CN115374016A - Test scene simulation system and method, electronic device and storage medium - Google Patents
Test scene simulation system and method, electronic device and storage medium Download PDFInfo
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Abstract
The invention discloses a test scene simulation system and method, electronic equipment and a storage medium. The system comprises: the setting module is used for responding to the setting operation and determining the area data of the test scene area; the automatic driving robot body is used for moving in a test scene area according to area data based on a preset high-precision map; the automatic driving robot comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring first initial data and first real-time data of an automatic driving robot body in a test scene area; the data analysis and transmission module is used for carrying out scene operation on the first initial data and the first real-time data and sending second initial data and second real-time data obtained by the scene operation to the control terminal; the control terminal is used for establishing a simulation test scene according to the second initial data and adjusting the simulation test scene in real time according to the second real-time data. By operating the technical scheme provided by the embodiment of the invention, the accuracy of acquiring the test scene data can be improved, and the accuracy of constructing the simulation test scene can be improved.
Description
Technical Field
The present invention relates to data acquisition technologies, and in particular, to a test scenario simulation system, a test scenario simulation method, an electronic device, and a storage medium.
Background
With the development of automobile driving technology, the test of various performances of the vehicle has important reference value for the evaluation of the vehicle performance, and the test result and the problems exposed in the test process can provide guidance for research and development and technical improvement.
At present, automatic driving simulation tests based on scenes are from research and development to testing, and data, interfaces and the like of all stages from a sensor to a functional module lack unified standards from a functional scene to a test case, so that the ground application of the simulation tests is hindered. The data collected by different data collecting devices are difficult to be compatible with different simulation test platforms. In addition, the closed scene test cannot completely restore the real use environment of the vehicle, and the open test scene can make the test result of the whole vehicle more accurate, but the test cost is relatively high, a lot of contingencies exist, and the test controllability is poor.
Disclosure of Invention
The invention provides a test scene simulation system, a test scene simulation method, electronic equipment and a storage medium, which are used for improving the accuracy and efficiency of data acquisition.
According to an aspect of the present invention, there is provided a test scenario simulation system, including: the robot comprises a setting module, an automatic driving robot body, an acquisition module, a data analysis and transmission module and a control terminal; the automatic driving robot comprises a setting module, an acquisition module, a data analysis and transmission module and a data analysis and transmission module, wherein the setting module, the acquisition module and the data analysis and transmission module are respectively connected with an automatic driving robot body; the data analysis transmission module is connected with the acquisition module; the control terminal is connected with the data analysis transmission module;
the setting module is used for responding to setting operation and determining area data of a test scene area; wherein the region data includes at least one of start point data, end point data, and path data;
the automatic driving robot body is used for moving in the test scene area according to the area data based on a preset high-precision map;
the acquisition module is used for acquiring first initial data and first real-time data of the automatic driving robot body in the test scene area;
the data analysis and transmission module is used for carrying out scene operation on the first initial data and the first real-time data and sending second initial data and second real-time data obtained by carrying out the scene operation to the control terminal; wherein the scene operation comprises at least one of a scene extraction operation and a scene labeling operation;
and the control terminal is used for establishing a simulation test scene according to the second initial data and adjusting the simulation test scene in real time according to the second real-time data.
Optionally, the collecting module includes:
at least one millimeter wave radar, and the setting position of millimeter wave radar includes at least one in autopilot body left side front, just preceding, left back, just back and the right back.
Optionally, the acquisition module includes:
the multi-angle camera device is used for acquiring current dynamic object data and current static object data in the test scene area;
the data analysis transmission module is further configured to: and determining target dynamic object data and target static object data in the simulation test scene based on the current dynamic object data and the current static object data.
Optionally, the acquisition module is further configured to:
and performing data association operation on the data acquired by the multi-angle camera device and/or the millimeter wave radar.
Optionally, the acquisition module further includes:
and the geographic information system is used for collecting geographic information data in the test scene area.
Optionally, the acquisition module further includes:
and the combined inertial navigation is used for acquiring positioning data.
Optionally, the acquisition module further includes:
and the road surface gradient sensor is used for acquiring the gradient data of the test scene area.
Optionally, the system further includes:
and the AI module is connected with the automatic driving robot body and used for generating a response instruction according to a preset emergency in the test scene area and controlling the automatic driving robot body to update the moving path according to the response instruction.
According to another aspect of the present invention, there is provided a test scenario simulation method, including:
in response to a setting operation, determining area data of a test scene area; wherein the region data includes at least one of start point data, end point data, and path data;
controlling the automatic driving robot body to move in the test scene area according to the area data;
acquiring first initial data and first real-time data of the automatic driving robot body in the test scene area;
performing scene operation on the first initial data and the first real-time data, and sending second initial data and second real-time data obtained by performing the scene operation to the control terminal; wherein the scene operation comprises at least one of a scene extraction operation and a scene labeling operation;
and the control terminal establishes a simulation test scene according to the second initial data and adjusts the simulation test scene in real time according to the second real-time data.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the test scenario simulation method of any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the test scenario simulation method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the automatic driving robot body moves in the test scene area according to the area data, the robot does not need to automatically find a way in the test scene area, the moving pertinence and efficiency of the robot are improved, and the automatic driving robot body moves based on the preset high-precision map. The acquisition module acquires first initial data and first real-time data of the automatic driving robot body in a test scene area, so that the data acquisition precision is improved, and meanwhile, a simulation test scene which is accurate and dynamically changes in real time along with a real scene can be conveniently established in a later period. The data acquired by the acquisition module is analyzed, classified and transmitted through the data analysis and transmission module so as to facilitate later modeling simulation, so that a simulation test scene model is accurate and light; the test scene simulation system can establish different simulation test scenes according to different collected data, is suitable for closed test scenes and open test scenes, and expands the application range of the simulation test scenes; and the simulation test scene can change along with the actual scene, so that the authenticity and reliability of the vehicle simulation test carried out through the simulation test scene subsequently are improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
Fig. 1 is a schematic diagram of a test scenario simulation system according to an embodiment of the present invention;
fig. 2 is a flowchart of a test scenario simulation method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," "target," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a schematic diagram of a test scenario simulation system 1 according to an embodiment of the present invention. Referring to fig. 1, the test scenario simulation system 1 provided in this embodiment includes: the automatic driving robot comprises a setting module 10, an automatic driving robot body 11, an acquisition module 12, a data analysis and transmission module 13 and a control terminal 14; the setting module 10, the acquisition module 12 and the data analysis and transmission module 13 are respectively connected with the automatic driving robot body 11, and the connection mode can be wired connection or wireless connection.
The data analysis transmission module 13 is connected with the acquisition module 12; the control terminal 14 is connected to the data analysis and transmission module 13, and the connection mode may be wired connection or wireless connection, which is not limited in this embodiment.
Wherein, the setting module 10 determines the area data of the test scene area in response to the setting operation; wherein the area data includes at least one of start point data, end point data, and path data.
The setting module 10 may be remotely connected to the robot body 11, for example, the setting module 10 may be located in a terminal remotely connected to the robot, and the terminal may be a terminal-side device such as a mobile phone, a Tablet Personal Computer (Tablet Personal Computer), a Laptop Computer (Laptop Computer), and accordingly, the setting operation may be sent out by operating the terminal.
The setting module 10 may also be disposed on the robot body 11, and accordingly, the setting operation may be sent out after operating an interactive device on the robot body, where the interactive device may be an interactive display screen or the like.
The test scene area is an actual scene area required for automobile test, the area data is data related to the test scene area, and the starting point data is data related to a starting point when the autonomous robot body 11 moves in the actual scene area, and may include a starting point position; the end point data is data related to an end point when the autonomous robot body 11 moves in an actual scene area, and may include an end point position and the like; the path data is data related to a moving path of the autonomous robot body 11 when moving in the actual scene area, and the moving path may cover the range of the test scene area in a comprehensive manner, and may include the number of paths, a path moving manner, such as uniform movement or variable movement, and a path moving direction.
The automatic driving robot body 11 is a body of the automatic driving robot which does not include an external module, and the automatic driving robot can autonomously move according to a preset path or a planned path.
The autonomous robot body 11 moves in the test scene area based on the area data based on a preset high-precision map, illustratively, a scene start point determined by the start point data to a scene end point determined by the end point data, and moves along a path determined by the path data during the movement.
The acquisition module 12 is configured to acquire first initial data and first real-time data of the autonomous driving robot body 11 in a test scene area, and may be acquisition equipment arranged on the autonomous driving robot body 11; the first initial data may be data obtained by the autonomous driving robot body 11 in a primary moving process in the test scene area, and the first real-time data may be data collected by the autonomous driving robot body 11 in another moving process performed after the primary moving process in the test scene area.
The data analysis and transmission module 13 is configured to perform scene operations on the first initial data and the first real-time data, and may be a data analysis and transmission device that is disposed on the robot body 11 and is connected to the acquisition module 12 in a wired or wireless manner, or may be a data analysis and transmission device that is remotely connected to the acquisition module 12. Wherein the scene operation is to perform an operation related to the test scene area on the first initial data and the first real-time data. For example, a scene extraction operation and/or a scene labeling operation, where the scene extraction operation is to extract scene data related to a test scene area in the first initial data and the first real-time data for analyzing the data; the scene labeling operation is an operation of labeling the scene data, for example, adding a label to the scene data to classify the data, which is not limited in this embodiment.
And performing scene operation on the first initial data to obtain second initial data, performing scene operation on the first real-time data to obtain second real-time data, and sending the second initial data and the second real-time data to the control terminal 14 by the data analysis and transmission module 13.
The control terminal 14 may be remotely connected to the data analysis and transmission module 13, and the control terminal 14 is configured to establish a simulation test scenario according to the second initial data, that is, an initial simulation test scenario is established according to the second initial data, where the method for establishing the initial simulation test scenario may adopt the prior art, the simulation test scenario is a simulation of a test scenario area, and the initial simulation test scenario is an initial version of the simulation test scenario. And (3) adjusting the simulation test scene in real time according to the second real-time data, namely adjusting the initial simulation test scene in real time according to the second real-time data, for example, adjusting a part of the initial simulation test scene, which is different from the second real-time data, if the first item is located at the position A and the second real-time data contains the first item located at the position B, adjusting the initial simulation test scene according to the second real-time data, and updating the position of the first item to the position B, so that the initial simulation test scene is closer to the actual test scene area.
According to the technical scheme provided by the embodiment, the automatic driving robot body 11 moves in the test scene area according to the area data, the robot is not required to automatically find a way in the test scene area, the moving pertinence and efficiency of the robot are improved, and the robot moves based on the preset high-precision map, compared with the traditional navigation mode, the automatic driving robot body 11 can move in the test scene area more accurately and intelligently, so that the data of all places in the test scene area can be acquired more accurately.
The acquisition module 12 acquires first initial data and first real-time data of the automatic driving robot body 11 in a test scene area, so that the data acquisition precision is improved, and meanwhile, a simulation test scene which is accurate and dynamically changes in real time along with a real scene is conveniently established in a later stage.
The data acquired by the acquisition module 12 is analyzed, classified and transmitted through the data analysis and transmission module 13 so as to facilitate later-stage modeling simulation, so that a simulation test scene model is accurate and light; the test scenario simulation system 1 can establish different simulation test scenarios according to different collected first initial data, illustratively, if the first initial data of a closed test scenario is collected, a simulation test scenario of the closed test scenario can be established, and if the first initial data of an open test scenario is collected, a simulation test scenario of the open test scenario can be established; when the collected first initial data is different, the same prior art may be used for different first initial data to establish the corresponding simulation test scenario, or different prior art may be used for different first initial data to establish the corresponding simulation test scenario, which is not limited in this embodiment; the method is suitable for both closed test scenes and open test scenes, and the application range of simulation test scenes is expanded; and the simulation test scene can change along with the actual scene, so that the authenticity and reliability of the vehicle simulation test carried out through the simulation test scene subsequently are improved.
In this embodiment, optionally, the acquisition module 12 includes:
at least one millimeter wave radar, and the setting position of millimeter wave radar includes at least one in the front left of autopilot body 11, just preceding, left back, just back and the right back.
The millimeter wave radar can be used for collecting point cloud data of objects in a test scene area, and optionally, the detection distance range of the millimeter wave radar is 0 to 120m, the short-distance detection angle is 120 degrees, the long-distance detection angle is 18 degrees, and the embodiment is not limited to this.
Because the millimeter wave radar has the advantages of being capable of distinguishing and identifying small targets, being capable of identifying a plurality of targets simultaneously and the like, the accuracy of the first initial data and the first real-time data acquisition is improved. And the millimeter wave radar is arranged on at least one position of the left front, the right front, the left back, the right back and the right back of the automatic driving robot body 11, so that data can be acquired from different angles, the range of data acquisition is expanded, and the comprehensiveness of data acquisition is improved.
In this embodiment, optionally, the acquisition module 12 includes:
the system comprises at least one multi-angle camera device, a plurality of image acquisition devices and a plurality of image processing devices, wherein the multi-angle camera device is used for acquiring current dynamic object data and current static object data in a test scene area;
the data parsing and transmitting module 13 is further configured to: and determining target dynamic object data and target static object data in the simulation test scene based on the current dynamic object data and the current static object data.
The multi-angle camera device is a ray device capable of collecting multiple angles, for example, a camera capable of changing a shooting angle, and this embodiment is not limited thereto. Optionally, the multi-angle image capturing device has a resolution of 1280 × 720, the field angle includes at least one of 52 °, 112 °, and 192 °, and the detection distance includes at least one of 120m, 60m, and 7 m.
The multi-angle camera device is used for acquiring current dynamic object data and current static object data in a test scene area, wherein the current dynamic object is a dynamic object such as a moving vehicle and a pedestrian when the multi-angle camera device acquires the current dynamic object, and the dynamic object data is data acquired from the dynamic object and can comprise the speed, the acceleration, the moving track and the like of the dynamic object.
Optionally, dynamic object data in the simulation test scene may be described based on a standard simulation test case format in an XML format, so that different simulation test platforms can be compatible. Complex movements involving multiple traffic participants are described, including vehicular pedestrian, etc., pedestrian traffic, and the natural environment, among others.
The static object is in static object when current static object is gathered for multi-angle camera device, for example for traffic lights, vegetation, building, street lamp, rail and median, and static object data is the data of gathering from static object, can include the interval time of waiting for the traffic lights, the distance between the adjacent traffic lights, the quantity and the density of vegetation, street lamp quantity and the interval of adjacent street lamp, setting position and the shape of rail and median etc..
The multi-angle camera device is used for collecting effective static object data and dynamic object data, and the types of data collection are enriched, so that a real test scene area can be better restored, wherein static objects such as traffic lights and the like, and information of dynamic objects such as pedestrians and vehicles and the like are relatively important influence factors of a test result.
Target dynamic object data and target static object data in the simulation test scene are determined based on the current dynamic object data and the current static object data. The target dynamic object data and the target static object data may be future dynamic object data and future static object data in the simulation test scene.
The target dynamic object data and the target static object data may be obtained by inputting the current dynamic object data and the current static object data into a machine learning model trained in advance and then calculating, which is not limited in this embodiment.
The data analysis and transmission module 13 deduces target dynamic object data and target static object data based on the current dynamic object data and the current static object data, so that a future simulation test scene can be simulated under the condition that the acquisition module 12 stops acquiring real-time data, the problem that the simulation test scene cannot be updated in time due to data transmission delay or interruption is avoided, and the smooth operation of the simulation test is ensured.
In this embodiment, optionally, the acquisition module 12 is further configured to:
and performing data association operation on data acquired by the multi-angle camera device and/or the millimeter wave radar.
It should be noted that the data acquired by the multi-angle camera device and/or the millimeter wave radar for the first time may be first initial data, and the data acquired by the multi-angle camera device and/or the millimeter wave radar in real time in the subsequent process may be first real-time data.
The data association operation may be to splice or fuse data acquired at the same time. The data analysis and transmission module 13 performs data association on the data collected by the multi-angle camera device and/or the millimeter wave radar, and may include at least one of data association acquired by a plurality of multi-angle camera devices, data association acquired by a plurality of millimeter wave radars, and data association acquired by a multi-angle camera device and a millimeter wave radar.
When data collected by a plurality of multi-angle camera devices are correlated or data collected by a plurality of millimeter wave radars are correlated, collected data at different positions in the same collection time can be spliced, and data of the overlapped part is screened out to obtain integral data of a test scene area; the data of different acquisition objects can be correlated, and the movement tracks of a plurality of pedestrians acquired by the multi-angle camera device can be correlated exemplarily. Therefore, the effectiveness of the final acquisition of the data is improved on the basis of improving the comprehensiveness of data acquisition, and the accuracy of a simulation test scene established according to the data is improved conveniently.
The data that multi-angle camera device and millimeter wave radar gathered are correlated, can be for confirming the timestamp that multi-angle camera device and millimeter wave radar's data collection correspond to correlate the data of same timestamp, for example can fuse etc. through the kalman filter, this embodiment does not restrict this to richen the information quantity of data collection, be convenient for improve the follow-up accuracy according to the emulation test scene that this data set up.
In this embodiment, optionally, the acquisition module 12 further includes:
and the geographic information system is used for collecting geographic information data in the test scene area.
A Geographic Information System (GIS) collects geographic information data in a test scene area, for example, collects geographic data of the earth's surface including various geographic data in the atmosphere, so that various geographic information data are brought into consideration of a simulation test scene for a vehicle, the real driving environment of the vehicle is restored more truly, and more evaluation dimensions are added to the judgment of various performance indexes of the vehicle.
In this embodiment, optionally, the acquisition module 12 further includes:
and the combined inertial navigation is used for acquiring positioning data.
The combined inertial navigation is a total inertial navigation system formed by combining at least two units or systems with positioning functions, for example, the combined inertial navigation system can be formed by combining an inertial measurement unit and a global positioning system, which is not limited in this embodiment, and the accuracy of positioning data acquisition is improved.
The combined inertial navigation positioning accuracy may be dgps0.4m or RTK1cm, which is not limited in this embodiment. The autonomous driving robot body 11 can move more accurately in the test scene area, thereby improving the effectiveness of data acquisition.
In this embodiment, optionally, the acquisition module 12 further includes:
and the road surface gradient sensor is used for acquiring gradient data of the test scene area.
The road surface gradient sensor is used for measuring gradient data of a test scene area, such as a gradient value. Therefore, the slope data can be conveniently brought into a consideration range aiming at a simulation test scene of the vehicle, the real driving environment of the vehicle can be more truly restored, and the accuracy of tests such as vehicle climbing capability or off-road performance is improved.
In this embodiment, optionally, the method further includes:
and the AI module is connected with the automatic driving robot body 11 and is used for generating a response instruction according to a preset emergency in the test scene area and controlling the automatic driving robot body 11 to update the moving path according to the response instruction.
The AI module makes a response instruction when the autonomous driving robot body 11 encounters a preset emergency in the process of moving in the test scene area, and controls the autonomous driving robot body 11 to change the original moving path.
That is, the autonomous driving robot body 11 is provided with a self-protection mechanism, and since there is a certain danger in the road in the real test scene area, various processing modes of emergency events are implanted in the AI module in advance, where the preset emergency event may be an event that a collision is about to occur or an abnormal road condition event, and the like, and this embodiment does not limit this.
The handling instruction is generated according to the preset emergency in the test scene area, for example, when the AI module senses that the robot body is about to be collided or recognizes an abnormal road condition, the AI module may make a corresponding instruction, for example, change a traveling path of the robot body 11, allow the robot body to travel to a safe place, and send out an alarm, etc., thereby preventing the robot body 11 from being damaged, improving the safety of data acquisition, and reducing the consumption of human resources without manual operation.
Optionally, the data analysis transmission module 13 includes an i-Tester host, and the i-Tester host transmits data through the CAN data and supports at least 8-channel CAN data acquisition. The efficiency of data acquisition is improved and the data volume of data acquisition is enriched.
Example two
Fig. 2 is a flowchart of a test scenario simulation method according to a second embodiment of the present invention, where this embodiment is applicable to data acquisition in a test scenario area and a simulation test scenario is established according to the acquired data, and the method may be executed by the test scenario simulation system according to the first embodiment of the present invention, and the apparatus may be implemented in a software and/or hardware manner. Referring to fig. 2, the test scenario simulation method provided in this embodiment includes:
step 210, responding to the setting operation, determining the area data of the test scene area; wherein the area data includes at least one of start point data, end point data, and path data.
And step 220, controlling the automatic driving robot body to move in the test scene area according to the area data.
Step 230, performing scene operation on the first initial data and the first real-time data, and sending second initial data and second real-time data obtained by performing the scene operation to the control terminal; wherein the scene operation comprises at least one of a scene extraction operation and a scene labeling operation.
And step 240, the control terminal establishes a simulation test scene according to the second initial data and adjusts the simulation test scene in real time according to the second real-time data.
Firstly, determining the starting point, the end point and the path data of a test scene, and then controlling the automatic driving robot body to collect the data of the test scene area along a set path according to the high-precision map information. And the collected first initial data is subjected to scene operation to obtain second initial data, the second initial data is transmitted to the control terminal, and the control terminal establishes a simulation test scene. In addition, the first real-time data are collected, and the second real-time data obtained by performing scene operation are transmitted to the control terminal to adjust the simulation test scene in real time, so that the accuracy of acquiring the test scene data is improved, and the accuracy of constructing the simulation test scene is improved.
EXAMPLE III
FIG. 3 shows a schematic block diagram of an electronic device 30 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 30 includes at least one processor 31, and a memory communicatively connected to the at least one processor 31, such as a Read Only Memory (ROM) 32, a Random Access Memory (RAM) 33, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 31 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 32 or the computer program loaded from the storage unit 38 into the Random Access Memory (RAM) 33. In the RAM 33, various programs and data necessary for the operation of the electronic apparatus 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other via a bus 34. An input/output (I/O) interface 35 is also connected to bus 34.
A plurality of components in the electronic device 30 are connected to the I/O interface 35, including: an input unit 36 such as a keyboard, a mouse, etc.; an output unit 37 such as various types of displays, speakers, and the like; a storage unit 38 such as a magnetic disk, an optical disk, or the like; and a communication unit 39 such as a network card, modem, wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 31 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 31 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 31 performs the various methods and processes described above, such as the test scenario simulation method.
In some embodiments, the test scenario simulation method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 38. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 30 via the ROM 32 and/or the communication unit 39. When the computer program is loaded into the RAM 33 and executed by the processor 31, one or more steps of the test scenario simulation method described above may be performed. Alternatively, in other embodiments, the processor 31 may be configured to perform the test scenario simulation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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 or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the Internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A test scenario simulation system, comprising: the system comprises a setting module, an automatic driving robot body, an acquisition module, a data analysis and transmission module and a control terminal; the automatic driving robot comprises a setting module, an acquisition module, a data analysis and transmission module and a data analysis and transmission module, wherein the setting module, the acquisition module and the data analysis and transmission module are respectively connected with an automatic driving robot body; the data analysis transmission module is connected with the acquisition module; the control terminal is connected with the data analysis transmission module;
the setting module is used for responding to setting operation and determining area data of the test scene area; wherein the region data includes at least one of start point data, end point data, and path data;
the automatic driving robot body is used for moving in the test scene area according to the area data based on a preset high-precision map;
the acquisition module is used for acquiring first initial data and first real-time data of the automatic driving robot body in the test scene area;
the data analysis and transmission module is used for carrying out scene operation on the first initial data and the first real-time data and sending second initial data and second real-time data obtained by carrying out the scene operation to the control terminal; wherein the scene operation comprises at least one of a scene extraction operation and a scene labeling operation;
the control terminal is used for establishing a simulation test scene according to the second initial data and adjusting the simulation test scene in real time according to the second real-time data;
the acquisition module comprises:
the multi-angle camera device is used for acquiring current dynamic object data and current static object data in the test scene area;
the data analysis transmission module is further configured to: and determining target dynamic object data and target static object data in the simulation test scene based on the current dynamic object data and the current static object data.
2. The system of claim 1, wherein the acquisition module comprises:
at least one millimeter wave radar, just the millimeter wave radar set up the position and include automatic pilot robot body at least one in left front, just preceding, left back, just back and the right back.
3. The system of claim 2, wherein the acquisition module is further configured to:
and carrying out data association operation on the data acquired by the multi-angle camera device and/or the millimeter wave radar.
4. The system of claim 1, wherein the acquisition module further comprises:
and the geographic information system is used for collecting geographic information data in the test scene area.
5. The system of claim 1, wherein the acquisition module further comprises:
and the combined inertial navigation is used for acquiring positioning data.
6. The system of claim 1, wherein the acquisition module further comprises:
and the road surface gradient sensor is used for acquiring the gradient data of the test scene area.
7. The system of claim 1, further comprising:
and the AI module is connected with the automatic driving robot body and used for generating a response instruction according to a preset emergency in the test scene area and controlling the automatic driving robot body to update the moving path according to the response instruction.
8. A test scenario simulation method is characterized by comprising the following steps:
in response to a setting operation, determining area data of a test scene area; wherein the region data includes at least one of start point data, end point data, and path data;
controlling the automatic driving robot body to move in the test scene area according to the area data;
acquiring first initial data and first real-time data of the automatic driving robot body in the test scene area;
performing scene operation on the first initial data and the first real-time data, and sending second initial data and second real-time data obtained by performing the scene operation to a control terminal; wherein the scene operation comprises at least one of a scene extraction operation and a scene labeling operation;
and the control terminal establishes a simulation test scene according to the second initial data and adjusts the simulation test scene in real time according to the second real-time data.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the test scenario simulation method of claim 8.
10. A computer-readable storage medium storing computer instructions for causing a processor to implement the test scenario simulation method of claim 8 when executed.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117670184A (en) * | 2024-01-31 | 2024-03-08 | 埃罗德智能科技(辽宁)有限公司 | Robot scene simulation method and system applied to digital robot industrial chain |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114067062A (en) * | 2022-01-17 | 2022-02-18 | 深圳慧拓无限科技有限公司 | Method and system for simulating real driving scene, electronic equipment and storage medium |
| WO2022141506A1 (en) * | 2020-12-31 | 2022-07-07 | 华为技术有限公司 | Method for constructing simulation scene, simulation method and device |
| CN115187742A (en) * | 2022-09-07 | 2022-10-14 | 西安深信科创信息技术有限公司 | Method, system and related device for generating automatic driving simulation test scene |
-
2022
- 2022-10-25 CN CN202211306405.9A patent/CN115374016A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022141506A1 (en) * | 2020-12-31 | 2022-07-07 | 华为技术有限公司 | Method for constructing simulation scene, simulation method and device |
| CN114067062A (en) * | 2022-01-17 | 2022-02-18 | 深圳慧拓无限科技有限公司 | Method and system for simulating real driving scene, electronic equipment and storage medium |
| CN115187742A (en) * | 2022-09-07 | 2022-10-14 | 西安深信科创信息技术有限公司 | Method, system and related device for generating automatic driving simulation test scene |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117670184A (en) * | 2024-01-31 | 2024-03-08 | 埃罗德智能科技(辽宁)有限公司 | Robot scene simulation method and system applied to digital robot industrial chain |
| CN117670184B (en) * | 2024-01-31 | 2024-05-03 | 埃罗德智能科技(辽宁)有限公司 | Robot scene simulation method and system applied to digital robot industrial chain |
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