Hardware-in-the-loop calibration method and device, electronic equipment and vehicle

Abstract

The invention relates to a hardware-in-the-loop calibration method and device, electronic equipment and a vehicle. The hardware-in-the-loop calibration method comprises the steps that a vehicle dynamics model and an intelligent driving control system model of the vehicle are established; constructing a plurality of typical working conditions and a plurality of intelligent driving scenes of vehicle driving; constructing a HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model, wherein the HiL bench test environment is used for performing hardware-in-the-loop calibration on an intelligent driving system and a chassis electric control steering system under the plurality of typical working conditions and the plurality of intelligent driving scenes; and performing open-loop calibration on the chassis electric control steering system and / or closed-loop calibration on the intelligent driving system by adopting the HiL rack test environment. According to the embodiment of the invention, the calibration efficiency can be improved when calibration is carried out in the HiL environment, frequent real vehicle testing is not needed, the testing cost is saved, the safety is enhanced, and various working conditions are comprehensively covered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle parameter calibration, in particular to a hardware-in-the-loop calibration method and device, an electronic equipment and a vehicle. BACKGROUND

[0002] With the development of electric, intelligent, networked and shared vehicles, the intelligent, in-domain coordination and cross-domain fusion (fusion with power domain and intelligent driving domain) of vehicle chassis are developing. In order to support the landing of intelligent driving control, the chassis electric power steering system needs to be calibrated to achieve accurate and timely response to typical intelligent driving instructions. In addition, the steering comfort and stability in intelligent scenarios also need to be matched and calibrated.

[0003] Based on typical intelligent driving instructions, the calibration of the chassis electric power steering system is called open-loop calibration. Based on intelligent driving scenarios, the calibration of the intelligent driving system is called closed-loop calibration. At present, the above two calibration processes are carried out based on real vehicles, mainly relying on the experience and repeated tests of calibration personnel, which has the problems of long calibration cycle, heavy task of calibration conditions, and high risk of high-speed conditions. SUMMARY

[0004] One of the purposes of the present application is to provide a hardware-in-the-loop calibration method to solve the problems of long calibration cycle, heavy task of calibration conditions, and high risk of high-speed conditions in the prior art hardware-in-the-loop calibration process. The second purpose is to provide a hardware-in-the-loop calibration device. The third purpose is to provide an electronic equipment. The fourth purpose is to provide a computer readable storage medium. The fifth purpose is to provide a vehicle.

[0005] In order to achieve the above purposes, the technical solutions adopted by the present application are as follows: A hardware-in-the-loop calibration method, the method comprising: creating a vehicle dynamics model and an intelligent driving control system model of a vehicle; constructing a plurality of typical working conditions and a plurality of intelligent driving scenarios of the vehicle; constructing a HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model, the HiL bench test environment being used for hardware-in-the-loop calibration of the intelligent driving system and the chassis electric power steering system under the plurality of typical working conditions and the plurality of intelligent driving scenarios; open-loop calibration of the chassis electric power steering system and / or closed-loop calibration of the intelligent driving system using the HiL bench test environment.

[0006] Optionally, the constructing a HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model comprises: Load the real-time simulation machine and add the hardware devices corresponding to the HiL bench test environment to the real-time simulation machine; Add the vehicle dynamics model, the intelligent driving control system model, the plurality of typical working conditions, and the plurality of intelligent driving scenes to the real-time simulation machine; Connect the real-time simulation machine to the steering HiL cabinet and the steering hardware system, and configure the HiL bench test environment.

[0007] Optionally, the open-loop calibration of the chassis electric power steering system and / or the closed-loop calibration of the intelligent driving system using the HiL bench test environment comprises: Determining a first target item for the open-loop calibration of the chassis electric power steering system; Selecting a first to-be-calibrated parameter of the chassis electric power steering system based on the first target item; Determining a first test scheme corresponding to the first to-be-calibrated parameter; Calling the HiL bench test environment for testing according to the first test scheme, to obtain a first key calibration parameter and a first parameter range; Optimizing the first key calibration parameter and the first parameter range to obtain a first calibration parameter.

[0008] Optionally, the determining of the first test scheme corresponding to the first to-be-calibrated parameter comprises: Determining an upper limit value and a lower limit value of the first to-be-calibrated parameter; Generating a first test matrix of the first to-be-calibrated parameter based on the upper limit value and the lower limit value.

[0009] Optionally, the calling of the HiL bench test environment for testing according to the test scheme to obtain a first key calibration parameter and a first parameter range comprises: Determining an open-loop calibration working condition corresponding to the to-be-calibrated parameter based on the chassis electric power steering system; Converting the first test matrix into a test sequence according to the open-loop calibration working condition; Running the test sequence in the HiL bench test environment to obtain a test result; Determining a first key calibration parameter and a first parameter range according to the test result.

[0010] Optionally, the optimizing of the first key calibration parameter and the first parameter range to obtain a first calibration parameter comprises: Establishing a response surface model between a calibration parameter and a calibration target based on the first key calibration parameter and the first parameter range; A multi-objective optimization method is used for global optimization of the response surface model to determine the first calibration parameter under a typical working condition.

[0011] Optionally, the open-loop calibration of the chassis electric power steering system and / or the closed-loop calibration of the intelligent driving system using the HiL bench test environment comprises: determining a second target item for the closed-loop calibration of the intelligent driving system; establishing a deep neural network model for intelligent driving; the deep neural network model is a model representing the correlation between subjective evaluation score data and objective test data; calling the HiL bench test environment based on the target item to perform testing and obtain target objective test data; inputting the target objective test data into the neural network model to output subjective evaluation score data corresponding to the target objective test data; performing sensitivity analysis based on the subjective evaluation score data to obtain a second important calibration parameter and a second parameter range; optimizing the second important calibration parameter and the second parameter range to obtain a second calibration parameter.

[0012] Optionally, the optimization of the second important calibration parameter and the second parameter range to obtain a second calibration parameter comprises: using an expected score value in the subjective evaluation score data as a calibration target to establish a response surface model between the calibration target and the second important calibration parameter and the second parameter range; using a multi-objective optimization method to perform global optimization of the response surface model to determine the second calibration parameter under a typical working condition.

[0013] Optionally, the hardware device comprises any one or more of the following: a CAN bus communication board card and an IO board card.

[0014] A hardware-in-the-loop calibration device, the device comprising: a model creation module for creating a vehicle dynamics model and an intelligent driving control system model of a vehicle; a working condition and scene creation module for constructing a plurality of typical working conditions and a plurality of intelligent driving scenes of vehicle driving; a test environment construction module for constructing a HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model, the HiL bench test environment being used for hardware-in-the-loop calibration of an intelligent driving system and a chassis electric power steering system under the plurality of typical working conditions and the plurality of intelligent driving scenes; A calibration module is configured to calibrate the electrically controlled steering system in an open loop and / or calibrate the intelligent driving system in a closed loop using the HiL bench test environment.

[0015] An electronic device comprising: a processor; a memory for storing processor-executable instructions; The processor is configured to execute the instructions to implement the hardware-in-the-loop calibration method described above.

[0016] A computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the mobile terminal, enable the mobile terminal to execute the hardware-in-the-loop calibration method described above.

[0017] A vehicle comprising the electronic device described above.

[0018] The beneficial effects of the present application are: In the embodiments of the present application, by constructing a HiL bench test environment, the calibration of the intelligent driving system and the electrically controlled steering system under different typical working conditions and different intelligent driving scenarios is realized, the calibration in the HiL bench test environment reduces the dependence on real vehicle testing, shortens the calibration period, and the automatic test process reduces manual intervention, significantly improving the calibration efficiency; the calibration scheme in the embodiments of the present application does not need to frequently perform real vehicle testing, saving the use cost, maintenance cost and site rental fee of the test vehicle; reducing the demand for real vehicle testing under high speed or other dangerous working conditions, reducing the safety risk in the testing process. At the same time, the dependence on the experience of calibration personnel is reduced, the possibility of human error is reduced, the consistency and reliability of the calibration result are improved; the simulation environment allows testing in various virtual scenarios, including different speeds, road conditions and intelligent driving scenarios, ensuring the adaptability of the system in variable environments. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1a A step flowchart of a hardware-in-the-loop calibration method provided in the embodiments of the present application; Figure 1b A schematic diagram of a HiL bench test environment for an electrically controlled steering system for intelligent driving provided in the embodiments of the present application; Figure 2a A step flowchart of a hardware-in-the-loop calibration method provided in the embodiments of the present application; Figure 2b A schematic diagram of an open loop calibration process provided in the embodiments of the present application; Figure 3a A step flowchart of a hardware-in-the-loop calibration method provided in the embodiments of the present application; Figure 3b A schematic diagram of a closed loop calibration step provided in the embodiments of the present application; Figure 4 FIG. 1 shows a structural schematic diagram of a hardware-in-the-loop calibration device according to an embodiment of the present application; Figure 5 FIG. 2 shows a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] Other advantages and effects of the present application can be easily understood by those skilled in the art from the content disclosed in the present specification. The present application can also be implemented or applied by means of other different specific embodiments, and various modifications or changes can be made to the details in the present specification based on different views and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, but not for limiting the protection scope of the present application.

[0021] It should be noted that the embodiments of the present application can involve the use of user data. In actual applications, user-specific personal data can be used in the schemes described herein within the scope permitted by applicable laws and regulations, for example, with the explicit consent of the user, with the actual notification to the user, etc.

[0022] Reference Figure 1a FIG. 1 shows a structural schematic diagram of a hardware-in-the-loop calibration device according to an embodiment of the present application; Step 101, creating a vehicle dynamics model and an intelligent driving control system model of a vehicle; In the embodiments of the present application, a hardware-in-the-loop (HiL) calibration method for intelligent driving and chassis electric control steering system fusion is involved, which can specifically implement open-loop calibration of a vehicle chassis electric control steering system and closed-loop calibration of a vehicle intelligent driving system.

[0023] In actual applications, a vehicle dynamics model can be established, which can specifically include establishing a vehicle body model, a tire model, a suspension system model, a driving and braking system model, etc.

[0024] In actual applications, to ensure the accuracy of the created model, the established vehicle model can be checked, and the specific checking process can include collecting the steering wheel angle and torque, vehicle speed, acceleration and deceleration, lateral acceleration and yaw angular velocity, etc. response data of the actual vehicle under different working conditions; comparing and analyzing the consistency of the output of the simulation model and the test data to ensure the accuracy of the vehicle model.

[0025] To realize the construction of the calibration environment, an intelligent driving control system model can also be created, which can specifically include a sensor model, such as a millimeter wave radar model, a visual sensor model, a laser radar sensor model, and the like, an environment recognition model, a decision planning model, a control instruction model, and the like.

[0026] In the embodiment of the present application, the vehicle dynamics model and the intelligent driving control system model are both models for simulating vehicle control logic.

[0027] Step 102, constructing a plurality of typical working conditions of vehicle driving and a plurality of intelligent driving scenes; The typical working conditions, as shown in Table 1, include constructing various typical working conditions of the vehicle at different vehicle speeds, different rotation directions, different rotation angle request amplitudes, and frequencies. Different vehicle speeds: 0 kph-200 kph, with a speed interval of 40 kph; different rotation angle input modes: rotation angle ramp signal, rotation angle sine signal, and the like; different rotation angle amplitudes: 0°-maximum rotation angle, with a rotation angle interval of 50°; and different rotation angle speeds: 0° / s-500° / s, with a rotation angle speed interval of 50° / s.

[0028] Table 1 Typical working condition table of open loop

[0029] The intelligent driving scenes, as shown in Table 2, include, but are not limited to, application scenes of intelligent driving auxiliary systems such as a lane keeping assistant system (LKA), a lane centering control (LCC), a lane departure prevention (LDP), a lane departure warning (LDW), an automatic parking system (APA), an automatic emergency steering (AES), and an emergency steering assistant (ESA).

[0030] Table 2 Intelligent driving scene table

[0031] The intelligent driving scene construction includes the following steps: (1) building diversified roads (straight lines, slopes, curved roads with various radii of curvature lane lines, intersections, and the like), road edges, dashed lines, and solid lane lines (clear, worn out); (2) building various parking space scenes (straight, side, and oblique parking spaces), parking space lines (clear, worn out, and no parking space lines); (3) building a driver model: setting driving behaviors at different vehicle speeds; (4) environment simulation: such as light conditions, complex scenes such as rainy days, smog, and high-density parking lots; (5) building a traffic scene: adding different dynamic vehicles (stationary, low speed, emergency lane change), pedestrians, bicycles, animals, traffic signs, buildings, and the like; (6) Build sensor configuration: configure the vehicle front camera to detect lane lines, front vehicles and obstacles; equipped with laser radar to measure the distance and relative speed of the front vehicle.

[0032] Step 103, based on the vehicle dynamics model and the intelligent driving control system model, a HiL test environment is constructed, and the HiL test environment is used for hardware-in-the-loop calibration of the intelligent driving system and the chassis electric control steering system under multiple typical working conditions and multiple intelligent driving scenes. In the embodiment of the present application, the HiL test environment can be constructed based on the vehicle dynamics model and the intelligent driving control system model, and the vehicle control can be simulated based on the vehicle dynamics model and the intelligent driving control system model, thereby realizing calibration.

[0033] The intelligent driving system and the chassis electric control steering system under multiple typical working conditions and multiple intelligent driving scenes can be calibrated in the HiL test environment, and the HiL test environment has universal applicability and can realize vehicle parameter calibration in different typical working conditions and different intelligent driving scenes. It can also calibrate parameters without real vehicle testing through the HiL test environment.

[0034] In an embodiment of the present application, the HiL test environment is constructed based on the vehicle dynamics model and the intelligent driving control system model, including the following sub-steps: Sub-step 11, load the real-time simulation machine, and add the hardware devices corresponding to the constructed HiL test environment to the real-time simulation machine; The real-time simulation machine is the core hardware of the HIL system, and the real-time simulation machine has a certain real-time performance and can run simulation models and complete calculations within a certain time. By loading the real-time simulation machine, a high-fidelity vehicle model can be run, and the model operation is strictly synchronized with the real time. Specifically, a real-time operating system (such as NI VeriStand, dSPACE SCALEXIO, etc.) can be started on the real-time simulation machine to prepare an environment for running the model.

[0035] Then the hardware devices corresponding to the constructed HiL test environment are added to the real-time simulation machine, which establishes a bridge between the virtual world and the real hardware.

[0036] In an example, the hardware devices include any one or more of the following: CAN bus communication board card, IO board card.

[0037] The CAN bus board card can be used for communication with the steering ECU. The real-time simulator can send virtual vehicle state information (such as vehicle speed, engine speed, steering wheel angle sensor signal, etc.) to the ECU through the CAN card, and receive control instructions (such as target auxiliary torque) from the ECU.

[0038] The IO board card (including D / A, A / D, DIO, PWM, etc.) can be used to connect and process analog signals and digital signals. For example: receiving motor control signals (PWM) from the steering ECU. Send analog torque sensor signals (analog voltage) to the steering ECU. Connect the fault injection unit to simulate line short circuit / disconnection (digital IO).

[0039] Substep 12, add the vehicle dynamics model, intelligent driving control system model, multiple typical working conditions, and multiple intelligent driving scenes to the real-time simulator; In actual application, the vehicle dynamics model, intelligent driving control system model, multiple typical working conditions, and multiple intelligent driving scenes can also be added to the real-time simulator to simulate the running environment and controlled object of the steering system.

[0040] Substep 13, connect the real-time simulator to the steering HiL cabinet and the steering hardware system, and configure the HiL bench test environment.

[0041] By connecting the real-time simulator to the steering HiL cabinet and the steering hardware system, the real steering ECU and its actuators (such as EPS motor) and sensors can be connected to the test system to form a complete "hardware-in-the-loop" test environment.

[0042] In actual application, the state of the entire test bench can be made consistent with the real vehicle or specific test scene by configuring the HiL bench test environment. Specifically, CAN communication configuration, power management configuration, test scene initialization configuration, etc. Configuration operations can be performed.

[0043] Step 104, open-loop calibration of the chassis electric power steering system and / or closed-loop calibration of the intelligent driving system using the HiL bench test environment.

[0044] After the HiL bench test environment is constructed, the chassis electric power steering system can be calibrated in open loop, and the intelligent driving system can be calibrated in closed loop.

[0045] For example, Figure 1bAs shown, it is a chassis electric control steering system HiL bench test environment schematic diagram for intelligent driving provided in an embodiment of the present application. The electric control steering HiL bench can include a host computer, a switch and a real-time simulator, and the real-time simulator can include a central processing unit, an IO board card and a CAN card. The vehicle dynamics model, the intelligent driving control model and the intelligent scene built can be applied to the electric control steering HiL bench to realize vehicle parameter calibration.

[0046] In the embodiment of the present application, by constructing the HiL bench test environment, the calibration of the intelligent driving system and the chassis electric control steering system under different typical working conditions and different intelligent driving scenes is realized. The calibration in the HiL bench test environment reduces the dependence on real vehicle testing, shortens the calibration period, and the automatic test process reduces manual intervention, significantly improving the calibration efficiency. The calibration scheme in the embodiment of the present application does not need to frequently perform real vehicle testing, saving the use cost, maintenance cost and site rental fee of the test vehicle. The real vehicle testing demand under high speed or other dangerous working conditions is reduced, and the safety risk in the testing process is reduced. At the same time, the dependence on the experience of calibration personnel is reduced, the possibility of human error is reduced, and the consistency and reliability of the calibration result are improved. The simulation environment allows testing in various virtual scenes, including different speeds, road conditions and intelligent driving scenes, ensuring the adaptability of the system in variable environments.

[0047] Referring to Figure 2a , a step flow chart of another hardware-in-the-loop calibration method provided in an embodiment of the present application is shown, and specifically includes the following steps: Step 201, creating a vehicle dynamics model and an intelligent driving control system model of a vehicle; Step 202, constructing a plurality of typical working conditions and a plurality of intelligent driving scenes of vehicle driving; Step 203, constructing a HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model, the HiL bench test environment being used for hardware-in-the-loop calibration of the intelligent driving system and the chassis electric control steering system under the plurality of typical working conditions and the plurality of intelligent driving scenes; Step 204, determining a first target item of open-loop calibration of the chassis electric control steering system; In actual application, a performance objective evaluation index system of the chassis electric control steering system under each typical working condition can be established, and objective evaluation indexes such as reaction time of angle control, time difference of reaching target angle, angle error of EPS execution, angle overshoot of EPS execution, maximum amplitude limit of response steering angle, and reverse request response time can be included in the performance objective evaluation index system as target items of calibration of the chassis electric control steering system. The target item is the performance objective evaluation index under each typical working condition.

[0048] Step 205, based on the first target item, the first to-be-calibrated parameter of the chassis electric-controlled steering system is selected; The first to-be-calibrated parameter is a parameter that needs to be calibrated in software development, including but not limited to the proportional, integral, and differential coefficients and damping coefficients of the chassis electric-controlled steering system, and the like. The first to-be-calibrated parameter is a variable that affects the first target item. By calibrating different parameters, the target item can reach the target.

[0049] Step 206, a first test scheme corresponding to the first to-be-calibrated parameter is determined; After the first to-be-calibrated parameter is determined, a first test scheme can be set for the first to-be-calibrated parameter. The first test scheme is a design idea for accurately calibrating the first to-be-calibrated parameter.

[0050] Step 207, the HiL bench test environment is called according to the first test scheme to perform testing, and a first key calibration parameter and a first parameter range are obtained; After the first test scheme is constructed, the HiL bench test environment can be called to perform testing. According to the test structure, the first key calibration parameter and the first parameter range can be determined.

[0051] Step 208, the first key calibration parameter and the first parameter range are optimized to obtain a first calibration parameter.

[0052] After the first key calibration parameter and the first parameter range are obtained, a preset optimization scheme can be used for optimization processing, and then the first calibration parameter is obtained. The first calibration parameter can be used as a standard parameter in the vehicle driving process.

[0053] In an embodiment of the present application, the first test scheme corresponding to the first to-be-calibrated parameter is determined, including: determining an upper limit value and a lower limit value of the first to-be-calibrated parameter; and generating a first test matrix of the first to-be-calibrated parameter based on the upper limit value and the lower limit value.

[0054] In actual application, the purpose of setting the upper and lower limit values is to set a reasonable range of the to-be-calibrated parameter. Sampling is performed within the reasonable range, the test matrix is designed, and then the key calibration parameter can be output based on the test results. The upper and lower limit values of the key calibration parameter do not exceed the upper and lower limit values of the to-be-calibrated parameter. The upper and lower limit values set here are variables (to-be-calibrated parameters) and are not used as a scoring standard.

[0055] In an example, the method for setting the upper limit and the lower limit of the to-be-calibrated parameter includes but is not limited to the actual physical meaning of the to-be-calibrated parameter, the value range set in the software development stage, and the scaling of a single initial value by ± 50%. The single initial value setting is a version of the parameter given based on engineering experience, physical meaning, and parameter identification technology. ± 50% is a commonly used method for setting the upper and lower limit values of the to-be-calibrated parameter in engineering, and can be used to generate a subsequent test matrix.

[0056] In practical applications, the first test matrix of the first to-be-calibrated parameter can be generated by methods including but not limited to an optimal Latin hypercube design method, a Latin hypercube design method, an orthogonal test method, a parameter test, and the like.

[0057] In an embodiment of the present application, the HiL bench test environment is called according to the test scheme to perform testing, and the first key calibration parameter and the first parameter range are obtained, including: determining an open-loop calibration working condition corresponding to the to-be-calibrated parameter based on the chassis electric power steering system; converting the first test matrix into a test sequence according to the open-loop calibration working condition; running the test sequence in the called HiL bench test environment to obtain a test result; The first key calibration parameter and the first parameter range are determined according to the test result. The method for determining the first key calibration parameter and the first parameter range includes but is not limited to drawing a Pareto chart, a correlation chart, a main effect chart, and a interaction effect chart.

[0058] In an embodiment of the present application, the first key calibration parameter and the first parameter range are optimized to obtain the first calibration parameter, including: establishing a response surface model between the calibration parameter and the calibration target based on the first key calibration parameter and the first parameter range; and performing global optimization on the response surface model by using a multi-objective optimization method to determine the first calibration parameter under a typical working condition.

[0059] In practical applications, the response surface model between the calibration parameter and the calibration target can be established by using a polynomial fitting method, and then the calibration data under the typical working condition can be determined by using an intelligent multi-objective optimization method to perform global optimization based on the established response surface model. The intelligent optimization method includes but is not limited to a second-generation non-neighbor planting genetic algorithm.

[0060] Referring to Figure 2b FIG. 1 is a schematic diagram of an open-loop calibration process in an embodiment of the present application. The process can include determining a target item of the open-loop calibration of the chassis electric power steering system, selecting a to-be-calibrated parameter of the chassis electric power steering system, designing a test scheme, carrying out a test and data collection, outputting a key calibration parameter, establishing a response surface model, and optimizing a calibration parameter.

[0061] In the embodiment of the present application, by constructing a HiL bench test environment, the calibration of the chassis electric control steering system under different typical working conditions and different intelligent driving scenes is realized. By calibrating in the HiL bench test environment, the dependence on real vehicle testing is reduced, the calibration period is shortened, and the automatic test process reduces manual intervention, thereby significantly improving the calibration efficiency. The calibration scheme in the embodiment of the present application does not need to frequently perform real vehicle testing, thereby saving the use cost, maintenance cost and site rental fee of the test vehicle. The demand for real vehicle testing under high speed or other dangerous working conditions is reduced, and the safety risk in the testing process is reduced. At the same time, the dependence on the experience of calibration personnel is reduced, the possibility of human error is reduced, and the consistency and reliability of the calibration result are improved. The simulation environment allows testing in various virtual scenes, including different speeds, road conditions and intelligent driving scenes, to ensure the adaptability of the system in variable environments.

[0062] Referring to Figure 3a , a step flowchart of another hardware-in-the-loop calibration method provided in the embodiment of the present application is shown, and specifically includes the following steps: Step 301, creating a vehicle dynamics model and an intelligent driving control system model of a vehicle; Step 302, constructing a plurality of typical working conditions and a plurality of intelligent driving scenes of vehicle driving; Step 303, constructing a HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model, the HiL bench test environment being used for hardware-in-the-loop calibration of the intelligent driving system and the chassis electric control steering system under the plurality of typical working conditions and the plurality of intelligent driving scenes; Step 304, determining a second target item of the closed-loop calibration of the intelligent driving system; In actual application, the target of the closed-loop calibration of the intelligent driving system can be determined, a performance subjective evaluation system of the chassis electric control steering system under each intelligent driving scene is established, including but not limited to the system stability and response speed in the LKA function, and the steering stability and ride comfort of the function in the operation process are comprehensively evaluated; the alarm sensitivity, alarm sound, vibration intensity and other human-computer interaction evaluation in the LDW function; the subjective evaluation indexes such as parking process smoothness, confidence, effect after parking completion and identification of special obstacles in the APA function are taken as the target of the calibration of the chassis electric control steering system.

[0063] Step 305, establishing a deep neural network model for intelligent driving; the deep neural network model is a model representing the correlation between subjective evaluation scoring data and objective test data; The establishment of the subjective and objective evaluation consistency model for intelligent driving is a correlation consistency model between the objective test index and the subjective evaluation of the intelligent driving vehicle based on the intelligent driving scene, which can include the following steps: (1) Data collection, record the actual vehicle in the intelligent driving scene (such as LKA, APA, LDW, ACC, etc.) subjective evaluation score, and collect the actual vehicle objective test index (such as yaw rate gain, lateral acceleration, longitudinal acceleration, roll angle gradient, vehicle pitch angle gradient, etc.), the subjective and objective data are divided into two parts, one part is used for training modeling (the data amount accounts for 70% of the total data amount), and the other part is used for model test (in addition to the data used for training modeling, the remaining data); (2) A deep neural network method is used to establish a deep neural network model of subjective evaluation score data and objective test data; (3) Based on the remaining data of steps (1) and (2), the accuracy of the deep neural network model is verified.

[0064] Step 306, based on the target item, a HiL bench test environment is called to test, and target objective test data is obtained; Step 307, input the target objective test data into the neural network model, and output the subjective evaluation score data corresponding to the target objective test data; Step 308, based on the subjective evaluation score data, sensitivity analysis is performed to obtain second important calibration parameters and a second parameter range; In actual application, in the intelligent driving system calibration process, sensitivity analysis can be performed, and the sensitivity analysis can include calibration parameter selection, upper and lower limit setting of the calibration parameter, design of the test matrix and collection of the test results. The calibration parameters include, but are not limited to, speed limit threshold, follow-stop distance, offset distance parameter, offset frequency, lane changing mode, trajectory smoothing parameter, vibration level amplitude, vibration duration, vibration interval, etc.

[0065] Step 309, the second important calibration parameter and the second parameter range are optimized to obtain a second calibration parameter.

[0066] In an embodiment of the present application, the second important calibration parameter and the second parameter range are optimized to obtain a second calibration parameter, including: taking the expected score in the subjective evaluation score data as a calibration target, and establishing a response surface model between the calibration parameter and the calibration target based on the second important calibration parameter and the second parameter range; a multi-objective optimization method is used for global optimization of the response surface model to determine the second calibration parameter under the typical working condition.

[0067] As Figure 3bAs shown, it is a closed-loop calibration step schematic diagram in the embodiment of the present application. It can include the following processes: determining the target item of the intelligent driving system closed-loop calibration, establishing the subjective and objective evaluation consistency model for intelligent driving (based on the collection of actual vehicle subjective scoring and objective test data), running the chassis electronic control to the HiL bench, collecting the bench objective test data, outputting the subjective scoring of the vehicle in the intelligent scene, sensitivity analysis, outputting the key calibration parameters and their ranges, designing the test matrix, calibrating the key parameters, whether the expected score is met, when the expected score is met, outputting the calibration data, if not, returning to the design test matrix and recalibrating.

[0068] In the embodiment of the present application, by constructing the HiL bench test environment, the calibration of the intelligent driving system under different typical working conditions and different intelligent driving scenes is realized. By calibrating in the HiL bench test environment, the dependence on real vehicle testing is reduced, the calibration period is shortened, and the automatic test process reduces manual intervention, significantly improving the calibration efficiency. The calibration scheme in the embodiment of the present application does not need to frequently perform real vehicle testing, saving the use cost, maintenance cost and site rental fee of the test vehicle; reducing the demand for real vehicle testing under high speed or other dangerous working conditions, reducing the safety risk in the testing process. At the same time, the dependence on the experience of calibration personnel is reduced, the possibility of human error is reduced, and the consistency and reliability of the calibration result are improved; the simulation environment allows testing in various virtual scenes, including different speeds, road conditions and intelligent driving scenes, ensuring the adaptability of the system in variable environments.

[0069] It should be noted that, for the method embodiment, in order to simply describe, it is expressed as a series of action combinations, but those skilled in the art should know that the embodiment of the present application is not limited by the described action sequence, because according to the embodiment of the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions involved are not necessarily required by the embodiment of the present application.

[0070] Referring to Figure 4 , a structure schematic diagram of a hardware-in-the-loop calibration device provided in the embodiment of the present application is shown, which specifically includes the following modules: The model creation module 401 is used to create a vehicle dynamics model and an intelligent driving control system model of the vehicle; The working condition and scene creation module 402 is used to construct a plurality of typical working conditions and a plurality of intelligent driving scenes of vehicle driving; The test environment construction module 403 is configured to construct a HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model, and the HiL bench test environment is used for hardware-in-the-loop calibration of the intelligent driving system and the electrically controlled steering system under the multiple typical working conditions and the multiple intelligent driving scenes. The calibration module 404 is configured to calibrate the electrically controlled steering system in an open loop and / or calibrate the intelligent driving system in a closed loop by using the HiL bench test environment.

[0071] In an embodiment of the present application, the test environment construction module 403 can include: The simulation machine loading submodule is configured to load a real-time simulation machine and add the hardware devices corresponding to the constructed HiL bench test environment to the real-time simulation machine. The data adding submodule is configured to add the vehicle dynamics model, the intelligent driving control system model, the multiple typical working conditions, and the multiple intelligent driving scenes to the real-time simulation machine. The configuration submodule is configured to connect the real-time simulation machine to a steering HiL cabinet and a steering hardware system, and configure the HiL bench test environment.

[0072] In an embodiment of the present application, the calibration module 404 can include: The first target item determining submodule is configured to determine a first target item of the open-loop calibration of the electrically controlled steering system. The first to-be-calibrated parameter determining submodule is configured to select a first to-be-calibrated parameter of the electrically controlled steering system based on the first target item. The first test scheme determining submodule is configured to determine a first test scheme corresponding to the first to-be-calibrated parameter. The first key calibration parameter determining submodule is configured to call the HiL bench test environment for testing according to the first test scheme, and obtain a first key calibration parameter and a first parameter range. The first optimization submodule is configured to optimize the first key calibration parameter and the first parameter range, and obtain a first calibration parameter.

[0073] In an embodiment of the present application, the first test scheme determining submodule can include: The first limit determining unit is configured to determine an upper limit value and a lower limit value of the first to-be-calibrated parameter. The first test matrix determining unit is configured to generate a first test matrix of the first to-be-calibrated parameter based on the upper limit value and the lower limit value.

[0074] In an embodiment of the present application, the first key calibration parameter determining submodule can include: An open-loop calibration working condition determination unit is configured to determine an open-loop calibration working condition corresponding to the to-be-calibrated parameter based on the chassis electrically-controlled steering system; A test sequence determination unit is configured to convert the first test matrix into a test sequence according to the open-loop calibration working condition; A test result determination unit is configured to run the test sequence in the HiL bench test environment to obtain a test result; A first key calibration parameter determination unit is configured to determine a first key calibration parameter and a first parameter range according to the test result.

[0075] In an embodiment of the present application, the first optimization sub-module can include: A first response surface parameter determination unit is configured to establish a response surface model between a calibration parameter and a calibration target based on the first key calibration parameter and the first parameter range; A first calibration parameter determination unit is configured to determine a first calibration parameter under a typical working condition by performing global optimization on the response surface model using a multi-objective optimization method.

[0076] In an embodiment of the present application, the calibration module 404 can include: A second target item determination sub-module is configured to determine a second target item of the closed-loop calibration of the intelligent driving system; A second to-be-calibrated parameter determination sub-module is configured to select a second to-be-calibrated parameter of the intelligent driving system based on the second target item; A second test scheme determination sub-module is configured to determine a second test scheme corresponding to the second to-be-calibrated parameter; A second key calibration parameter determination sub-module is configured to call the HiL bench test environment for testing according to the second test scheme to obtain a second key calibration parameter and a second parameter range; A second optimization sub-module is configured to optimize the second key calibration parameter and the second parameter range to obtain a second calibration parameter.

[0077] In an embodiment of the present application, the second optimization sub-module can include: A deep neural network model construction unit is configured to establish a deep neural network model for intelligent driving; the deep neural network model is a model representing the correlation between subjective evaluation scoring data and objective test data; A target objective test data determination unit is configured to call the HiL bench test environment for testing based on the target item to obtain target objective test data; A subjective evaluation scoring data determination unit is configured to input the target objective test data into the neural network model to output subjective evaluation scoring data corresponding to the target objective test data; The response surface model determination unit is used to establish a response surface model between the calibration parameters and the calibration target by using the expected score in the subjective evaluation scoring data as the calibration target, and the second key calibration parameters and the second parameter range. The second calibration parameter determination unit is used to perform global optimization using a multi-objective optimization method on the response surface model to determine the second calibration parameters under typical working conditions.

[0078] In one embodiment of the present invention, the hardware device includes any one or more of the following: CAN bus communication board, IO board.

[0079] In this embodiment of the invention, by constructing a HiL bench test environment, the calibration of the intelligent driving system and the chassis electronic steering system under different typical working conditions and intelligent driving scenarios is achieved. Calibration within the HiL bench test environment reduces reliance on real-vehicle testing, shortens the calibration cycle, and the automated testing process reduces manual intervention, significantly improving calibration efficiency. The calibration scheme in this embodiment eliminates the need for frequent real-vehicle testing, saving on test vehicle usage costs, maintenance costs, and site rental fees. It also reduces the need for real-vehicle testing under high-speed or other hazardous conditions, lowering safety risks during testing. Furthermore, it reduces reliance on the experience of calibration personnel, lowering the possibility of human error and improving the consistency and reliability of calibration results. The simulation environment allows testing in various virtual scenarios, including different speeds, road conditions, and intelligent driving scenarios, ensuring the system's adaptability in changing environments.

[0080] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment. This invention also provides an electronic device, such as... Figure 5 As shown, it includes a processor 501, a device interface 502, a memory 503, and a bus 504; Memory 503 is used to store computer programs; The processor 501 performs the above steps when executing the program stored in the memory 503.

[0081] The bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0082] The memory can include a Random Access Memory (RAM) and can also include a non-volatile memory, such as at least one disk memory. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0083] The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc., and can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0084] The present application also provides a storage medium, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the hardware-in-the-loop calibration method of the aforementioned embodiments.

[0085] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts are referred to the part of the method embodiments.

[0086] The algorithms and displays presented herein are not inherently related to any particular computer, virtual apparatus, or other apparatus. Structural requirements of such apparatus required to be constructed in accordance with the description above are explicitly apparent from the above description. Moreover, the present application is not directed to any particular programming language. It will be appreciated that a variety of programming languages can be used to implement the present application described herein, and that the descriptions above are provided for the best mode of practicing the present application.

[0087] In the specification provided herein, a large number of specific details are described. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some examples, well-known methods, structures and techniques are not shown in detail in order not to obscure the understanding of the present specification.

[0088] Similarly, it is to be understood that the embodiments of the present application can be positioned and described in a number of different orientations, and the terminology or description can be chosen for purposes of convenience only. It is therefore intended that the application include all such modifications and alterations in the depiction of the essential aspects of the preferred embodiments of the application that were required over and above the normal variations inherent in any significant, modular, reusable hardware or software components. Additionally, the term "data bus" as used throughout this detailed description is intended to refer to any communication bus or interconnect, including a system bus, a memory bus, and / or a peripheral bus, and can include any interconnect (e.g., point-to-point interconnects, shared bus interconnects, etc.) that allows communications between various hardware or software components depicted in the drawings or described throughout this detailed description.

[0089] Those skilled in the art will appreciate that the modules in the apparatuses in the embodiments can be adapted and placed in one or more apparatuses other than the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and further can be divided into more sub-modules or sub-units or sub-components. Any combination of all the features disclosed in the specification (including the accompanying claims, abstract and drawings), and any method or steps of any method so disclosed can be made unless specifically stated otherwise. Each feature disclosed in the specification (including the accompanying claims, abstract and drawings) can be replaced by alternative features serving the same, equivalent or similar purpose unless specifically stated otherwise.

[0090] Embodiments of the various components of the application can be implemented in hardware, or as software modules running in one or more processors, or some combination of both. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functionality of some or all of the components in the sequencing apparatus according to the present application. The present application can also be implemented as a program of instructions for performing part or all of the methods described herein on a device or apparatus. Such a program of instructions can be stored on a computer readable medium or can be in the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0091] It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that one skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word 'comprising' does not exclude the presence of elements or steps other than those listed in a claim. The word 'a' or 'an' preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of both hardware and software, and any combination thereof. In a unit claim, several devices can be listed with a comma. The use of the term 'about' followed by a value and / or a term 'approximately' preceding a value means that the value can vary from the stated value by 10%. The use of any of the following terms in the claims is neither meant to limit the scope nor to introduce a non-combination limitation. The terms 'comprise', 'include', and 'contain' are not used in their exclusive sense. The use of the term 'first','second', and 'third' does not connote any order, quantity, creation or importance, but rather are used to denote one element from another. The use of these terms is interchangeable under appropriate circumstances.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0093] The above only represents the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0094] The above only represents the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0095] It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that one skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word 'comprising' does not exclude the presence of elements or steps other than those listed in a claim. The word 'a' or 'an' preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of both hardware and software, and any combination thereof. In a unit claim, several devices can be listed with a comma. The use of the term 'about' followed by a value and / or a term 'approximately' preceding a value means that the value can vary from the stated value by 10%. The use of any of the following terms in the claims is neither meant to limit the scope nor to introduce a non-combination limitation. The terms 'comprise', 'include', and 'contain' are not used in their exclusive sense. The use of the term 'first','second', and 'third' does not connote any order, quantity, creation or importance, but rather are used to denote one element from another. The use of these terms is interchangeable under appropriate circumstances.

Claims

1. A hardware-in-the-loop calibration method, characterized in that, The method includes: Create vehicle dynamics models and intelligent driving control system models; Construct multiple typical operating conditions and multiple intelligent driving scenarios for vehicle operation; The HiL bench test environment is constructed based on the vehicle dynamics model and the intelligent driving control system model. The HiL bench test environment is used to perform hardware-in-the-loop calibration of the intelligent driving system and chassis electronic steering system under multiple typical working conditions and multiple intelligent driving scenarios. The HiL bench test environment is used to perform open-loop calibration of the chassis electronic steering system and / or closed-loop calibration of the intelligent driving system.

2. The method according to claim 1, characterized in that, The HiL bench test environment, constructed based on the vehicle dynamics model and the intelligent driving control system model, includes: Load the real-time simulator and add the hardware devices corresponding to the HiL bench test environment to the real-time simulator; The vehicle dynamics model, the intelligent driving control system model, the multiple typical operating conditions, and the multiple intelligent driving scenarios are added to the real-time simulator. Connect the real-time simulator to the HiL cabinet and the HiL hardware system, and configure the HiL bench test environment.

3. The method according to claim 1, characterized in that, The open-loop calibration of the chassis electronic steering system and / or the closed-loop calibration of the intelligent driving system using the HiL bench test environment includes: Determine the first target item for the open-loop calibration of the chassis electronic steering system; Based on the first target item, the first parameter to be calibrated for the chassis electronic steering system is selected; Determine the first test plan corresponding to the first parameter to be calibrated; According to the first test plan, the HiL bench test environment is called to perform the test, and the first recalibration parameters and the first parameter range are obtained. The first calibration parameters and the first parameter range are optimized to obtain the first calibration parameters.

4. The method according to claim 3, characterized in that, The step of determining the first test plan corresponding to the first parameter to be calibrated includes: Determine the upper and lower limits of the first parameter to be calibrated; The first test matrix of the first parameter to be calibrated is generated based on the upper and lower limits.

5. The method according to claim 4, characterized in that, The step of calling the HiL bench test environment according to the first test plan to perform the test, and obtaining the first recalibration parameters and the first parameter range, includes: Based on the chassis electronic steering system, determine the open-loop calibration condition corresponding to the first parameter to be calibrated; The first test matrix is ​​converted into a test sequence according to the open-loop calibration conditions described above; The test sequence is run in the HiL bench test environment to obtain the test results; The first recalibration parameters and the range of the first parameters were determined based on the test results.

6. The method according to claim 3, characterized in that, The optimization of the first key calibration parameter and the first parameter range to obtain the first calibration parameter includes: A response surface model between the calibration parameters and the calibration target is established based on the first key calibration parameters and the first parameter range; A multi-objective optimization method is used to perform global optimization on the response surface model to determine the first calibration parameter under typical operating conditions.

7. The method according to claim 1, characterized in that, The open-loop calibration of the chassis electronic steering system and / or the closed-loop calibration of the intelligent driving system using the HiL bench test environment includes: Determine the second target item for the closed-loop calibration of the intelligent driving system; A deep neural network model for intelligent driving is established; the deep neural network model is a model that represents the correlation between subjective evaluation scoring data and objective test data. Based on the second target item, the HiL bench test environment is invoked to perform the test and obtain objective test data of the target. The objective test data of the target is input into the deep neural network model, and the subjective evaluation score data corresponding to the objective test data of the target is output. Sensitivity analysis was performed based on the subjective evaluation scoring data to obtain the second recalibration parameter and the range of the second parameter. The second calibration parameters and the range of the second parameter are optimized to obtain the second calibration parameters.

8. The method according to claim 7, characterized in that, The optimization of the second critical calibration parameter and the range of the second parameter to obtain the second calibration parameter includes: Using the expected score in the subjective evaluation scoring data as the calibration target, a response surface model between the calibration parameters and the calibration target is established with the second critical calibration parameter and the second parameter range; A multi-objective optimization method is used to perform global optimization on the response surface model to determine the second calibration parameter under typical operating conditions.

9. The method according to claim 2, characterized in that, The hardware device includes any one or more of the following: CAN bus communication board, IO board.

10. A hardware-in-the-loop calibration device, characterized in that, The device includes: The model creation module is used to create vehicle dynamics models and intelligent driving control system models. The working condition and scenario creation module is used to construct multiple typical working conditions and multiple intelligent driving scenarios for vehicle operation; The test environment construction module is used to construct the HiL bench test environment based on the vehicle dynamics model and the intelligent driving control system model. The HiL bench test environment is used to perform hardware-in-the-loop calibration of the intelligent driving system and chassis electronic steering system under multiple typical working conditions and multiple intelligent driving scenarios. The calibration module is used to perform open-loop calibration of the chassis electronic steering system and / or closed-loop calibration of the intelligent driving system using the HiL bench test environment.

11. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the instructions to implement the hardware-in-the-loop calibration method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the mobile terminal, the mobile terminal is able to perform the hardware-in-the-loop calibration method as described in any one of claims 1 to 9.

13. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 11.

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