Automatic calibration method, device and equipment for ultrasonic radar echo simulator model

Abstract

The invention provides an automatic calibration method, device and equipment for an ultrasonic radar echo simulator model, and the method comprises the steps: carrying out the radar detection distance test of an uncalibrated ultrasonic radar echo simulator in a simulation test environment, and generating test data, the test data comprises a radar input distance value and an actual output distance value; processing the test data, extracting target data and converting the target data into a preset format; calculating calibration parameters of the ultrasonic radar echo simulator model based on the target data; and configuring the calibration parameters into the ultrasonic radar echo simulator model, and verifying an output error of the model until the error is within a preset range. By constructing the whole process of automatic calibration, the limitation of manual calibration is eliminated, full-process automation from test data generation to error verification is realized, a foundation is laid for implementation of subsequent steps, and the efficiency of calibration work is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to an automatic calibration method, apparatus, and equipment for an ultrasonic radar echo simulator model. Background Technology

[0002] In existing technologies, the calibration of ultrasonic radars is mostly done manually, especially in simulation testing environments. Vehicles are typically equipped with multiple ultrasonic radars, each requiring calibration for different distances and configuration within the corresponding model. This results in a large amount of calibration data, a heavy workload, and low efficiency. Furthermore, existing real-vehicle calibration methods cannot adapt to the calibration requirements of simulator models in simulation environments, making it difficult to solve the problem of inaccurate radar detection distances in simulations, severely impacting the efficiency and accuracy of simulation testing.

[0003] To address the aforementioned issues, this invention proposes an automatic calibration method for an ultrasonic radar echo simulator model, thereby automating the calibration process and improving calibration efficiency and accuracy. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes an automatic calibration method, apparatus, device and computer-readable medium for ultrasonic radar echo simulator models.

[0005] In a first aspect, embodiments of the present invention provide an automatic calibration method for an ultrasonic radar echo simulator model, the method comprising:

[0006] In a simulation test environment, the radar detection range of an uncalibrated ultrasonic radar echo simulator is tested, and test data is generated, which includes the radar input distance value and the actual output distance value.

[0007] The test data is processed to extract the target data and convert it into a preset format;

[0008] The calibration parameters of the ultrasonic radar echo simulator model are calculated based on the target data;

[0009] Configure the calibration parameters into the ultrasonic radar echo simulator model and verify the model output error until the error is within the preset range.

[0010] In some embodiments, calculating the calibration parameters of the ultrasonic radar echo simulator model based on the target data includes:

[0011] If the difference between the radar input distance value and the actual output distance value is not greater than the preset error threshold, then the calibration parameter is 0;

[0012] If the difference between the radar input distance value and the actual output distance value is greater than a preset error threshold, then the calibration parameters are calculated according to Formula 1:

[0013] i jn =A jn (1-k(B) jn –A jn )) Formula 1

[0014] Where A jn B is the input range value for radar j. jn denoted as the actual output distance value of radar j, k is a preset coefficient, and n represents the number of calibrations.

[0015] In some embodiments, configuring the calibration parameters into the ultrasonic radar echo simulator model includes:

[0016] The calibration parameters are stored in CSV format in the model directory, imported into the Matlab workspace, and then associated with the model's calibration module.

[0017] After completing the configuration in the calibration module, the ultrasonic radar echo simulator is compiled.

[0018] In some embodiments, the verification model outputs an error until the error is within a preset range, including:

[0019] Under the same simulation test environment, the ultrasonic radar echo simulator with the calibration parameters configured and compiled was tested again to obtain a new actual output distance value.

[0020] If the difference between the new actual output distance value and the radar input distance value is not greater than the preset error threshold, it means that the error is within the preset range.

[0021] In some embodiments, the verification model output error, until the error is within a preset range, further includes:

[0022] If the difference between the new actual output distance value and the radar input distance value is greater than a preset error threshold, then the new actual output distance value is updated in the target data;

[0023] The calibration parameters of the ultrasonic radar echo simulator model based on the target data are returned to be calibrated again, and the current calibration parameters are recalculated.

[0024] When calibration is repeated multiple times, the final calibration parameter is the sum of the previous calibration parameter and the current calibration parameter.

[0025] In some embodiments, the simulation test environment is a Dspace-Simulink co-simulation model.

[0026] The generated test data includes:

[0027] The radar detection range test is performed using Dspace automated testing software, and the test data is output in the form of an HTML test report.

[0028] In some embodiments, processing the test data, extracting target data, and converting it into a preset format includes:

[0029] The test report in HTML format was parsed using Python to extract the radar input distance value and the actual output distance value.

[0030] Convert the data into a CSV file according to a preset table structure, and store the CSV file in the ultrasonic radar echo simulator model directory.

[0031] Secondly, embodiments of the present invention provide an automatic calibration device for an ultrasonic radar echo simulator model, the device comprising:

[0032] The test module is configured to perform radar detection range tests on an uncalibrated ultrasonic radar echo simulator in a simulation test environment and generate test data, which includes radar input distance values ​​and actual output distance values.

[0033] The extraction module is configured to process the test data, extract the target data, and convert it into a preset format;

[0034] The calibration module is configured to calculate the calibration parameters of the ultrasonic radar echo simulator model based on the target data;

[0035] The verification module is configured to incorporate the calibration parameters into the ultrasonic radar echo simulator model and verify the model output error until the error is within a preset range.

[0036] Thirdly, embodiments of the present invention provide an electronic device, comprising:

[0037] One or more processors;

[0038] Memory, used to store one or more programs;

[0039] When one or more programs are executed by one or more processors, the one or more processors implement the automatic calibration method for the ultrasonic radar echo simulator model provided in the first aspect.

[0040] Fourthly, embodiments of the present invention provide a computer-readable medium storing a computer program, which, when executed by a processor, implements the automatic calibration method for the ultrasonic radar echo simulator model provided in the first aspect.

[0041] In the automatic calibration method for the ultrasonic radar echo simulator model provided in this embodiment, test data is first generated in the simulation test environment. This is the foundation for obtaining calibration data. Only by obtaining the input and output distance values ​​through testing can subsequent parameter calculations and calibration be performed. Secondly, the test data is processed and its format is converted to extract effective information, transforming complex data into a form that is easy to calculate and process, ensuring the smooth progress of subsequent steps. Then, calibration parameters are calculated, which is also the core step of the calibration process. The accuracy of the radar output is achieved through parameter adjustment. Finally, parameters are configured and errors are verified, which is crucial to ensuring the calibration effect. Through iterative verification until the error reaches the target, the accuracy of the simulator is guaranteed. By constructing the above-mentioned automatic calibration process, the limitations of manual calibration are overcome, and the entire process from test data generation to error verification is automated, laying the foundation for the implementation of subsequent steps and significantly improving the efficiency of calibration work. Attached Figure Description

[0042] Figure 1 A flowchart illustrating an automatic calibration method for an ultrasonic radar echo simulator model provided in an embodiment of the present invention;

[0043] Figure 2 A comparison chart of the input distance value and actual output value of the front ultrasonic radar provided in this embodiment of the disclosure;

[0044] Figure 3 A comparison chart of the input distance value and the actual output value of the ultrasonic radar provided in this embodiment of the disclosure;

[0045] Figure 4 A schematic diagram of the structure of an automatic calibration device for an ultrasonic radar echo simulator model provided in an embodiment of this disclosure;

[0046] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0047] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0048] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0049] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0050] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0051] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0052] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0053] In the field of intelligent driving, simulation testing is a key step in promoting the implementation of technology. It can simulate various traffic scenarios and road conditions in a virtual environment, efficiently evaluating the performance of autonomous driving systems. Ultrasonic echo simulators play an important role in the simulation testing of automatic parking functions.

[0054] In existing simulation tests, a single vehicle is usually equipped with 12 ultrasonic radars, and the detection range of each radar is limited to 500cm. Since different radars and different distances need to be calibrated separately, the amount of data and workload in the traditional manual calibration mode increases exponentially, further highlighting the adaptability and necessity of automatic calibration methods for multi-radar scenarios.

[0055] Furthermore, existing technologies focus on the manual calibration of physical radar in real vehicles, achieved by adjusting hardware circuits (magnification factor, sensor threshold); while this invention addresses the error correction of ultrasonic radar models in virtual simulation environments, with the core being the automatic processing of simulation data and configuration of model parameters through software algorithms. The application scenarios (real vehicle vs. simulation) and implementation methods (hardware adjustment vs. software algorithm) of the two are fundamentally different, and existing technologies cannot solve the problem of automated calibration of simulation models.

[0056] In summary, current calibration of ultrasonic radars is mostly done manually, especially in simulation testing environments. Vehicles are typically equipped with multiple ultrasonic radars, each requiring calibration for different distances and configuration within the corresponding model. This results in a large amount of calibration data, a heavy workload, and low efficiency. Furthermore, existing real-vehicle calibration methods cannot adapt to the calibration requirements of simulator models in simulation environments, making it difficult to solve the problem of inaccurate radar detection distances in simulations, severely impacting the efficiency and accuracy of simulation testing.

[0057] To address at least one or more of the aforementioned technical issues, this disclosure provides an automatic calibration method for an ultrasonic radar echo simulator model, which automates the calibration process and improves calibration efficiency and accuracy.

[0058] Figure 1 This is a flowchart illustrating an automatic calibration method for an ultrasonic radar echo simulator model provided in an embodiment of the present invention. Figure 1 As shown, the above calibration method includes steps S1-S4:

[0059] Step S1: Perform radar detection range test on the uncalibrated ultrasonic radar echo simulator in the simulation test environment, and generate test data. The test data includes the radar input distance value and the actual output distance value.

[0060] Step S2: Process the test data, extract the target data and convert it into a preset format.

[0061] Step S3: Calculate the calibration parameters of the ultrasonic radar echo simulator model based on the target data.

[0062] Step S4: Configure the calibration parameters into the ultrasonic radar echo simulator model and verify the model output error until the error is within the preset range.

[0063] In the automatic calibration method for the ultrasonic radar echo simulator model provided in this embodiment, test data is first generated in the simulation test environment. This is the foundation for obtaining calibration data. Only by obtaining the input and output distance values ​​through testing can subsequent parameter calculations and calibration be performed. Secondly, the test data is processed and its format is converted to extract effective information, transforming complex data into a form that is easy to calculate and process, ensuring the smooth progress of subsequent steps. Then, calibration parameters are calculated, which is also the core step of the calibration process. The accuracy of the radar output is achieved through parameter adjustment. Finally, parameters are configured and errors are verified, which is crucial to ensuring the calibration effect. Through iterative verification until the error reaches the target, the accuracy of the simulator is guaranteed. By constructing the above-mentioned automatic calibration process, the limitations of manual calibration are overcome, and the entire process from test data generation to error verification is automated, laying the foundation for the implementation of subsequent steps and significantly improving the efficiency of calibration work.

[0064] It should be noted that the above simulation test environment is a Dspace-Simulink co-simulation model. Specifically, the working process of the Dspace-Simulink co-simulation model is as follows: Dspace software is responsible for building the hardware-in-the-loop test environment and simulating the interaction between real vehicle hardware and virtual scene; Matlab-Simulink is responsible for building the mathematical model of ultrasonic radar echo simulator. The two communicate in real time through data interface to ensure the accurate transmission of test data (input / output distance values) between virtual model and hardware simulation environment, providing a real simulation test basis for automatic calibration.

[0065] In some embodiments, generating test data in step S1 above may include: performing a radar detection range test using Dspace automated testing software, and outputting the test data in the form of a test report in HTML format.

[0066] By defining the simulation environment and data output format, the standardization of the testing process and the consistency of the data are ensured, providing a unified processing object for subsequent data processing steps and laying the foundation for automated data extraction and processing. At the same time, standardizing the testing environment and data format improves the operability and versatility of the method, facilitating its widespread application within the industry.

[0067] In some embodiments, step S2 above may include:

[0068] The test report in HTML format is parsed using Python to extract the radar input distance value and the actual output distance value; it is then converted into a CSV file according to a preset table structure and stored in the ultrasonic radar echo simulator model directory.

[0069] In one example, the above-mentioned preset table structure is a two-dimensional table with radar numbers stored in columns, as shown in Table 1. Each row corresponds to a set of test data, and the columns are the input distance values ​​of each radar (such as the input distance value of radar 1 and the input distance value of radar j) and the corresponding actual output distance values ​​(such as the output distance value of radar 1 and the output distance value of radar j), ensuring that the test data of different radars can be extracted and calculated independently, adapting to the requirement of simultaneous calibration of multiple radars.

[0070] Table 1 Test data storage format

[0071] Radar No. 1 input range value Radar No. 1 output range value … Radar input range value Radar J output range value <![CDATA[A 11 ]]> <![CDATA[B 11 ]]> … <![CDATA[A j1 ]]> <![CDATA[B j1 ]]> <![CDATA[A 12 ]]> <![CDATA[B 12 ]]> … <![CDATA[A j2 ]]> <![CDATA[B j2 ]]> … … … … … <![CDATA[A 1n ]]> <![CDATA[B 1n ]]> … <![CDATA[A jn ]]> <![CDATA[B jn ]]>

[0072] During the calibration process, the original test data format was complex and difficult to use directly. Therefore, step S2 used specific tools and format conversion to extract key target data, providing a clear and standardized data foundation for subsequent parameter calculations. Specifically, Python was used to parse the HTML report and convert it to CSV format, automating data processing, improving the efficiency of data extraction and conversion, reducing the error rate of manual data processing, and ensuring the accuracy of the target data.

[0073] In some embodiments, step S2 involves calling a Matlab script through the interaction between Matlab and Python to acquire and convert the test data.

[0074] Matlab possesses powerful engineering computing capabilities in building intelligent driving simulation models and calculating parameters, while Python offers greater flexibility in parsing unstructured data (such as HTML reports). By interactively calling Matlab scripts, Python's efficiency in parsing complex test data can be leveraged, while Matlab's compatibility with data formats ensures that the processed target data (such as CSV files) can be directly adapted to subsequent model parameter calculations. This reduces data loss or errors during format conversion, significantly improving the smoothness and accuracy of data processing. Furthermore, automated script execution eliminates the need for manual data transfer between the two tools, further reducing human error and enhancing the automation of data processing.

[0075] In some embodiments, step S3 above may include:

[0076] If the difference between the radar input range value and the actual output range value is not greater than the preset error threshold, the calibration parameter is 0; if the difference between the radar input range value and the actual output range value is greater than the preset error threshold, the calibration parameter is calculated according to Formula 1:

[0077] ijn=Ajn(1-k(Bjn–Ajn)) Formula 1

[0078] Where Ajn is the input range value of radar j, Bjn is the actual output range value of radar j, k is the preset coefficient, and n represents the number of calibrations.

[0079] The preset error threshold can be 1 cm, meaning the preset range in step S1 is such that the absolute value of the difference between the radar input distance value and the actual output distance value is no greater than 1. The preset system value k can be 0.5. It should be noted that both the preset error threshold and the preset system value k can be flexibly set based on empirical values, and this embodiment does not limit their settings.

[0080] The above calculation process clarifies the calculation rules for calibration parameters, providing a basis for automated calculations. Adopting different processing methods based on the magnitude of the error ensures both high efficiency when the error is acceptable and the rationality of the parameters through scientific formula calculation when the error is large. This is the core link in achieving accurate calibration. Specifically, setting a preset error threshold distinguishes whether to calculate calibration parameters, avoiding unnecessary parameter adjustments. When the error is small, calibration is directly determined not to be needed, reducing the amount of calculation; for larger errors, parameters are calculated using specific formulas, which can accurately correct the error and improve the targeting and accuracy of the calibration.

[0081] In some embodiments, configuring the calibration parameters into the ultrasonic radar echo simulator model in step S4 above may include:

[0082] Store the calibration parameters in CSV format in the model directory, import them into the Matlab workspace, and then associate them with the model's calibration module. After completing the configuration in the calibration module, compile the ultrasonic radar echo simulator model.

[0083] Understandably, parameter configuration is a crucial step in applying the calculated calibration parameters to the actual model. Only through correct storage, association, and compilation can the parameters function effectively in the model and correct the simulator output. Specifically, storing the calibration parameters in CSV format and associating them with the model's calibration module facilitates data storage, retrieval, and model invocation, ensuring that the parameters are accurately applied to the model. The model compilation step guarantees the effectiveness of the parameter configuration, enabling the adjusted model to run normally and improving the reliability of the calibration.

[0084] In some embodiments, verifying the model output error in step S4 above until the error is within a preset range may include:

[0085] Under the same simulation test environment, the ultrasonic radar echo simulator with configured calibration parameters and completed compilation is tested again to obtain a new actual output distance value. If the difference between the new actual output distance value and the radar input distance value is not greater than the preset error threshold, it means that the error is within the preset range.

[0086] Verifying the error is a necessary step to check the success of calibration. Only by retesting and comparing with a preset threshold can the validity of the calibration parameters be determined, avoiding invalid calibration and ensuring the accuracy of the model. In this embodiment, by retesting under the same simulation environment and judging whether the error is within the preset range, the calibration effect can be accurately verified, ensuring that the output of the calibrated model meets expectations, and providing an effective result judgment standard for the calibration process.

[0087] In some embodiments, step S4 above, verifying the model output error until the error is within a preset range, may further include:

[0088] If the difference between the new actual output distance value and the radar input distance value is greater than the preset error threshold, the new actual output distance value is updated in the target data; the calibration parameters of the ultrasonic radar echo simulator model calculated based on the target data are returned to repeat the calibration, and the calibration parameters of the current time are recalculated; when the calibration is repeated multiple times, the final calibration parameters are the sum of the previous calibration parameters and the current calibration parameters.

[0089] In one example, the final calibration parameter ijn” is calculated using Formula 2 during multiple calibrations:

[0090] Formula 2: ijn”=ijn'+ijn

[0091] Where ijn' is the current calibration parameter and ijn is the previous calibration parameter.

[0092] It should be understood that in actual calibration, a single calibration may not be able to meet the error target. This step establishes a cyclic calibration mechanism, which continuously updates the data and gradually accumulates the correction amount of each calibration to achieve iterative compensation for model error. This can gradually reduce the error until the error is within the preset range, thereby improving the success rate and accuracy of calibration.

[0093] Specifically, when the error does not meet the standard, the target data is updated and the calibration is repeated. The calibration parameters are accumulated multiple times. The previous calibration parameter ijn corrects the historical error, and the current calibration parameter ijn' supplements the correction of the residual error found in the new test. The accumulation of the two can achieve the gradual convergence of the error, and finally control the deviation between the output distance value and the input value within the preset range.

[0094] In some embodiments, steps 1 to 4 are executed automatically through a GUI interface. The GUI interface is associated with the processing code corresponding to each step, and the corresponding operation is triggered by clicking on the interface module.

[0095] The GUI interface encapsulates the complex automated calibration process of test data generation, data processing, parameter calculation, and error verification into a visual operation module. Operators do not need to understand the underlying code logic or the usage details of tools such as Matlab and Python. They can trigger the entire process by simply clicking on the interface module, enabling engineers who are not professional programmers to complete the calibration work efficiently. At the same time, the code associated with the interface ensures that each step is executed in the preset logical order, avoiding problems such as omissions or reversals of steps that may occur in manual operation, and significantly improving the standardization and reliability of the calibration process.

[0096] Figure 2 This is a comparison chart of the input distance value and the actual output value of the ultrasonic radar provided in this embodiment of the disclosure. Figure 3 A comparison diagram of the input distance value and actual output value of the rear ultrasonic radar provided in this embodiment of the disclosure. Figure 2 It covers six radars at the rear of the vehicle (left rear, left rear corner, left rear center, right rear center, right rear corner, and right rear). Figure 3 The method covers six radars at the front of the vehicle (left front, left front corner, left front center, right front center, right front corner, and right front), totaling 12 ultrasonic radars. This means the method provided in this embodiment is applicable to the application scenario where "vehicles with automatic parking are typically equipped with 12 ultrasonic radars." Furthermore, the horizontal / vertical axis coverage ranges from 20 to 90 cm, which falls within the typical short-range detection range of ultrasonic radars. This indicates that the application scenario is an extension of the "detection distance is typically no greater than 500 cm," verifying the method's adaptability to multiple distance ranges.

[0097] In this embodiment of the disclosure, it is clearly stated that "the preset error range is the difference between the input and output distance values ​​≤ 1cm", and error convergence is achieved through "repeated testing → iterative calibration → parameter accumulation". Figure 2 , Figure 3 As can be seen, the deviation between the actual output value and the baseline is less than 1cm (for example, within the range of 20~90cm, the marked point does not deviate significantly from the solid line), which intuitively reflects the high precision of the radar output after calibration. This data verifies the effectiveness of error convergence.

[0098] The automatic calibration method for ultrasonic radar echo simulator models provided in this disclosure automates the calibration of ultrasonic radar echo simulator models, especially adapting to multi-radar and multi-distance scenarios, reducing manual operation and significantly improving calibration efficiency; through precise parameter calculation and multiple calibration mechanisms, the calibration accuracy is improved, ensuring the accuracy of the simulator's output distance and providing a reliable guarantee for intelligent driving simulation testing.

[0099] Figure 4 This is a schematic diagram of the structure of an automatic calibration device for an ultrasonic radar echo simulator model provided in an embodiment of this disclosure, as shown below. Figure 4 As shown, the automatic calibration device includes:

[0100] Test module 10 is configured to perform radar detection range tests on an uncalibrated ultrasonic radar echo simulator in a simulation test environment, and generate test data, which includes radar input distance values ​​and actual output distance values.

[0101] Extraction module 20 is configured to process test data, extract target data, and convert it into a preset format.

[0102] The calibration module 30 is configured to calculate the calibration parameters of the ultrasonic radar echo simulator model based on the target data.

[0103] The verification module 40 is configured to input calibration parameters into the ultrasonic radar echo simulator model and verify the model output error until the error is within a preset range.

[0104] The automatic calibration device for the ultrasonic radar echo simulator model provided in this embodiment of the present disclosure, and Figure 1 The automatic calibration method for the corresponding ultrasonic radar echo simulator model achieves the same technical effect, and will not be elaborated here.

[0105] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement an automatic calibration method for any of the ultrasonic radar echo simulator models described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0106] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0107] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0108] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0109] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in the automatic calibration method for any of the ultrasonic radar echo simulator models described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0110] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described automatic calibration method for the ultrasonic radar echo simulator model.

[0111] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0112] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0113] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0114] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0115] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0116] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0117] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0118] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0119] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0120] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. An automatic calibration method for an ultrasonic radar echo simulator model, characterized in that, The method includes: In a simulation test environment, the radar detection range of an uncalibrated ultrasonic radar echo simulator is tested, and test data is generated, which includes the radar input distance value and the actual output distance value. The test data is processed to extract the target data and convert it into a preset format; The calibration parameters of the ultrasonic radar echo simulator model are calculated based on the target data; Configure the calibration parameters into the ultrasonic radar echo simulator model and verify the model output error until the error is within the preset range.

2. The method according to claim 1, characterized in that, The calibration parameters for calculating the ultrasonic radar echo simulator model based on the target data include: If the difference between the radar input distance value and the actual output distance value is not greater than the preset error threshold, then the calibration parameter is 0; If the difference between the radar input distance value and the actual output distance value is greater than a preset error threshold, then the calibration parameters are calculated according to Formula 1: i jn =A jn (1 - k(B jn –A jn )) Formula 1 Where A jn B is the input range value for radar j. jn denoted as the actual output distance value of radar j, k is a preset coefficient, and n represents the number of calibrations.

3. The method according to claim 2, characterized in that, The step of configuring the calibration parameters into the ultrasonic radar echo simulator model includes: The calibration parameters are stored in CSV format in the model directory, imported into the Matlab workspace, and then associated with the model's calibration module. After completing the configuration in the calibration module, the ultrasonic radar echo simulator is compiled.

4. The method according to claim 2, characterized in that, The verification model outputs an error until the error is within a preset range, including: Under the same simulation test environment, the ultrasonic radar echo simulator with the calibration parameters configured and compiled was tested again to obtain a new actual output distance value. If the difference between the new actual output distance value and the radar input distance value is not greater than the preset error threshold, it means that the error is within the preset range.

5. The method according to claim 2, characterized in that, The verification model outputs an error until the error is within a preset range, and this process further includes: If the difference between the new actual output distance value and the radar input distance value is greater than a preset error threshold, then the new actual output distance value is updated in the target data; The calibration parameters of the ultrasonic radar echo simulator model based on the target data are returned to be calibrated again, and the current calibration parameters are recalculated. When calibration is repeated multiple times, the final calibration parameter is the sum of the previous calibration parameter and the current calibration parameter.

6. The method according to claim 1, characterized in that, The simulation test environment is a Dspace-Simulink co-simulation model. The generated test data includes: The radar detection range test is performed using Dspace automated testing software, and the test data is output in the form of an HTML test report.

7. The method according to claim 6, characterized in that, The process of processing the test data, extracting target data, and converting it into a preset format includes: The test report in HTML format was parsed using Python to extract the radar input distance value and the actual output distance value. Convert the data into a CSV file according to a preset table structure, and store the CSV file in the ultrasonic radar echo simulator model directory.

8. An automatic calibration device for an ultrasonic radar echo simulator model, characterized in that, The device includes: The test module is configured to perform radar detection range tests on an uncalibrated ultrasonic radar echo simulator in a simulation test environment and generate test data, which includes radar input distance values ​​and actual output distance values. The extraction module is configured to process the test data, extract the target data, and convert it into a preset format; The calibration module is configured to calculate the calibration parameters of the ultrasonic radar echo simulator model based on the target data; The verification module is configured to incorporate the calibration parameters into the ultrasonic radar echo simulator model and verify the model output error until the error is within a preset range.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the automatic calibration method for the ultrasonic radar echo simulator model as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the automatic calibration method for the ultrasonic radar echo simulator model as described in any one of claims 1 to 7.

Images