, pedestrians, obstacles, and other vehicles on the lane of the actual road.
Optionally, the real scene data includes at least one of multi-channel camera data, laser radar data, millimeter wave radar data, real-time dynamics, inertial navigation measurement unit data, and vehicle own data.
The invention also provides a device for generating the automobile driving simulation scene, which comprises the following components:
the real scene acquisition module acquires real scene data and determines real scene key elements based on the real scene data;
the scene category analysis module is used for generating a preliminary simulation scene according to the real scene key elements, determining the preliminary simulation scene key elements based on the simulation scene data, and obtaining preliminary scene category data according to the preliminary simulation scene key elements and a preset mapping relation between the key elements and the scene category data;
the scene generation module is used for comparing the preliminary scene category data with preset target scene category data to obtain lacking scene category data, generating a supplementary simulation scene based on the lacking scene category data, and combining the preliminary simulation scene and the supplementary simulation scene to obtain a final simulation scene.
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the generation method of the automobile driving simulation scene.
The present invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the steps of the method for generating a simulation scenario for driving a vehicle as set forth in any one of the above.
As described above, the method, the apparatus, the computer and the storage medium for generating the automobile driving simulation scene according to the present invention have the following advantages: and determining preliminary scene category data based on the preliminary scene key elements, wherein the preliminary scene category data comprises category information of each scene in the preliminary simulation scene. The target scene category data includes information of each scene category required in the automobile driving simulation, and the lacking scene category data can be obtained by comparing the preliminary scene category data with the target scene category data. The lacking scene type data comprises information of a scene which is lacking in the preliminary simulation scene compared with the required automobile driving simulation scene, so that the supplementing simulation scene can be generated according to the lacking scene type data. And combining the supplementary simulation scene and the preliminary simulation scene into a final simulation scene, wherein the scene type of the final simulation scene meets each scene type required by the automobile driving simulation. In the simulation scene generation process, the generation target is clear according to the lack of scene category data, the randomness of the generation of the simulation scene is reduced, and the generation efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
In the drawings:
FIG. 1 is a flow chart diagram of a method for generating a simulation scene of driving an automobile according to an embodiment of the present invention;
FIG. 2 is a block diagram of an apparatus for generating a simulation scenario of driving a vehicle according to an embodiment of the present invention;
FIG. 3 illustrates a schematic structural diagram of a computer system suitable for use to implement the electronic device of the embodiments of the subject application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure of the present specification, wherein the following description is made for the embodiments of the present invention with reference to the accompanying drawings and the preferred embodiments. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
Firstly, it should be noted that, in order to improve the safety and reliability of the automatic driving system, it is often necessary to perform a simulation operation on the automatic driving process through the automatic driving system. In the simulation operation process, the automatic driving automobile is tested and verified in a virtual scene, so that potential safety risks can be avoided, and the research and development test cost can be reduced. However, the existing simulation scene building usually adopts manual work to input the data of the natural driving scene into the simulation software for building, so that the labor input cost is high, the efficiency is low, and the problems that the output scene is different from the actual scene due to manual data input errors and the like are easily caused. Therefore, at present, a computer is usually adopted to automatically generate a simulation scene based on a real scene, so as to solve the problems of high cost, low efficiency and the like of manually building the simulation scene in the prior art. However, when a simulation scene is automatically generated by a computer at present, the simulation scene has high randomness, and a large amount of generation is needed to enable the generated scene to cover the requirements of each scene, so that the generation process of the simulation scene is long, and the efficiency is low.
Therefore, the embodiment provides a method for generating an automobile driving simulation scene, which includes the following steps:
s10, acquiring real scene data, and determining key elements of a real scene based on the real scene data;
s20, generating a preliminary simulation scene according to the key elements of the real scene, and determining the key elements of the preliminary simulation scene based on the simulation scene data;
s30, obtaining preliminary scene category data according to the preliminary simulation scene key elements and the preset mapping relation between the key elements and the scene category data;
s40, comparing the preliminary scene category data with preset target scene category data to obtain lacking scene category data, and generating a supplementary simulation scene based on the lacking scene category data;
and S50, combining the primary simulation scene and the supplementary simulation scene to obtain a final simulation scene.
In some embodiments, the step S10, that is, the step of determining the key elements of the real scene based on the real scene data, includes the following sub-steps:
s11: automatic driving data of a real automobile are collected, and the automatic driving data are determined as training data. Wherein the step autopilot data is obtained by recording data generated by sensors during actual operation of the autopilot vehicle.
S12: and establishing an analysis model, and training the analysis model by adopting the training data. In this embodiment, the analysis model is a perception model with a deep learning algorithm.
S13: and analyzing the real scene data by adopting the trained analysis model to obtain the key elements of the real scene.
The automatic driving data are detected and tracked through a deep learning algorithm of an analysis model, and then the reappearance of the actual scene at that time is obtained, wherein the reappearance of the actual scene comprises key elements such as lane lines, road edges, other vehicles, pedestrians and obstacles on the lanes of the actual road.
Specifically, in step S11, the automatic driving data of the real vehicle includes at least one of a live-action video stream, a live-action point cloud stream, a millimeter wave radar detection target, a vehicle speed of the vehicle, and a steering angle of the vehicle. The autonomous driving data is obtained by recording data generated by sensors during actual operation of the autonomous vehicle.
In some embodiments, the simulation scenario includes at least one of traffic elements, vehicle control strategies, traffic flows, and configuration information, the vehicle control strategies including at least one of vehicle parameter information and vehicle behavior information.
Specifically, the vehicle parameter information is various parameter information of the vehicle, and in some embodiments, the vehicle parameter information includes at least one of driver information, vehicle type, vehicle name, vehicle starting position, and vehicle starting speed; the vehicle behavior information includes at least one of a vehicle speed change, a vehicle lane change behavior, a lane change duration, a lane change trigger time, and a lane change trigger range.
Specifically, the key elements include at least one of lane lines, road edges, pedestrians, obstacles and other vehicles on the lane of the actual road. The real scene data comprises at least one of multi-path camera data, laser radar data, millimeter wave radar data, real-time dynamics, inertial navigation measuring unit data and vehicle data.
In step S20, that is, in the step of generating the preliminary simulation scene according to the key elements of the real scene, the preliminary simulation scene is generated by using simulation software. The data structure form required by the unmanned simulation software is that traffic elements, vehicle control strategies, traffic flows, signal lamps, configuration information and the like are respectively stored in the extensible markup language. The vehicle control strategy is mainly used for storing vehicle related information, wherein the vehicle related information comprises vehicle parameter information and vehicle behavior information, and the vehicle information specifically comprises information such as driver information, vehicle type, vehicle name, vehicle starting position and vehicle starting speed information. The vehicle behavior information specifically includes a change in vehicle speed, a behavior of a vehicle lane change, a duration of the lane change, a time for triggering the lane change, a triggering range, and the like.
For example, a vehicle on the left side of the straight lane overtaking into the straight lane. The data processed by calculation is written into a vehicle control strategy according to a structural form required by unmanned simulation software, and is stored as a data file of extensible markup language. And importing the obtained data file into simulation software to generate a simulation scene. The saved extensible markup language data file is imported into simulation software, and then a simulation scene can be automatically generated.
In step S20, the key elements of the preliminary simulation scene are determined based on the simulation scene data,
and analyzing the generated preliminary simulation scene by adopting a perception model of a deep learning algorithm to obtain key elements of the preliminary simulation scene. And summarizing key elements of the primary simulation scene to obtain a scene summary, wherein the scene summary of the primary simulation scene is primary scene category data.
In step S30, that is, in the step of obtaining the preliminary scene category data according to the preliminary simulation scene key elements and the preset mapping relationship between the key elements and the scene category data, the proportional distribution display may be performed on all scenes in the preliminary simulation scene, so as to compare with the preset target scene category data, and analyze scenes that cannot be obtained in the actual scene obtaining process. When the key elements of the deep learning algorithm result are summarized, the type of each scene segment can be specifically analyzed. For example, the current vehicle is in the right lane of the two lanes, one vehicle with the same speed as the current vehicle exists in the front 15m, and one vehicle in the left lane passes through the left lane at a speed exceeding the speed of the current vehicle and cuts into the front of the current vehicle in the right lane and the back of the original vehicle. Analyzing all collected scenes, counting scene distribution, comparing defined scenes of the automatic driving function product to obtain scene types, summarizing and displaying comparison results.
In step S40, that is, comparing the preliminary scene category data with the preset target scene category data to obtain the lacking scene category data, and in the step of generating the supplementary simulation scene based on the lacking scene category data, the basic scene may be generalized for the simulation software to generate the supplementary simulation scenes in batch by using the software, so as to compensate the scenes that cannot be obtained by the real scene obtaining process, so as to complement the test scene corresponding to the automatic driving.
In step S50, that is, in the step of combining the preliminary simulation scenario and the supplemental simulation scenario to obtain the final simulation scenario, based on the preliminary simulation scenario, information such as the speed range of the vehicle, the speed range of another vehicle, and the behavior of another vehicle in the xml data file is modified to generalize the scenario, and then the scenario is saved as the xml data file. And importing the saved extensible markup language data file into simulation software, and automatically generating a final simulation scene.
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the generation method of the automobile driving simulation scene as described in any one of the above items when executing the computer program.
The present invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the method for generating a simulation scene of driving a vehicle as described in any one of the above.
Fig. 2 is a block diagram of a generation device of a car driving simulation scenario according to an exemplary embodiment of the present application. The apparatus may also be applied to other exemplary implementation environments, and is specifically configured in other devices, and the embodiment does not limit the implementation environment to which the apparatus is applied.
As shown in fig. 2, the exemplary traffic condition refreshing apparatus includes:
the real scene acquisition module acquires real scene data and determines real scene key elements based on the real scene data;
the scene category analysis module is used for generating a preliminary simulation scene according to the real scene key elements, determining the preliminary simulation scene key elements based on the simulation scene data, and obtaining preliminary scene category data according to the preliminary simulation scene key elements and a preset mapping relation between the key elements and the scene category data;
the scene generation module is used for comparing the preliminary scene category data with preset target scene category data to obtain lacking scene category data, generating a supplementary simulation scene based on the lacking scene category data, and combining the preliminary simulation scene and the supplementary simulation scene to obtain a final simulation scene.
It should be noted that the device for generating a driving simulation scene provided in the foregoing embodiment and the method for generating a driving simulation scene provided in the foregoing embodiment belong to the same concept, and specific manners in which each module and unit execute operations have been described in detail in the method embodiment, and are not described again here. In practical applications, the road condition refreshing apparatus provided in the above embodiment may distribute the above functions by different functional modules according to needs, that is, divide the internal structure of the apparatus into different functional modules to complete all or part of the above described functions, which is not limited herein.
An embodiment of the present application further provides an electronic device, including: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic equipment is enabled to realize the generation method of the vehicle driving simulation scene provided in the above embodiments.
FIG. 3 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application. It should be noted that the computer system 1200 of the electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the application scope of the embodiments of the present application.
As shown in fig. 3, the computer system 1200 includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data necessary for system operation are also stored. The CPU 1201, ROM 1202, and RAM 1203 are connected to each other by a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.
The following components are connected to the I/O interface 1205: an input portion 1206 including a keyboard, a mouse, and the like; an output section 1207 including a Display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a Network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1209, and/or installed from the removable medium 1211. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1201.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may comprise a propagated data signal with a computer-readable computer program embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
Another aspect of the present application also provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor of a computer, causes the computer to execute a generation method of a preceding vehicle driving simulation scene, the computer-readable storage medium may be included in the electronic device described in the above-described embodiments, or may exist separately without being assembled into the electronic device.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and executes the computer instructions, so that the computer device executes the generation method of the car driving simulation scene provided in the above embodiments.
In summary, in the generation method, the generation device, the computer, and the storage medium of the driving simulation scene of the vehicle according to the embodiments, the preliminary scene category data is determined based on the preliminary scene key elements, and the preliminary scene category data includes category information of each scene in the preliminary simulation scene. The target scene category data includes information of each scene category required in the automobile driving simulation, and the lacking scene category data can be obtained by comparing the preliminary scene category data with the target scene category data. The lacking scene type data comprises information of a scene lacking in the preliminary simulation scene compared with the required automobile driving simulation scene, so that a supplementary simulation scene can be generated according to the lacking scene type data. And combining the supplementary simulation scene and the preliminary simulation scene into a final simulation scene, wherein the scene type of the final simulation scene meets each scene type required by the automobile driving simulation. In the simulation scene generation process, the generation target is clear according to the lack of scene category data, the randomness of the generation of the simulation scene is reduced, and the generation efficiency is improved.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A generation method of an automobile driving simulation scene is characterized by comprising the following steps:
acquiring real scene data, and determining real scene key elements based on the real scene data;
generating a preliminary simulation scene according to the key elements of the real scene, and determining the key elements of the preliminary simulation scene based on the simulation scene data;
obtaining preliminary scene category data according to the preliminary simulation scene key elements and a preset mapping relation between the key elements and the scene category data;
comparing the preliminary scene category data with preset target scene category data to obtain lacking scene category data, and generating a supplementary simulation scene based on the lacking scene category data;
and obtaining a final simulation scene according to the preliminary simulation scene and the supplementary simulation scene.
2. The method for generating a simulation scenario for driving an automobile according to claim 1, wherein determining key elements of a real scenario based on the real scenario data comprises:
collecting automatic driving data of a real automobile, and determining the automatic driving data as training data;
establishing an analysis model, and training the analysis model by adopting the training data;
and analyzing the real scene data by adopting the trained analysis model to obtain the key elements of the real scene.
3. The method for generating the automobile driving simulation scene according to claim 2, wherein the automatic driving data of the real automobile comprises at least one of a live-action video stream, a live-action point cloud stream, a millimeter wave radar detection target, an automobile speed and an automobile steering angle.
4. The generation method of the automobile driving simulation scene according to claim 1, characterized in that:
the simulation scene comprises at least one of traffic elements, vehicle control strategies, traffic flows and configuration information;
the vehicle control strategy includes at least one of vehicle parameter information and vehicle behavior information.
5. The generation method of the automobile driving simulation scene according to claim 4, characterized in that:
the vehicle parameter information comprises at least one of driver information, vehicle type, vehicle name, vehicle starting position and vehicle starting speed;
the vehicle behavior information includes at least one of a vehicle speed change, a vehicle lane change behavior, a lane change duration, a lane change trigger time, and a lane change trigger range.
6. The generation method of the automobile driving simulation scene according to claim 1, characterized in that: the key elements include at least one of lane lines, curbs, pedestrians, obstacles, and other vehicles on the lane of the actual road.
7. The generation method of the automobile driving simulation scene according to claim 1, characterized in that: the real scene data comprises at least one of multi-path camera data, laser radar data, millimeter wave radar data, real-time dynamic data, inertial navigation measurement unit data and vehicle data.
8. An apparatus for generating a simulation scene of driving of a vehicle, comprising:
the real scene acquisition module acquires real scene data and determines real scene key elements based on the real scene data;
the scene category analysis module is used for generating a preliminary simulation scene according to the real scene key elements, determining the preliminary simulation scene key elements based on the simulation scene data, and obtaining preliminary scene category data according to the preliminary simulation scene key elements and a preset mapping relation between the key elements and the scene category data;
the scene generation module is used for comparing the preliminary scene category data with preset target scene category data to obtain lacking scene category data, generating a supplementary simulation scene based on the lacking scene category data, and combining the preliminary simulation scene and the supplementary simulation scene to obtain a final simulation scene.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method for generating a driving simulation scenario of a vehicle according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for generating a simulation scenario for driving a vehicle according to any one of claims 1 to 7.
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| CN202211059912.7A CN115422836A (en) | 2022-08-30 | 2022-08-30 | Method and device for generating automobile driving simulation scene, computer and storage medium |
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