Vehicle driving simulation systems and methods suitable for road condition simulation testing

Abstract

This application relates to the field of road condition simulation driving simulation technology, specifically to a vehicle driving simulation system and method suitable for road condition simulation testing. The method includes: acquiring the amplitude values ​​of various command signals representing the simulated driver's actions and the tilt angle data of a six-degree-of-freedom motion platform; extracting high-frequency and low-frequency sets of the tilt angles and obtaining the high-frequency motion suppression degree for each acquisition cycle; calculating the motion gain characteristic values ​​of various command signals and, in conjunction with the high-frequency motion suppression degree, obtaining the scaling characteristic values ​​of various command signals, adjusting the time-varying scaling coefficients of various command signals in the classical washout algorithm; and using the classical washout algorithm combined with the six-degree-of-freedom motion platform pose inverse kinematics algorithm to obtain the target extension amount of each electric cylinder of the six-degree-of-freedom motion platform, thereby controlling the attitude of the six-degree-of-freedom motion platform. This application can improve the accuracy of vehicle driving simulation.

Description

Technical Field

[0001] This application relates to the field of road condition simulation driving simulation technology, specifically to vehicle driving simulation systems and methods applicable to road condition simulation testing. Background Technology

[0002] Driving tests are a core component of road testing, but conducting on-site driving tests faces numerous limitations, such as high costs, limited coverage of operating conditions, and high testing risks. Against this backdrop, driving simulation testing has become an effective alternative to on-site driving tests. The core of this alternative is the degree of realism in replicating the driving experience. To realistically simulate the postures that occur during vehicle driving, a six-degree-of-freedom (6DOF) motion platform is typically used for vehicle driving simulation, achieving six degrees of freedom of motion in space, including pitch, yaw, and roll rotations, as well as linear motion in the lateral, longitudinal, and vertical directions. The 6DOF motion platform uses a classic washout algorithm to perform a washout operation on the simulated vehicle driving, calculating the target posture of the 6DOF motion platform at the next moment. Then, the inverse kinematics algorithm for the 6DOF motion platform's pose is used to solve for the target extension amount of each electric cylinder. Finally, a servo controller drives the servo motors to control the extension amount of each electric cylinder in the 6DOF motion platform, achieving posture control of the 6DOF motion platform.

[0003] In classic shuffling algorithms, time-varying scaling factors are typically used to scale the input signal to improve the realism of vehicle driving simulation and prevent the six-degree-of-freedom (6DOF) motion platform from bottoming out due to drastic changes. However, traditional classic shuffling algorithms generally only consider the amplitude changes of the input signal for adaptive scaling, failing to fully exploit the segmented motion characteristics of the 6DOF motion platform. This results in inaccurate scaling of the input signal, lower accuracy in calculating the target posture of the 6DOF motion platform, and negatively impacts the user experience during vehicle driving simulation. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a vehicle driving simulation system and method suitable for road condition simulation testing. The specific technical solution adopted is as follows:

[0005] This application provides a vehicle driving simulation method suitable for road condition simulation testing, including the following steps:

[0006] Acquire the amplitude values ​​of various command signals for the simulated driver's operating behavior, including the amplitude values ​​of command signals for linear acceleration and angular acceleration, and acquire the tilt angle data of the six-degree-of-freedom motion platform;

[0007] Based on the frequency variation characteristics of the tilt angle data in the frequency domain within each acquisition cycle, high-frequency and low-frequency sets of the tilt angle for each acquisition cycle are extracted. Then, by combining the low-frequency proportion within each acquisition cycle and the average level of the amplitude corresponding to the frequency in the low-frequency set of the tilt angle with the degree of change and the average level of the amplitude corresponding to the frequency in the high-frequency set of the tilt angle, the high-frequency motion suppression degree of each acquisition cycle is obtained.

[0008] By simulating the peak significance of various command signals in the amplitude of various command signals, the salience of each peak is obtained. Based on the dispersion and average level of the peak salience, the motion gain characteristic value of various command signals in each acquisition period is obtained. Combined with the high-frequency motion suppression degree, the scaling characteristic value of various command signals is obtained. The time-varying scaling coefficient of various command signals in the classic washing algorithm is adjusted by the degree of change of the scaling characteristic value of various command signals.

[0009] By using the classic washout algorithm and combining it with the inverse kinematics algorithm of a six-degree-of-freedom motion platform, the target extension amount of each electric cylinder of the six-degree-of-freedom motion platform at the next moment is obtained, thereby realizing the attitude control of the six-degree-of-freedom motion platform.

[0010] Preferably, the tilt angle data in each acquisition cycle are normalized and arranged in time sequence to form the tilt angle sequence of each acquisition cycle. The amplitude spectrum of the tilt angle sequence is extracted by frequency domain transformation, and all frequency values ​​with amplitudes not equal to 0 in the amplitude spectrum are used to form the tilt angle frequency set of each acquisition cycle.

[0011] Preferably, the tilt angle frequency set of each acquisition cycle is segmented by a threshold. All frequencies in the tilt angle frequency set that are greater than the segmentation threshold are formed into the high-frequency tilt angle set of each acquisition cycle, and all frequencies that are less than or equal to the segmentation threshold are formed into the low-frequency tilt angle set of each acquisition cycle.

[0012] Preferably, the process for obtaining the high-frequency motion suppression degree in each acquisition cycle is as follows: In the formula, Let be the high-frequency motion suppression degree in the t-th acquisition cycle. Here is the range normalization function. Let be the proportion of low-frequency distribution in the t-th acquisition cycle. Let be the average amplitude of all frequencies in the low-frequency set of the tilt angle during the t-th acquisition period. To avoid constants with a denominator of 0, Let be the mean of the absolute values ​​of all elements in the first-order difference sequence of the high-frequency amplitude variation sequence in the t-th acquisition period. Let be the mean value of the elements in the high-frequency amplitude change sequence of the t-th acquisition period, where the amplitude values ​​corresponding to all frequencies in the high-frequency set of tilt angle are arranged in ascending order of frequency to form the high-frequency amplitude change sequence of each acquisition period.

[0013] Preferably, the ratio of the number of elements in the low-frequency set of the tilt angle to the number of elements in the high-frequency set of the tilt angle in each acquisition cycle is taken as the low-frequency distribution proportion of each acquisition cycle.

[0014] Preferably, the normalized results of the amplitudes of various command signals in each acquisition cycle are arranged in time sequence to form the amplitude sequence of each command signal in each acquisition cycle. The peak values ​​in each command signal amplitude sequence are extracted, and the ratio of each peak value in the command signal amplitude sequence to the mean of the command signal amplitude sequence is used as the prominence of each peak value in the command signal amplitude sequence.

[0015] Preferably, the process for obtaining the motion gain characteristic values ​​of various command signals in each acquisition cycle is as follows: In the formula, Let be the motion gain characteristic value of the j-th type of command signal in the t-th sampling period. Let represent the dispersion of the peak prominence in the j-th command signal amplitude sequence during the t-th sampling period. Let be the mean of the peak prominence values ​​in the amplitude sequence of the j-th command signal during the t-th sampling period. To avoid constants with a denominator of 0.

[0016] Preferably, the average of the high-frequency motion suppression degree of each acquisition cycle and the motion gain characteristic value of various command signals in each acquisition cycle is used as the scaling characteristic value of various command signals in each acquisition cycle.

[0017] Preferably, the time-varying scaling factors of various instruction signals in the classic washing algorithm are adjusted, specifically as follows: In the formula, This represents the time-varying scaling factor for the j-th type of command signal in the next acquisition cycle of the current acquisition cycle. This is the time-varying scaling factor preset in the classic washing algorithm. It is the difference between the scaling feature value of the j-th type of instruction signal in the current acquisition cycle and that in the previous acquisition cycle.

[0018] This application also provides a vehicle driving simulation system suitable for road condition simulation testing, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described vehicle driving simulation methods suitable for road condition simulation testing.

[0019] As can be seen from the above, the vehicle driving simulation system and method for road condition simulation testing provided in this application have at least the following beneficial effects:

[0020] This application utilizes the high and low frequency motion characteristics of a six-degree-of-freedom motion platform by tilting and varying, to more fully explore the segmented motion characteristics of the six-degree-of-freedom motion platform. It also accurately analyzes and measures the degree of suppression of the high-frequency motion characteristics of the six-degree-of-freedom motion platform, thus more clearly demonstrating the realism of the actual motion of the six-degree-of-freedom motion platform, which can be used to improve the realism of vehicle driving simulation in the future.

[0021] Furthermore, this application precisely measures and analyzes the gain of motion perception in vehicle driving simulation, and combines the high-frequency motion suppression characteristics of the six-degree-of-freedom motion platform with the motion gain characteristics of various command signals to comprehensively measure the scaling processing characteristics of various command signals, in order to improve the accuracy of the classical washing algorithm in scaling the input command signals.

[0022] In this application, the time-varying scaling factor preset in the classic washing algorithm is accurately adjusted in real time by the difference in the scaling feature value of various command signals in the current acquisition period. This avoids the problem of the time-varying scaling factor being too large or too small in the classic washing algorithm, improves the realism of vehicle driving simulation, and ensures the stability of the six-degree-of-freedom motion platform, so as to avoid affecting the experience of the user in the vehicle driving simulation process. Attached Figure Description

[0023] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating the steps of the vehicle driving simulation method for road condition simulation testing provided in this application. Detailed Implementation

[0025] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the vehicle driving simulation system and method suitable for road condition simulation testing proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0026] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0027] The following description, in conjunction with the accompanying drawings, details the specific scheme of the vehicle driving simulation system and method applicable to road condition simulation testing provided in this application.

[0028] Please see Figure 1 It illustrates a flowchart of a vehicle driving simulation method for road condition simulation testing provided in an embodiment of this application, including the following steps:

[0029] Step 1: Obtain the amplitude of various command signals of the simulated driver's operation behavior, including the amplitude of the command signal for linear acceleration and the amplitude of the command signal for angular acceleration, and obtain the tilt angle data of the six-degree-of-freedom motion platform.

[0030] A simulated driving scenario for road condition simulation testing is constructed using ADS simulation software (Advanced Design System). The road condition simulation of the simulated driving scenario can be a highway tunnel, a series of curves, a muddy road section, or a water crossing, etc. In this embodiment, ADS simulation software is used to simulate the road condition of a highway tunnel to obtain the simulated driving scenario for road condition simulation testing. The use of ADS simulation software to construct the simulated driving scenario is a well-known technology, and the specific process will not be described in detail.

[0031] Furthermore, in the simulated driving scenario of road condition simulation test, vehicle driving simulation is carried out through a six-degree-of-freedom motion platform. The six-degree-of-freedom motion platform is a load-bearing platform, which is supported by 6 electric cylinders. The load-bearing platform and the piston rod end of the electric cylinder are connected by 6 sets of Hooke hinges. The cylinder body end of the electric cylinder is also connected to the lower platform by 6 sets of Hooke hinges. The 6 electric cylinders are driven by servo motors.

[0032] In this embodiment, during the vehicle driving simulation using a six-degree-of-freedom motion platform, the signal acquisition terminal of the six-degree-of-freedom motion platform acquires the amplitude of various command signals of the simulated driver's operation behavior, including the amplitude of the command signal of linear acceleration corresponding to the vehicle throttle and the amplitude of the command signal of angular acceleration corresponding to the steering wheel during the simulation, and the tilt angle data of the six-degree-of-freedom motion platform is acquired through an inertial measurement unit (IMU).

[0033] To improve the realism of vehicle driving simulation, the data acquisition frequency in this embodiment is set to 1kHz and the acquisition period is 0.2s. The amplitude values ​​of various command signals and tilt angle data acquired in each acquisition period are normalized and arranged in chronological order to obtain the amplitude sequence of each command signal and the tilt angle sequence of each acquisition period.

[0034] Step 2: Extract the high-frequency and low-frequency sets of the tilt angle data in the frequency domain based on the frequency change characteristics of the tilt angle data in each acquisition cycle. Then, obtain the high-frequency motion suppression degree of each acquisition cycle by combining the low-frequency proportion in each acquisition cycle and the average level of the frequency corresponding amplitude in the low-frequency set of the tilt angle with the degree of change and the average level of the frequency corresponding amplitude in the high-frequency set of the tilt angle.

[0035] To more accurately calculate the target posture of a six-degree-of-freedom motion platform, it is necessary to fully explore the segmented motion characteristics of the six-degree-of-freedom motion platform, thereby more accurately scaling the input signal of the classic washing algorithm and improving the experience of the simulated driver during vehicle simulation.

[0036] Therefore, in order to fully explore the segmented motion characteristics of the six-degree-of-freedom motion platform, the tilt angle sequence of each acquisition cycle is used as the input of the Fourier transform. The Fourier transform can be either a discrete Fourier transform or a fast Fourier transform. In this embodiment, the fast Fourier transform is used to extract the amplitude spectrum of the tilt angle sequence, and the set of all frequency values ​​with amplitudes not equal to 0 in the amplitude spectrum is used as the tilt angular frequency set of each acquisition cycle, reflecting the magnitude of all changing frequencies in the actual tilt motion of the six-degree-of-freedom motion platform.

[0037] Furthermore, in order to analyze the low-frequency and high-frequency segmented motion characteristics of the six-degree-of-freedom motion platform, the set of tilt angle frequencies in each acquisition cycle is used as the input of the maximum inter-class variance algorithm. The maximum inter-class variance algorithm is used to obtain the segmentation threshold. The set of all frequencies in the tilt angle frequency set that are greater than the segmentation threshold is denoted as the high-frequency tilt angle set of each acquisition cycle, and the set of all frequencies in the tilt angle frequency set that are less than or equal to the segmentation threshold is denoted as the low-frequency tilt angle set of each acquisition cycle. The Fourier transform and the maximum inter-class variance algorithm are well-known techniques, and the specific process will not be described in detail.

[0038] In general, when using time-varying scaling factors to scale the input signal in classic washing algorithms, the time-varying scaling factors should not be too large or too small. If the time-varying scaling factor is too large, it will reduce the stability of the six-degree-of-freedom motion platform, which may cause drastic changes in the six-degree-of-freedom motion platform and lead to the risk of the six-degree-of-freedom motion platform bottoming out. Conversely, if the time-varying scaling factor is too small, it will seriously reduce the realism of vehicle driving simulation and affect the experience of the user in the vehicle driving simulation process.

[0039] Therefore, in order to accurately measure the high and low frequency motion characteristics of the six-degree-of-freedom motion platform, in this embodiment, the amplitudes corresponding to all frequencies in the high-frequency set of the tilt angle sequence are arranged in ascending order of frequency to obtain the high-frequency amplitude change sequence for each acquisition period. If the fluctuation of amplitude changes in the high-frequency amplitude change sequence is smaller, and the average amplitude level in the high-frequency amplitude change sequence is lower, while the proportion of low frequencies is larger and the average level of low-frequency amplitude is higher, then the high degree of suppression of the high-frequency motion characteristics of the six-degree-of-freedom motion platform is greater, which will seriously affect the realism of the vehicle driving simulation.

[0040] Based on the above analysis, the high-frequency motion suppression degree for each acquisition cycle is calculated: In the formula, Let be the high-frequency motion suppression degree in the t-th acquisition cycle. Here is the range normalization function. Let be the proportion of low-frequency distribution in the t-th acquisition cycle. Let be the average amplitude of all frequencies in the low-frequency set of the tilt angle during the t-th acquisition period. Let be the mean of the absolute values ​​of all elements in the first-order difference sequence of the high-frequency amplitude variation sequence in the t-th acquisition period. Let be the mean value of the elements in the high-frequency amplitude variation sequence during the t-th acquisition period. To avoid constants with a denominator of 0, the value is taken within a small data range (0.01, 0.1), and its impact on the calculation result is negligible. In this embodiment, the value is taken as 0.05. The acquisition of the first-order difference sequence is a well-known technique, and the specific process will not be described in detail.

[0041] It should be noted that, in this embodiment, the method for calculating the proportion of low-frequency distribution in the t-th acquisition period is to divide the number of elements in the low-frequency set of the tilt angle in the t-th acquisition period by the number of elements in the high-frequency set of the tilt angle. To avoid the denominator being zero during the ratio calculation, a constant is added to the denominator in this embodiment to prevent it from being zero; in this embodiment, the value is 0.05.

[0042] Based on the above process, it can be understood that the high-frequency motion suppression degree reflects the degree to which the high-frequency motion characteristics of the six-degree-of-freedom motion platform are suppressed. The greater the high-frequency motion suppression degree, the greater the degree to which the high-frequency motion characteristics of the six-degree-of-freedom motion platform are suppressed, resulting in a worse experience of motion perception for the simulated driver. In this case, the size of the time-varying scaling factor in the classic washout algorithm should be increased to improve the realism of the vehicle simulation driving.

[0043] Step 3: Obtain the peak salience by simulating the peak significance of various command signals in the amplitude of various commands. Based on the dispersion and average level of the peak salience, obtain the motion gain characteristic value of various command signals in each acquisition period. Combined with the high-frequency motion suppression degree, obtain the scaling characteristic value of various command signals. Adjust the time-varying scaling coefficient of various command signals in the classic washing algorithm by the degree of change of the scaling characteristic value of various command signals.

[0044] Meanwhile, in order to accurately determine the time-varying scaling factor in the classical washing algorithm, the amplitude sequence of each command signal in each acquisition cycle is used as the input of the AMPD (Automatic Multiscale Peak Detection) algorithm. The AMPD algorithm obtains all the peaks in each command signal amplitude sequence, and the ratio of each peak in each command signal amplitude sequence to the mean of the command signal amplitude sequence is recorded as the salience of each peak in each command signal amplitude sequence. The AMPD algorithm is a well-known technology, and the specific process will not be described in detail.

[0045] Generally, the smaller the dispersion of all peak salience in various command signals used by a driver to perform operations, and the smaller the average level of all peak salience, the weaker the change in signal amplitude in various command signals. In order to simulate sufficient motion perception in vehicle driving simulation, the time-varying scaling factor in the classic washout algorithm should be increased to avoid affecting the motion experience of the user during vehicle driving simulation.

[0046] Based on the above analysis, the motion gain characteristic values ​​of various command signals in each sampling period are calculated:

[0047] In the formula, Let be the motion gain characteristic value of the j-th type of command signal in the t-th sampling period. Let represent the dispersion of the peak prominence in the j-th command signal amplitude sequence during the t-th sampling period. It is the mean of the peak prominence in the amplitude sequence of the j-th command signal in the t-th sampling period.

[0048] The dispersion can be measured by variance or standard deviation. In this embodiment, standard deviation is used to measure the dispersion.

[0049] Understandably, the motion gain eigenvalue reflects the degree of gain in motion perception during vehicle driving simulation. The larger the motion gain eigenvalue, the weaker the change in signal amplitude among various command signals. In order to simulate sufficient motion perception in vehicle driving simulation, the time-varying scaling factor in the classic washout algorithm should be increased to improve the realism of vehicle driving simulation.

[0050] Therefore, the stronger the suppression of the high-frequency motion characteristics of the six-degree-of-freedom motion platform, and the weaker the changes in signal amplitude among various command signals, the more the time-varying scaling factor in the classical washout algorithm should be increased to improve the realism of the vehicle driving simulation. Thus, to accurately determine the time-varying scaling factor in the classical washout algorithm, the average of the high-frequency motion suppression degree and the motion gain characteristic values ​​of various command signals in each acquisition cycle is used as the scaling characteristic value of various command signals in each acquisition cycle. The scaling characteristic value reflects the scaling processing of various command signals in the vehicle driving simulation. The larger the scaling characteristic value, the less the high-frequency motion characteristics of the six-degree-of-freedom motion platform can be reflected, affecting the simulated driver's perception of the vehicle's motion. Therefore, the time-varying scaling factor in the classical washout algorithm should be appropriately increased.

[0051] Therefore, by referring to the scaling characteristic values ​​of various command signals in the previous acquisition cycle of the current acquisition cycle, if the scaling characteristic values ​​of various command signals in the current acquisition cycle increase, it indicates that it is difficult to reflect the high-frequency motion characteristics of the six-degree-of-freedom motion platform. In this case, the time-varying scaling factor in the classic washing algorithm should be appropriately increased to improve the realism of vehicle driving simulation.

[0052] Conversely, if the scaling characteristic values ​​of various command signals in the current acquisition cycle decrease, it indicates that the high-frequency motion characteristics of the six-degree-of-freedom motion platform are more prominent at this time. In order to avoid the risk of the six-degree-of-freedom motion platform bottoming out due to drastic changes, the time-varying scaling factor in the classic washing algorithm should be appropriately reduced to improve the stability of the six-degree-of-freedom motion platform.

[0053] Therefore, based on the above real-time analysis, the time-varying scaling factors of various command signals for the next acquisition cycle in the current acquisition cycle are calculated: In the formula, This represents the time-varying scaling factor for the j-th type of command signal in the next acquisition cycle of the current acquisition cycle. The time-varying scaling factor is a preset value in the classic washing algorithm. The preset range of the time-varying scaling factor is (0.6, 0.7). In this embodiment, the preset value of the time-varying scaling factor is 0.65. It is the difference between the scaling feature value of the j-th type of instruction signal in the current acquisition cycle and that in the previous acquisition cycle.

[0054] According to the above process in this embodiment, by calculating the scaling feature values ​​of various command signals in each acquisition cycle, the preset time-varying scaling factor in the classical washout algorithm is adjusted to avoid the problem of the time-varying scaling factor of the classical washout algorithm being too large or too small, thereby improving the realism of vehicle driving simulation and ensuring the stability of the six-degree-of-freedom motion platform.

[0055] Step 4: Using the classic washing algorithm and combined with the inverse kinematics algorithm of the six-degree-of-freedom motion platform, the target extension amount of each electric cylinder of the six-degree-of-freedom motion platform at the next moment is obtained, thereby realizing the attitude control of the six-degree-of-freedom motion platform.

[0056] Furthermore, a classical washout algorithm is employed to wash out the simulated vehicle motion. Various command signals and their time-varying proportional coefficients from the real-time acquisition of simulated driver actions are used as input to the classical washout algorithm to obtain the target attitude of the six-degree-of-freedom motion platform at the next moment. Then, the target attitude of the six-degree-of-freedom motion platform at the next moment is used as input to a six-degree-of-freedom motion platform pose inverse kinematics (IKK) algorithm. The IKK algorithm solves for the target extension amount of each electric cylinder at the next moment and transmits this extension amount to the servo controller. The servo controller drives the servo motors to control the extension amount of each electric cylinder in the six-degree-of-freedom motion platform. The specific control process is prior art known to those skilled in the art, and this embodiment does not impose any special limitations on it. Based on the above process of this embodiment, attitude control of the six-degree-of-freedom motion platform during vehicle driving simulation can be achieved.

[0057] Among them, the classic washing algorithm and the six-degree-of-freedom motion platform pose inverse solution algorithm are well-known technologies, and the specific process will not be described in detail.

[0058] Based on the same inventive concept as the above method, this application also provides a vehicle driving simulation system suitable for road condition simulation testing, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described vehicle driving simulation methods suitable for road condition simulation testing.

[0059] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0061] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A vehicle driving simulation method suitable for road condition simulation testing, characterized in that, Includes the following steps: Acquire the amplitude values ​​of various command signals for the simulated driver's operating behavior, including the amplitude values ​​of command signals for linear acceleration and angular acceleration, and acquire the tilt angle data of the six-degree-of-freedom motion platform; Based on the frequency variation characteristics of the tilt angle data in the frequency domain within each acquisition cycle, high-frequency and low-frequency sets of the tilt angle for each acquisition cycle are extracted. Then, by combining the low-frequency proportion within each acquisition cycle and the average level of the amplitude corresponding to the frequency in the low-frequency set of the tilt angle with the degree of change and the average level of the amplitude corresponding to the frequency in the high-frequency set of the tilt angle, the high-frequency motion suppression degree of each acquisition cycle is obtained. By simulating the peak significance of various command signals in the amplitude of various command signals, the salience of each peak is obtained. Based on the dispersion and average level of the peak salience, the motion gain characteristic value of various command signals in each acquisition period is obtained. Combined with the high-frequency motion suppression degree, the scaling characteristic value of various command signals is obtained. The time-varying scaling coefficient of various command signals in the classic washing algorithm is adjusted by the degree of change of the scaling characteristic value of various command signals. By using the classic washout algorithm and combining it with the inverse kinematics algorithm of a six-degree-of-freedom motion platform, the target extension amount of each electric cylinder of the six-degree-of-freedom motion platform at the next moment is obtained, thereby realizing the attitude control of the six-degree-of-freedom motion platform; The process for obtaining the high-frequency motion suppression degree in each acquisition cycle is as follows: In the formula, Let be the high-frequency motion suppression degree in the t-th acquisition cycle. Here is the range normalization function. Let be the proportion of low-frequency distribution in the t-th acquisition cycle. Let be the average amplitude of all frequencies in the low-frequency set of the tilt angle during the t-th acquisition period. To avoid constants with a denominator of 0, Let be the mean of the absolute values ​​of all elements in the first-order difference sequence of the high-frequency amplitude variation sequence in the t-th acquisition period. Let be the mean of the elements in the high-frequency amplitude change sequence of the t-th acquisition period, where the amplitudes corresponding to all frequencies in the high-frequency set of tilt angle are arranged in ascending order of frequency to form the high-frequency amplitude change sequence of each acquisition period; The normalized results of the amplitudes of various command signals in each acquisition cycle are arranged in time sequence to form the amplitude sequence of each command signal in each acquisition cycle. The process for obtaining the motion gain characteristic values ​​of various command signals in each acquisition cycle is as follows: In the formula, Let be the motion gain characteristic value of the j-th type of command signal in the t-th sampling period. Let represent the dispersion of the peak prominence in the amplitude sequence of the j-th command signal during the t-th sampling period. Let be the mean of the peak prominence values ​​in the amplitude sequence of the j-th command signal during the t-th sampling period. To avoid constants with a denominator of 0.

2. The vehicle driving simulation method for road condition simulation testing as described in claim 1, characterized in that, After normalizing the tilt angle data in each acquisition period, the data are arranged in time sequence to form the tilt angle sequence of each acquisition period. The amplitude spectrum of the tilt angle sequence is extracted by frequency domain transformation, and all frequency values ​​with non-zero amplitude in the amplitude spectrum are used to form the tilt angle frequency set of each acquisition period.

3. The vehicle driving simulation method for road condition simulation testing as described in claim 2, characterized in that, The tilt angle frequency set of each acquisition cycle is segmented by a threshold. All frequencies in the tilt angle frequency set that are greater than the segmentation threshold are formed into the high-frequency tilt angle set of each acquisition cycle, and all frequencies that are less than or equal to the segmentation threshold are formed into the low-frequency tilt angle set of each acquisition cycle.

4. The vehicle driving simulation method for road condition simulation testing as described in claim 1, characterized in that, The ratio of the number of elements in the low-frequency set of the tilt angle to the number of elements in the high-frequency set of the tilt angle in each acquisition cycle is taken as the proportion of low-frequency distribution in each acquisition cycle.

5. The vehicle driving simulation method for road condition simulation testing as described in claim 1, characterized in that, Extract the peak values ​​from each command signal amplitude sequence, and use the ratio of each peak value to the mean of the command signal amplitude sequence as the prominence of each peak value in the command signal amplitude sequence.

6. The vehicle driving simulation method for road condition simulation testing as described in claim 1, characterized in that, The average of the high-frequency motion suppression degree of each acquisition cycle and the motion gain characteristic value of various command signals in each acquisition cycle is used as the scaling characteristic value of various command signals in each acquisition cycle.

7. The vehicle driving simulation method for road condition simulation testing as described in claim 1, characterized in that, The time-varying scaling factors for various command signals in the classic washing algorithm are adjusted as follows: In the formula, This represents the time-varying scaling factor for the j-th type of command signal in the next acquisition cycle of the current acquisition cycle. This is the time-varying scaling factor preset in the classic washing algorithm. It is the difference between the scaling feature value of the j-th type of instruction signal in the current acquisition cycle and that in the previous acquisition cycle.

8. A vehicle driving simulation system suitable for road condition simulation testing, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle driving simulation method for road condition simulation testing as described in any one of claims 1-7.

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