Vehicle driving simulation system and method suitable for road condition simulation test

Abstract

The invention relates to the technical field of road condition simulation driving simulation, in particular to a vehicle driving simulation system and method suitable for a road condition simulation test, and the method comprises the steps: obtaining various instruction signal amplitudes for simulating the operation behavior of a driver and the inclination angle data of a six-degree-of-freedom motion platform; extracting inclination angle high-frequency and low-frequency sets, and obtaining high-frequency motion inhibition degrees of all acquisition periods; calculating a motion gain characteristic value of each instruction signal, obtaining a scaling characteristic value of each instruction signal in combination with the high-frequency motion suppression degree, and adjusting a time-varying proportionality coefficient of each instruction signal in a classical washout algorithm; and the target extension amount of each electric cylinder of the six-degree-of-freedom motion platform is obtained by utilizing a classic washout algorithm and combining a six-degree-of-freedom motion platform pose inverse solution algorithm, and the pose of the six-degree-of-freedom motion platform is controlled. According to the invention, the vehicle driving simulation precision can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of road condition simulation driving simulation technology, in particular to a vehicle driving simulation system and method suitable for road condition simulation test. BACKGROUND

[0002] Driving test is the core link of road test, but there are many limitations in carrying out on-site driving test, such as high scene test cost, few covered working conditions, high test risk, etc. Under this background, driving simulation test becomes an effective alternative to on-site driving test. The core of the alternative is the restoration degree of real driving experience. In order to truly simulate the attitude appearing in vehicle driving, a six-degree-of-freedom motion platform is generally used to simulate vehicle driving, to realize the motion of six degrees of freedom, including rotation in three directions of pitch, yaw and roll, and linear motion in three directions of lateral, longitudinal and vertical. The six-degree-of-freedom motion platform uses a classic washout algorithm to perform washout operation on vehicle simulation driving, to realize the solution of the target attitude of the six-degree-of-freedom motion platform at the next moment, and to solve the target extension amount of each electric cylinder by using a six-degree-of-freedom motion platform pose inverse algorithm, and then to drive the servo motor to work by using a servo controller, to control the extension amount of each electric cylinder in the six-degree-of-freedom motion platform, to realize the attitude control of the six-degree-of-freedom motion platform.

[0003] In the classic washout algorithm, a time-varying proportional coefficient is generally used to scale the input signal to improve the fidelity of vehicle driving simulation, while avoiding the six-degree-of-freedom motion platform from being violently changed and causing the six-degree-of-freedom motion platform to hit the bottom. However, in the traditional classic washout algorithm, the time-varying proportional coefficient generally only considers the amplitude change of the input signal for self-adaptation, without fully exploiting the segmented motion characteristics of the six-degree-of-freedom motion platform, resulting in that the input signal of the classic washout algorithm cannot be accurately scaled, the solution accuracy of the target attitude of the six-degree-of-freedom motion platform is low, and the experience of the experimenter in the vehicle simulation driving process is affected. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide a vehicle driving simulation system and method suitable for road condition simulation test, and the technical solution adopted is as follows: The embodiment of the present application provides a vehicle driving simulation method suitable for road condition simulation test, comprising the following steps: Obtain the amplitude of various instruction signals of the operation behavior of the simulation driving personnel, including the amplitude of the linear acceleration instruction signal and the amplitude of the angular acceleration instruction signal, and obtain the inclination 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.

[0005] 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.

[0006] 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 tilt angle high frequency set of each acquisition cycle, and all frequencies that are less than or equal to the segmentation threshold are formed into the tilt angle low frequency set of each acquisition cycle.

[0007] 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.

[0008] 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.

[0009] 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.

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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.

[0014] 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: 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. 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. 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

[0015] 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.

[0016] Figure 1 A flowchart illustrating the steps of the vehicle driving simulation method for road condition simulation testing provided in this application. Detailed Implementation

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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: 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.

[0021] 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.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Based on the above analysis, the motion gain characteristic values ​​of various command signals in each sampling period are calculated: 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.

[0038] The dispersion can be measured by variance or standard deviation. In this embodiment, standard deviation is used to measure the dispersion.

[0039] 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.

[0040] 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.

[0041] 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. 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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 method for vehicle driving simulation suitable for road condition simulation test, characterized in that, The method comprises the following steps: Obtaining various instruction signal amplitudes of the simulated driver's operation behavior, including linear acceleration instruction signal amplitudes and angular acceleration instruction signal amplitudes, and obtaining the tilt angle data of the six-degree-of-freedom motion platform; According to the frequency variation characteristics of the tilt angle data in the frequency domain in each collection period, tilt angle high-frequency and low-frequency sets of each collection period are extracted, and the low-frequency distribution ratio of each collection period is obtained by the low-frequency proportion in each collection period and the average level of the frequency corresponding amplitudes in the tilt angle low-frequency set, in combination with the variation degree and average level of the frequency corresponding amplitudes in the tilt angle high-frequency set. The peak prominence of each peak value is obtained by the peak value prominence degree in the various instruction signal amplitudes of the simulated driver's operation behavior, the motion gain characteristic value of each type of instruction signal in each collection period is obtained according to the discrete degree and average level of the peak value prominence, and the scaling characteristic value of each type of instruction signal is obtained in combination with the high-frequency motion suppression system, and the time-varying proportional coefficient of each type of instruction signal in the classical washout algorithm is adjusted by the variation degree of the scaling characteristic value of each type of instruction signal. The target extension amount of each electric cylinder of the six-degree-of-freedom motion platform at the next moment is obtained by using the classical washout algorithm and combining the six-degree-of-freedom motion platform pose inverse solution algorithm, and the attitude control of the six-degree-of-freedom motion platform is realized.

2. The vehicle driving simulation method suitable for road condition simulation test according to claim 1, wherein, After normalizing the tilt angle data in each collection period, the tilt angle sequence of each collection period is arranged in time sequence, the amplitude spectrum of the tilt angle sequence is extracted by frequency domain transformation, and all frequency values with amplitude not equal to 0 in the amplitude spectrum form the tilt angle frequency set of each collection period.

3. The vehicle driving simulation method suitable for road condition simulation test according to claim 2, wherein, The tilt angle frequency set of each collection period is threshold segmented respectively, all frequencies greater than the segmentation threshold in the tilt angle frequency set form the tilt angle high-frequency set of each collection period, and all frequencies less than or equal to the segmentation threshold form the tilt angle low-frequency set of each collection period.

4. The vehicle driving simulation method suitable for road condition simulation test according to claim 2, wherein, The acquisition process of the high-frequency motion suppression degree of each acquisition cycle is: ; in the formula, is the high-frequency motion suppression degree of the tth acquisition cycle, is a range normalization function, is the low-frequency distribution proportion of the tth acquisition cycle, is the mean value of the amplitudes of all frequencies in the tilt angle low-frequency set of the tth acquisition cycle, is a constant to avoid the denominator being 0, is the mean value of the absolute values of all elements in the first-order difference sequence of the high-frequency amplitude change sequence of the tth acquisition cycle, is the mean value of the elements in the high-frequency amplitude change sequence of the tth acquisition cycle, wherein the amplitudes corresponding to all frequencies in the tilt angle high-frequency set are arranged in the high-frequency amplitude change sequence of each acquisition cycle in ascending order of frequency.

5. The vehicle driving simulation method suitable for road condition simulation test according to claim 4, wherein, The ratio of the number of elements in the tilt angle low-frequency set of each collection period to the number of elements in the tilt angle high-frequency set is the low-frequency distribution ratio of each collection period.

6. The vehicle driving simulation method suitable for road condition simulation test according to claim 1, wherein, The normalized results of the various instruction signal amplitudes in each collection period are arranged in time sequence respectively to form the instruction signal amplitude sequence of each collection period, the peaks in the instruction signal amplitude sequence are extracted, and the ratio of each peak value in the instruction signal amplitude sequence to the average value of the instruction signal amplitude sequence is taken as the peak prominence of the instruction signal amplitude sequence.

7. The vehicle driving simulation method suitable for road condition simulation test according to claim 6, wherein, The acquisition process of the motion gain characteristic value of each type of instruction signal in each acquisition cycle is as follows: ; in the formula, is the motion gain characteristic value of the jth type of instruction signal in the tth sampling cycle, is the dispersion degree of all peak prominence degrees in the jth instruction signal amplitude sequence in the tth sampling cycle, is the mean value of all peak prominence degrees in the jth instruction signal amplitude sequence in the tth sampling cycle, is a constant for avoiding the denominator being 0.

8. The vehicle driving simulation method suitable for road condition simulation test according to Claim 1, wherein, The average value of the high-frequency motion suppression system of each collection period and the motion gain characteristic value of each type of instruction signal of each collection period is taken as the scaling characteristic value of each type of instruction signal of each collection period.

9. The vehicle driving simulation method suitable for road condition simulation test according to Claim 1, wherein, Adjusting time-varying proportional coefficients of various instruction signals in the classical elution algorithm, specifically: ; in the formula, is the time-varying proportional coefficient of the jth instruction signal in the next acquisition cycle of the current acquisition cycle, is the preset time-varying proportional coefficient in the classical elution algorithm, is the difference between the scaling characteristic values of the jth instruction signal in the current acquisition cycle and the previous acquisition cycle.

10. A vehicle driving simulation system suitable for road condition simulation test, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the vehicle driving simulation method suitable for road simulation test according to any one of claims 1-9.

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