Method, device and equipment for determining active suspension control parameters of a vehicle

Abstract

The application relates to a method, device, equipment, readable storage medium and program product for determining an active suspension control parameter of a vehicle. The method comprises: acquiring first data collected by a first sensor arranged on a target vehicle; acquiring second data collected by a second sensor arranged on the target vehicle; determining driving style information of a target user driving the target vehicle and road elevation data of a road on which the target vehicle travels according to the first data and the second data; determining an expected suspension control parameter of the target vehicle according to the driving style information and the road elevation data; acquiring a current suspension control parameter of the target vehicle; and determining a suspension control parameter of the target vehicle in each unit time within a transition time according to the current suspension control parameter and the expected suspension control parameter, wherein the transition time refers to a time during which the target vehicle changes from the current suspension control parameter to the expected suspension control parameter. The method can improve the working efficiency of the active suspension.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining active suspension control parameters of a vehicle. Background Technology

[0002] With the rapid development of intelligent driving and electric chassis technologies, traditional passive suspension systems can no longer simultaneously meet the dual demands of "high maneuverability" and "high comfort." Active suspension systems have become a research hotspot due to their ability to adjust damping / stiffness in real time. An active suspension system is an intelligent chassis system that can actively adjust suspension stiffness, damping coefficient, or vehicle height based on vehicle driving conditions, road conditions, and driver operation.

[0003] In existing technologies, most methods determine the control parameters of the active suspension based on a preset control strategy, and then control the operation of the active suspension based on the control parameters of the active suspension.

[0004] However, the accuracy of the control parameters of the active suspension determined by the preset control strategy is poor, and directly controlling the active suspension based on the determined control parameters can easily cause user discomfort or transient instability of the chassis, thus resulting in low working efficiency of the active suspension. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining the active suspension control parameters of a vehicle, which can effectively improve the working efficiency of the active suspension, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for determining active suspension control parameters for a vehicle, including:

[0007] Acquire first data collected by a first sensor installed on the target vehicle, the first sensor including at least one of a steering wheel angle sensor, an accelerator pedal position sensor and a brake pressure sensor;

[0008] Acquire second data collected by a second sensor installed on the target vehicle, the second sensor including at least one of a camera device, a lidar device, and a millimeter-wave radar device;

[0009] Based on the first and second data, determine the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is driving, and determine the desired suspension control parameters of the target vehicle based on the driving style information and the road surface elevation data.

[0010] Obtain the current suspension control parameters of the target vehicle, and determine the suspension control parameters of the target vehicle in each unit time during the transition time based on the current suspension control parameters and the desired suspension control parameters. The transition time refers to the time it takes for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters.

[0011] In one embodiment, determining the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle travels, based on the first data and the second data, includes:

[0012] The target feature vector is determined based on the first data, and the driving style information of the target user of the target vehicle is determined based on the target feature vector using the Bayesian update algorithm.

[0013] The target feature vector includes at least one of the following: steering wheel angular velocity entropy, mean accelerator pedal aggression, mean brake pedal aggression, peak lateral acceleration, standard deviation of longitudinal acceleration, and vehicle speed variation coefficient.

[0014] The road surface elevation data of the road surface on which the target vehicle travels is determined based on the second data.

[0015] In one embodiment, the desired suspension control parameters for the target vehicle are determined based on driving style information and road surface elevation data, including:

[0016] The maximum amplitude is determined based on road surface elevation data. The maximum amplitude is used to characterize the maximum height difference of road surface undulation.

[0017] The dominant wavelength is determined based on road surface elevation data. The dominant wavelength is used to characterize the period length of road surface undulations.

[0018] The excitation intensity coefficient is determined based on the maximum amplitude and the dominant wavelength, and the desired suspension control parameters are determined based on the excitation intensity coefficient, the dominant wavelength, and driving style information.

[0019] In one embodiment, the desired suspension control parameters are determined based on the excitation intensity coefficient, dominant wavelength, and driving style information, including:

[0020] Obtain the mapping relationship list of driving style information - dominant wavelength - excitation intensity coefficient. The mapping relationship list of driving style information - dominant wavelength - excitation intensity coefficient is used to indicate the mapping relationship between different driving style information, different dominant wavelengths, different excitation intensity coefficients and different desired suspension control parameters.

[0021] Using the mapping relationship list of driving style information-dominant wavelength-excitation intensity coefficient, the initial desired suspension control parameters are determined based on the excitation intensity coefficient, dominant wavelength, and driving style information;

[0022] Obtain the preset comfort envelope equation, and determine the desired suspension control parameters based on the comfort envelope equation and the initial desired suspension control parameters.

[0023] In one embodiment, the initial desired suspension control parameters include the initial damping coefficient, the initial air spring stiffness, and the initial permissible peak vertical acceleration of the vehicle body. The desired suspension control parameters are determined based on the comfort envelope equation and the initial desired suspension control parameters, including:

[0024] The initial desired suspension control parameters are determined based on the comfort envelope equation and the initial allowable peak vertical acceleration of the vehicle body to determine whether they meet the comfort requirements.

[0025] If not, the initial permissible vertical acceleration peak value of the vehicle body is reduced by a preset ratio, and the initial damping coefficient and initial air spring stiffness are recalculated based on the reduced initial permissible vertical acceleration peak value of the vehicle body until the comfort requirements are met.

[0026] The initial permissible peak vertical acceleration of the vehicle body that meets comfort requirements, as well as the initial damping coefficient and initial air spring stiffness corresponding to the permissible peak vertical acceleration of the vehicle body that meets comfort requirements, are determined as the desired suspension control parameters.

[0027] In one embodiment, determining the suspension control parameters of the target vehicle for each unit time period during the transition time based on the current suspension control parameters and the desired suspension control parameters includes:

[0028] A parameter transition trajectory model is constructed based on the current suspension control parameters and the desired suspension control parameters, and the transition time is determined based on the current suspension control parameters and the desired suspension control parameters.

[0029] The suspension control parameters of the target vehicle are determined based on the parametric transition trajectory model and the transition time, within each unit time of the transition time.

[0030] Secondly, this application also provides a device for determining active suspension control parameters for a vehicle, comprising:

[0031] The first acquisition module is used to acquire first data collected by a first sensor installed on the target vehicle. The first sensor includes at least one of a steering wheel angle sensor, an accelerator pedal position sensor, and a brake pressure sensor.

[0032] The second acquisition module is used to acquire second data collected by a second sensor installed on the target vehicle. The second sensor includes at least one of a camera device, a lidar device, and a millimeter-wave radar device.

[0033] The first determining module is used to determine the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is driving based on the first data and the second data, and to determine the desired suspension control parameters of the target vehicle based on the driving style information and the road surface elevation data.

[0034] The second determining module is used to obtain the current suspension control parameters of the target vehicle, and determine the suspension control parameters of the target vehicle in each unit time during the transition time based on the current suspension control parameters and the desired suspension control parameters. The transition time refers to the time it takes for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the embodiments of the first aspect above.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the embodiments of the first aspect above.

[0037] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the embodiments of the first aspect above.

[0038] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining active suspension control parameters of a vehicle first acquires first data collected by a first sensor installed on the target vehicle. The first sensor includes at least one of a steering wheel angle sensor, an accelerator pedal position sensor, and a brake pressure sensor. Then, it acquires second data collected by a second sensor installed on the target vehicle. The second sensor includes at least one of a camera device, a lidar device, and a millimeter-wave radar device. Next, based on the first and second data, it determines the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is traveling. Based on the driving style information and the road surface elevation data, it determines the desired suspension control parameters of the target vehicle. Finally, it acquires the current suspension control parameters of the target vehicle and determines the suspension control parameters of the target vehicle within each unit time of the transition time, based on the current suspension control parameters and the desired suspension control parameters. The transition time refers to the time it takes for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters. The method for determining the active suspension control parameters of a vehicle provided in this application determines the suspension control parameters based on first data collected by a steering wheel angle sensor, accelerator pedal position sensor, and brake pressure sensor, and second data collected by a camera device, a lidar device, and a millimeter-wave radar device. This method takes into account road surface factors and user driving style factors, thereby improving the accuracy of the suspension control parameters. Furthermore, after determining the suspension control parameters, the method also determines the vehicle's suspension control parameters for each unit time within the transition time based on the current suspension control parameters. This avoids user discomfort or transient chassis instability caused by sudden changes in control parameters, thus effectively improving the working efficiency of the active suspension. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating a method for determining the active suspension control parameters of a vehicle in one embodiment.

[0041] Figure 2 This is a flowchart illustrating a method for determining the driving style information of a target user driving a target vehicle and the road surface elevation data of the road surface on which the target vehicle is traveling, based on first data and second data, in one embodiment.

[0042] Figure 3This is a flowchart illustrating a method for determining desired suspension control parameters of a target vehicle based on driving style information and road surface elevation data in one embodiment.

[0043] Figure 4 This is a flowchart illustrating a method for determining desired suspension control parameters based on excitation intensity coefficient, dominant wavelength, and driving style information in one embodiment.

[0044] Figure 5 This is a flowchart illustrating a method for determining desired suspension control parameters based on the comfort envelope equation and initial desired suspension control parameters in one embodiment.

[0045] Figure 6 This is a flowchart illustrating a method for determining the suspension control parameters of a target vehicle within each unit time interval during a transition time, based on the current suspension control parameters and the desired suspension control parameters, in one embodiment.

[0046] Figure 7 This is a flowchart illustrating a method for determining the active suspension control parameters of a vehicle in another embodiment;

[0047] Figure 8 This is a structural block diagram of a vehicle active suspension control parameter determination device in one embodiment;

[0048] Figure 9 This is an internal structural diagram of a computer device in one embodiment;

[0049] Figure 10 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It should be noted that the terms "comprising" and "having," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusion. The term "multiple" as used in this application refers to two or more. The term "and / or" as used in this application refers to one of the solutions, or any combination of multiple solutions.

[0052] With the rapid development of intelligent driving and electric chassis technologies, traditional passive suspension systems can no longer simultaneously meet the dual demands of "high maneuverability" and "high comfort." Active suspension systems have become a research hotspot due to their ability to adjust damping / stiffness in real time. An active suspension system is an intelligent chassis system that can actively adjust suspension stiffness, damping coefficient, or vehicle height based on vehicle driving conditions, road conditions, and driver operation.

[0053] In existing technologies, most methods determine the control parameters of the active suspension based on a preset control strategy, and then control the operation of the active suspension based on the control parameters of the active suspension.

[0054] However, the accuracy of the control parameters of the active suspension determined by the preset control strategy is poor, and directly controlling the active suspension based on the determined control parameters can easily cause user discomfort or transient instability of the chassis, thus resulting in low working efficiency of the active suspension.

[0055] In view of this, this application provides a method for determining active suspension control parameters for a vehicle. Since the suspension control parameters are determined based on first data collected by a steering wheel angle sensor, accelerator pedal position sensor, and brake pressure sensor, and second data collected by a camera device, a lidar device, and a millimeter-wave radar device, road surface factors and user driving style factors are considered, thereby improving the accuracy of the suspension control parameters. Furthermore, after determining the suspension control parameters, the method also determines the vehicle's suspension control parameters for each unit time within the transition time based on the current suspension control parameters. This avoids user discomfort or transient chassis instability caused by sudden changes in control parameters, thus effectively improving the efficiency of suspension control.

[0056] The method for determining active suspension control parameters for a vehicle provided in this application can be executed by a computer device, which can be a terminal, such as the electronic control unit of the target vehicle or an on-board intelligent computing platform. The computer device can also be a server, which can communicate with the target vehicle via a network, obtain the data required to determine the active suspension control parameters through the network, and send the determined active suspension control parameters to the target vehicle through the network to achieve control of the target vehicle.

[0057] In an optional embodiment, the active suspension control parameter determination method for a vehicle provided in this application can be executed by an active suspension domain controller, which adopts a dual-core lockstep MCU + AI acceleration unit with a main frequency of 400MHz.

[0058] In one exemplary embodiment, such as Figure 1 As shown, a method for determining active suspension control parameters of a vehicle is provided, the method comprising the following steps:

[0059] Step 101: Obtain the first data collected by the first sensor installed on the target vehicle.

[0060] The first sensor includes at least one of a steering wheel angle sensor, an accelerator pedal position sensor, and a brake pressure sensor.

[0061] Optionally, the target vehicle can be a new energy vehicle or a traditional fuel vehicle.

[0062] For example, a steering wheel angle sensor refers to a sensor installed at the steering wheel transmission mechanism of a target vehicle to monitor the steering wheel rotation angle, rotation direction, and angular velocity in real time.

[0063] An accelerator pedal position sensor is a sensor installed under the accelerator pedal of a target vehicle that can detect the travel distance and rate of change of the accelerator pedal when it is pressed.

[0064] A brake pressure sensor is a sensor installed on the brake lines or master cylinder of a target vehicle to collect hydraulic or pneumatic pressure data within the braking system, as well as the rate of pressure change.

[0065] In some exemplary embodiments, the computer device may first acquire first data collected by a first sensor mounted on the target vehicle.

[0066] Specifically, the computer device can communicate with the first sensor and acquire the first data collected by the first sensor through the communication connection. The first data can be used to characterize the driving operation characteristics of the driver corresponding to the target vehicle. In an optional embodiment, when the computer device is an active suspension domain controller, the computer device can be linked to the first sensor via a high-speed CAN-FD bus, and Ethernet 100BASE-T1 can be used as a redundant link.

[0067] When the first sensor is a steering wheel angle sensor, the first data can be the real-time steering wheel angle, the steering wheel rotation angular velocity, the steering wheel rotation direction, etc. The sampling frequency of the steering wheel angle sensor can be 100Hz.

[0068] When the first sensor is an accelerator pedal position sensor, the first data can be the real-time travel opening of the accelerator pedal, the rate of change of the accelerator pedal opening, and the absolute position of the accelerator pedal, that is, the distance from the initial position of the accelerator pedal. The sampling frequency of the accelerator pedal position sensor can be 200Hz.

[0069] When the first sensor is a brake pressure sensor, the first data can be the real-time pressure value within the braking system, the rate of change of brake pressure, etc.

[0070] Step 102: Obtain the second data collected by the second sensor installed on the target vehicle.

[0071] The second sensor includes at least one of a camera device, a lidar device, and a millimeter-wave radar device.

[0072] For example, the camera device can be a front-view camera of the target vehicle, which is installed near the rearview mirror inside the windshield of the target vehicle. It can acquire the road elevation texture within a preset distance in front of the target vehicle. The preset distance can be 20-30 meters. The resolution of the front-view camera can be 1920×1080 and the frame rate can be 60fps.

[0073] A lidar device is a sensor that detects the surrounding environment by emitting laser beams and receiving reflected signals. LiDAR devices can be installed on the roof of a target vehicle to collect high-density point cloud data to accurately reconstruct road surface undulations. This lidar device can be a 16-line lidar, that is, a lidar device that can emit 16 laser beams at the same time. The more beams there are, the higher the vertical resolution and the denser and more accurate the point cloud data.

[0074] Millimeter-wave radar equipment refers to a sensor that uses electromagnetic waves in the millimeter-wave frequency band for detection. Millimeter-wave radar equipment can be a 77GHz millimeter-wave radar. Millimeter-wave radar equipment can be set in the center of the front bumper of the target vehicle and can be used to collect information on road obstacles at night or in strong light conditions.

[0075] Optionally, the second sensor may also include a vehicle height sensor and a three-axis accelerometer and gyroscope combination unit. The vehicle height sensor comprises four units, each fixedly mounted on one of the four lower suspension arms of the target vehicle, for real-time measurement of the vertical displacement of the vehicle's wheels relative to the vehicle body. The three-axis accelerometer and gyroscope combination unit can be positioned near the target vehicle's center of gravity to collect data such as vertical acceleration and roll rate. The sampling frequency of the three-axis accelerometer and gyroscope combination unit can be 100Hz.

[0076] In some exemplary embodiments, the computer device may acquire second data collected by a second sensor mounted on the target vehicle.

[0077] Specifically, the computer device can communicate with the second sensor and acquire second data collected by the second sensor through the communication connection. The second data can be used to characterize the road condition features of the road on which the target vehicle is traveling. In an optional embodiment, when the computer device is an active suspension domain controller, the computer device can be linked to the second sensor via a high-speed CAN-FD bus, and Ethernet 100BASE-T1 can be used as a redundant link.

[0078] When the second sensor is a camera device, the second data can be road image data within a preset distance in front of the target vehicle. Specifically, it can include the position and shape of lane lines, road surface texture features, visual outlines and positions of dynamic targets (such as vehicles, pedestrians, non-motorized vehicles, etc.), visual content of traffic signs, and road surface visual environment information under current lighting and weather conditions.

[0079] When the second sensor is a lidar device, the second data can be high-density point cloud data within a preset range around the target vehicle. Specifically, it can include road elevation information, three-dimensional point cloud contours of road boundaries, three-dimensional dimensions and spatial coordinates of road obstacles, and three-dimensional point cloud models, relative positions, and motion states of dynamic targets (such as vehicles, pedestrians, non-motorized vehicles, etc.).

[0080] When the second sensor is a millimeter-wave radar device, the second data can be obstacle detection data in front of and around the target vehicle within a preset range. Specifically, it can include the relative distance, relative speed, horizontal azimuth angle, and motion state attributes of the obstacle and the target vehicle.

[0081] Step 103: Determine the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is driving based on the first data and the second data, and determine the desired suspension control parameters of the target vehicle based on the driving style information and the road surface elevation data.

[0082] Optionally, driving style information can be used to characterize the driving behavior of the target user. Driving style information can be mild, aggressive, or neutral.

[0083] Road surface elevation data can be used to characterize the vertical height changes of the road surface on which the target vehicle is traveling, and can accurately describe terrain features such as road surface undulations, slopes, and potholes.

[0084] Desired suspension control parameters refer to the suspension control parameters that the target vehicle needs to achieve. For example, desired suspension control parameters may include damping force, suspension height, air spring pressure, stabilizer bar torsional stiffness, etc.

[0085] In some exemplary embodiments, after obtaining the first data and the second data, the computer device can determine the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is traveling, based on the first data and the second data.

[0086] Specifically, computer equipment can determine the driving style information of the target user driving the target vehicle based on the first data.

[0087] For example, a computer device can input the first data into a pre-trained driving style information determination model to obtain the driving style information of the target user output by the driving style information determination model.

[0088] Specifically, the computer equipment can also determine the road surface elevation data of the road surface on which the target vehicle is traveling based on the second data.

[0089] For example, computer equipment can input second data into a pre-trained road surface elevation data determination model to obtain the road surface elevation data of the road surface on which the target vehicle travels, as output by the road surface elevation data determination model.

[0090] Furthermore, after determining the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is traveling based on the first data and the second data, the computer equipment can determine the desired suspension control parameters of the target vehicle based on the driving style information and the road surface elevation data.

[0091] Specifically, the computer equipment can input driving style information and road surface elevation data into a pre-trained desired suspension control parameter determination model to obtain the desired suspension control parameters of the target vehicle output by the desired suspension control parameter determination model.

[0092] Step 104: Obtain the current suspension control parameters of the target vehicle, and determine the suspension control parameters of the target vehicle in each unit time during the transition time based on the current suspension control parameters and the desired suspension control parameters.

[0093] The transition time refers to the time it takes for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters.

[0094] For example, if the desired suspension control parameters are determined and then directly adjusted from the current suspension control parameters to the desired suspension control parameters, it will cause a body impact. For example, a sudden increase in damping force may cause a bump, or a sudden change in suspension height may cause the body to sway. Therefore, in order to avoid the problem of body impact caused by "sudden changes" in suspension control parameters, a transition time is set to allow the active suspension to smoothly transition from the current suspension control parameters to the desired suspension control parameters.

[0095] Furthermore, the unit time refers to the minimum time interval set within the transition period to smoothly transition the current suspension control parameters to the desired suspension control parameters. In other words, the "total transition process" is divided into multiple time segments, each corresponding to a small adjustment of the suspension control parameters, avoiding a sudden jump in suspension control parameters that could cause vehicle impact.

[0096] In some exemplary embodiments, after determining the desired suspension control parameters, the computer device can obtain the current suspension control parameters of the target vehicle, and determine the suspension control parameters of the target vehicle in each unit time of the transition time based on the current suspension control parameters and the desired suspension control parameters.

[0097] Specifically, the computer equipment can first determine the transition time based on the desired suspension control parameters and the current suspension control parameters, and then determine the suspension control parameters of the target vehicle within each unit time of the transition time based on the transition time.

[0098] For example, if the transition time is 100ms and the unit time is 10ms, the damping force in the current suspension control parameters is 150N・s / m, and the desired damping force in the suspension control parameters is 80N・s / m, the total amount to be adjusted is -70N・s / m. Then, the suspension control parameters in each 10ms interval can be -7N・s / m to avoid body impact caused by sudden parameter changes.

[0099] In an optional embodiment of this application, suspension control parameters are used to control the actuator layer of the target vehicle, which may include continuously adjustable damping shock absorbers, air springs, and an active stabilizer bar. The continuously adjustable damping shock absorbers may include four units, with a valve response time of less than 6ms and a maximum damping adjustment range of [missing information]. The air springs can include up to four units, each with independently controllable height and travel. Maximum load capacity 10 bar; active lateral stabilizer bar is used to suppress roll under extreme conditions.

[0100] The method for determining the active suspension control parameters of the aforementioned vehicle first acquires first data collected by a first sensor installed on the target vehicle. The first sensor includes at least one of a steering wheel angle sensor, an accelerator pedal position sensor, and a brake pressure sensor. Then, it acquires second data collected by a second sensor installed on the target vehicle. The second sensor includes at least one of a camera device, a lidar device, and a millimeter-wave radar device. Next, based on the first and second data, it determines the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is traveling. Based on the driving style information and the road surface elevation data, it determines the desired suspension control parameters of the target vehicle. Finally, it acquires the current suspension control parameters of the target vehicle and determines the suspension control parameters of the target vehicle within each unit of time during the transition time, based on the current suspension control parameters and the desired suspension control parameters. The transition time refers to the time it takes for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters. The method for determining the active suspension control parameters of a vehicle provided in this application determines the suspension control parameters based on first data collected by a steering wheel angle sensor, accelerator pedal position sensor, and brake pressure sensor, and second data collected by a camera device, a lidar device, and a millimeter-wave radar device. This method takes into account road surface factors and user driving style factors, thereby improving the accuracy of the suspension control parameters. Furthermore, after determining the suspension control parameters, the method also determines the vehicle's suspension control parameters for each unit time within the transition time based on the current suspension control parameters. This avoids user discomfort or transient chassis instability caused by sudden changes in control parameters, thus effectively improving the working efficiency of the active suspension.

[0101] In one exemplary embodiment, such as Figure 2 As shown, determining the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is traveling, based on the first data and the second data, includes the following steps:

[0102] Step 201: Determine the target feature vector based on the first data, and use the Bayesian update algorithm to determine the driving style information of the target user of the target vehicle based on the target feature vector.

[0103] The target feature vector includes at least one of the following: steering wheel angular velocity entropy, mean accelerator pedal aggression, mean brake pedal aggression, peak lateral acceleration, standard deviation of longitudinal acceleration, and vehicle speed variation coefficient.

[0104] For example, the steering wheel angular velocity entropy can be used to characterize the steering agility of the target vehicle; the mean accelerator pedal aggression can be used to characterize the longitudinal dynamic aggression of the target vehicle; the mean brake pedal aggression can be used to characterize the deceleration aggression of the target vehicle; the peak lateral acceleration can be used to characterize cornering aggression; the standard deviation of longitudinal acceleration can be used to characterize straight-line acceleration and deceleration fluctuations; and the vehicle speed variation coefficient can be used to characterize the driving rhythm of the target vehicle.

[0105] In some exemplary embodiments, the computer device can determine a target feature vector based on first data. The target feature vector may include six dimensions: steering wheel angular velocity entropy, mean accelerator pedal aggression, mean brake pedal aggression, peak lateral acceleration, standard deviation of longitudinal acceleration, and vehicle speed variation coefficient.

[0106] Specifically, the computer equipment can take 2 seconds as an analysis frame, shift the frame by 0.5 seconds, form a real-time data stream based on the first data, and then determine a six-dimensional target feature vector based on the first data of each frame.

[0107] Furthermore, the computer equipment uses a Bayesian update algorithm to determine the driving style information of the target user for the target vehicle based on the target feature vector.

[0108] Specifically:

[0109] 1) Predefined style: The driving style information is divided into three categories, namely mild, neutral and aggressive, represented by N, M and A respectively, as the target categories for identification.

[0110] 2) Prior probability: Initially, it is assumed that the probabilities of the three driving styles are equal, that is... ,in, , and These refer to the probabilities of a mild, neutral, and aggressive driving style in the initial state.

[0111] 3) Likelihood Assumption: It is assumed that under each driving style, the characteristics of the target vehicle follow a one-dimensional Gaussian distribution, and the parameters of the distribution are... Obtained through offline calibration, meaning it is pre-trained using a large amount of driving data. The mean, The standard deviation is denoted as .

[0112] 4) Post-hoc update: ,in, Let be the posterior probability at time t under style k. is a normalization constant; k is a style; i is a target feature vector of a certain dimension; It is the value of the i-th target feature vector observed at time t; Let be the mean and standard deviation of the i-th dimension target feature vector under style k, respectively.

[0113] 5) Define the forgetting factor It can be used to adjust the weights of prior probabilities; it is optional. It can be 0.9.

[0114] 6) Likelihood function Defined as the observed feature vector of the current target under a given driving style k. The probability of the feature vector following a multivariate Gaussian distribution given style k: .

[0115] 7) Bayesian update formula: Define the forgetting factor The value is 0.9 to ensure a balance between system response speed and stability.

[0116] 8) Take the style corresponding to the maximum posterior probability: .

[0117] 9) Confidence level , .

[0118] 10) A first-order low-pass filter eliminates transient jitter, with a cutoff frequency of 0.2Hz, yielding the final output driving style information. .

[0119] In an optional embodiment of this application, If the value is less than 0.5, it can be determined as "driving style unknown", and the default driving style information can be set to neutral. If the sensor frame loss is greater than 20%, the current frame rate should be frozen. It also reports the fault code to the vehicle gateway.

[0120] Using the above method, the first style convergence can be completed within 2.5 seconds, and then the driving style information is updated every 0.5 seconds to ensure the real-time performance, accuracy and reproducibility of driving style recognition.

[0121] Step 202: Determine the road surface elevation data of the road surface on which the target vehicle is traveling based on the second data.

[0122] In some exemplary embodiments, the computer device can determine the road surface elevation data of the road surface on which the target vehicle is traveling based on the second data.

[0123] Specifically, road surface elevation data can be a height sequence, which can be obtained by projecting the 0-20m point cloud in front of the target vehicle onto the longitudinal profile of the vehicle body, with the rear axle center of the target vehicle as the origin. The height sequence can be represented as follows: , ,in, It is the road surface elevation data measured by sensors. y is the heading angle of the target vehicle, and x and y are the positions in the sensor coordinate system.

[0124] In one exemplary embodiment, such as Figure 3 As shown, the desired suspension control parameters for the target vehicle are determined based on driving style information and road surface elevation data, including the following steps:

[0125] Step 301: Determine the maximum amplitude based on the road surface elevation data.

[0126] The maximum amplitude value is used to characterize the maximum height difference of road surface undulations.

[0127] In some exemplary embodiments, after determining the road surface elevation data, the computer device can determine the maximum amplitude based on the road surface elevation data.

[0128] Specifically, the maximum amplitude can be represented as A. ,in, This is road surface elevation data.

[0129] Step 302: Determine the main wavelength based on the road surface elevation data.

[0130] The dominant wavelength is used to characterize the period length of road surface undulations.

[0131] In some exemplary embodiments, after determining the road surface elevation data, the computer device can determine the dominant wavelength based on the road surface elevation data.

[0132] Specifically, the dominant wavelength can be represented as B. ,in, For road surface elevation data, This represents the rate of change of road surface elevation data along the horizontal direction.

[0133] Step 303: Determine the excitation intensity coefficient based on the maximum amplitude and the dominant wavelength, and determine the desired suspension control parameters based on the excitation intensity coefficient, the dominant wavelength, and driving style information.

[0134] In some exemplary embodiments, after determining the maximum amplitude and the dominant wavelength, the computer device can determine the excitation intensity coefficient based on the maximum amplitude and the dominant wavelength.

[0135] Specifically, the excitation intensity coefficient can be expressed as I, I=A / B, where A is the maximum amplitude and B is the dominant wavelength.

[0136] Furthermore, after determining the excitation intensity coefficient based on the maximum amplitude and the dominant wavelength, the computer equipment can determine the desired suspension control parameters based on the excitation intensity coefficient, the dominant wavelength, and driving style information.

[0137] Specifically, the computer equipment can input the excitation intensity coefficient, dominant wavelength, and driving style information into a pre-trained desired suspension control parameter determination model to obtain the desired suspension control parameters output by the desired suspension control parameter determination model.

[0138] In an optional embodiment of this application, the computer device can also determine the temporal excitation within a preset time period based on the current speed of the target vehicle. , The preset time can be 0.8 seconds. Where v is the current speed of the target vehicle. It's the aiming time.

[0139] In one possible implementation, the computer device can also determine the desired suspension control parameters based on temporal excitation and driver style information.

[0140] In one exemplary embodiment, such as Figure 4 As shown, the desired suspension control parameters are determined based on the excitation intensity coefficient, dominant wavelength, and driving style information, including the following steps:

[0141] Step 401: Obtain the driving style information - main wavelength - excitation intensity coefficient mapping relationship list.

[0142] The driving style information-dominant wavelength-excitation intensity coefficient mapping list is used to indicate the mapping relationship between different driving style information, different dominant wavelengths, different excitation intensity coefficients and different desired suspension control parameters.

[0143] In an exemplary embodiment, the mapping relationship list of driving style information - dominant wavelength - excitation intensity coefficient can be shown in Table 1. Table 1 is a three-dimensional discrete table, where the behavioral excitation intensity coefficient I... The dominant wavelength is B. The layer is the driving style information. , .

[0144] Table 1

[0145]

[0146]

[0147]

[0148] In Table 1, C, K, and a are the desired suspension control parameters, where C is the desired damping coefficient, K is the desired air spring stiffness, and a is the allowable peak vertical acceleration of the vehicle body.

[0149] Step 402: Using the driving style information-dominant wavelength-excitation intensity coefficient mapping relationship list, determine the initial desired suspension control parameters based on the excitation intensity coefficient, dominant wavelength, and driving style information.

[0150] In some exemplary embodiments, after obtaining the driving style information-dominant wavelength-excitation intensity coefficient mapping relationship list, the computer device can use the driving style information-dominant wavelength-excitation intensity coefficient mapping relationship list to determine the initial desired suspension control parameters based on the excitation intensity coefficient, dominant wavelength, and driving style information.

[0151] Specifically, if the driving style information, dominant wavelength, and excitation intensity coefficient are determined to fall within the grid of the list, the desired suspension control parameters are determined using a trilinear interpolation algorithm. If they exceed the boundaries of the list, the desired suspension control parameters are determined by saturation processing based on the nearest boundary value.

[0152] For example, I=1.0, B=4, If the value is 0.7, then the desired suspension control parameters can be determined as C = 2300 Ns / m, K = 18000 N / m, and a = 1.0 m / s². 2 .

[0153] Step 403: Obtain the preset comfort envelope equation, and determine the desired suspension control parameters based on the comfort envelope equation and the initial desired suspension control parameters.

[0154] In some exemplary embodiments, the computer device may first obtain a preset comfort envelope equation.

[0155] Specifically, the preset comfort envelope equation can be expressed as: ,in, , The desired impact force on the human body, which is also the rate of change of vertical acceleration, This is the aiming time.

[0156] Furthermore, after obtaining the preset comfort envelope equation, the computer equipment can determine the desired suspension control parameters based on the comfort envelope equation and the initial desired suspension control parameters.

[0157] Specifically, the computer equipment can input the comfort envelope equation and the initial desired suspension control parameters into a pre-trained desired suspension control parameter determination model to obtain the desired suspension control parameters output by the desired suspension control parameter determination model.

[0158] In one exemplary embodiment, such as Figure 5 As shown, the initial desired suspension control parameters include the initial damping coefficient, initial air spring stiffness, and initial permissible peak vertical acceleration of the vehicle body. The desired suspension control parameters are determined based on the comfort envelope equation and the initial desired suspension control parameters, including the following steps:

[0159] Step 501: Determine whether the initial desired suspension control parameters meet the comfort requirements based on the comfort envelope equation and the initial allowable peak vertical acceleration of the vehicle body.

[0160] In some exemplary embodiments, after obtaining the comfort envelope equation, the computer device can determine whether the initial desired suspension control parameters meet the comfort requirements based on the comfort envelope equation and the initial permissible peak vertical acceleration of the vehicle body.

[0161] Specifically, the comfort envelope equation can be expressed as: ,and Therefore, the initial permissible peak vertical acceleration of the vehicle body can be substituted into the comfort envelope equation. By judging whether the comfort envelope equation holds, it can be determined whether the initial desired suspension control parameters meet the comfort requirements.

[0162] Step 502: If not satisfied, the initial permissible vertical acceleration peak value of the vehicle body is reduced according to a preset ratio, and the initial damping coefficient and initial air spring stiffness are recalculated based on the reduced initial permissible vertical acceleration peak value of the vehicle body until the comfort requirements are met.

[0163] In some exemplary embodiments, if the computer device determines that the initial desired suspension control parameters do not meet the comfort requirements, it can reduce the initial permissible vertical acceleration peak value of the vehicle body by a preset ratio, and recalculate the initial damping coefficient and the initial air spring stiffness based on the reduced initial permissible vertical acceleration peak value of the vehicle body until the comfort requirements are met.

[0164] Specifically, the preset ratio can be pre-set by technicians according to actual needs. The computer equipment can first reduce the initial permissible vertical acceleration peak of the vehicle body according to the preset ratio, and determine whether the initial expected suspension control parameters meet the comfort requirements based on the reduced initial permissible vertical acceleration peak. If they do, a new initial damping coefficient and initial air spring stiffness are determined based on the reduced initial permissible vertical acceleration peak. If they do not meet, the initial permissible vertical acceleration peak is further reduced according to the preset ratio until the comfort requirements are met.

[0165] Step 503: Determine the initial permissible vertical acceleration peak of the vehicle body that meets the comfort requirements, as well as the initial damping coefficient and initial air spring stiffness corresponding to the permissible vertical acceleration peak of the vehicle body that meets the comfort requirements, as the desired suspension control parameters.

[0166] In some exemplary embodiments, after determining that the comfort requirements are met, the computer device can determine the initial permissible peak vertical acceleration of the vehicle body that meets the comfort requirements, as well as the initial damping coefficient and initial air spring stiffness corresponding to the permissible peak vertical acceleration of the vehicle body that meets the comfort requirements, as the desired suspension control parameters.

[0167] In an optional embodiment of this application, if the sensor fails, it can automatically switch to traditional vehicle body sensor closed-loop PID damping control and set the driving style information to neutral. If the driving style information is less than 0.5, the most conservative grid can be used to ensure safety. The most conservative grid is... .

[0168] In an optional embodiment of this application, after the target vehicle is powered on, the computer device can check the hardware status of the first sensor and the second sensor. If the check fails, the device will not execute the process of determining the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is traveling based on the first data and the second data. Instead, it will use fixed damping and maintain the air spring at the mid-position height, illuminate the instrument fault light, and wait for repair.

[0169] In one exemplary embodiment, such as Figure 6 As shown, the suspension control parameters of the target vehicle are determined at each unit time interval during the transition time based on the current suspension control parameters and the desired suspension control parameters, including the following steps:

[0170] Step 601: Construct a parameter transition trajectory model based on the current suspension control parameters and the desired suspension control parameters, and determine the transition time based on the current suspension control parameters and the desired suspension control parameters.

[0171] In some exemplary embodiments, after determining the current suspension control parameters and the desired suspension control parameters, the computer device can construct a parameter transition trajectory model based on the current suspension control parameters and the desired suspension control parameters, and determine the transition time based on the current suspension control parameters and the desired suspension control parameters.

[0172] Specifically, the computer equipment can construct a parametric transition trajectory model using a cubic polynomial based on the current suspension control parameters and the desired suspension control parameters. The starting point of the parametric transition trajectory model is the current suspension control parameters, and the ending point is the desired suspension control parameters. The parameter change rate at the start and end times is 0.

[0173] The following explanation will take the damping coefficient, a parameter in suspension control, as an example.

[0174] The parameter transition trajectory model corresponding to the damping coefficient can be expressed as follows: ,in, This refers to the parameter transition reference trajectory corresponding to the damping coefficient. This refers to the damping coefficient in the current suspension control parameters. , , It is a normalized variable representing the relative position from the start of the transition to the current time. For the transition period, , This is the maximum permissible rate of change for the CDC valve. This refers to the damping coefficients in the desired suspension control parameters, where a, b, and c are preset coefficients determined by boundary conditions. , , The only certainty.

[0175] Based on the above method, the parametric transition trajectory models corresponding to the air spring stiffness can be obtained respectively. Transition trajectory model corresponding to the initial allowed peak vertical acceleration of the vehicle body .

[0176] Step 602: Determine the suspension control parameters of the target vehicle within each unit time during the transition time based on the parameter transition trajectory model and transition time.

[0177] In some exemplary embodiments, after determining the parameter transition trajectory model and the transition time, the computer device can determine the suspension control parameters of the target vehicle within each unit time of the transition time based on the parameter transition trajectory model and the transition time.

[0178] Specifically, the computer equipment can divide the transition time into several continuous time intervals according to a preset unit time step. Based on the parameter transition trajectory model, the suspension control parameters in each time interval are solved by a quadratic programming algorithm. The objective function of the quadratic programming algorithm includes minimizing the trajectory tracking error and smoothing the parameter change, and satisfies the parameter change rate constraint, actuator range constraint, and comfort envelope constraint.

[0179] For example, suppose the damping increment per unit time during the transition time is... Let i represent the i-th unit of time, and the objective function can be expressed as: Where d is the weighting factor, and d can be 10. -2 It can be used to suppress high-frequency jitter.

[0180] Parameter change rate constraints include rate constraints. Human safety restraints , Real-time estimation using a 1-DOF vertical model; actuator saturation constraints. The quadratic programming algorithm can handle up to 30 variables and 90 linear constraints, with a solution time of less than 2 milliseconds.

[0181] In an optional embodiment of this application, the computer device also uses timing excitation in the process of determining the suspension control parameters of the target vehicle at each unit time during the transition time.

[0182] In an optional embodiment of this application, the stability of the suspension control parameters obtained in each unit time period can be verified. If the preset stability conditions are not met, the parameters in the corresponding time interval are corrected until the suspension control parameters in all unit time periods meet the requirements.

[0183] Specifically, the vertical dynamic energy function is selected: ,in, For the sprung mass, Let be the vertical acceleration of the mass on the spring. Let z be the spring stiffness, and z be the displacement of the mass on the spring. This is the normalization coefficient, which can be 100. This is the actual damping coefficient.

[0184] Furthermore, by differentiating, we get Substitute into QP constraints It can be proven that: , That is, the system is exponentially stable during the transition process.

[0185] In one possible implementation, the computer device can also use timing excitation to verify the stability of the suspension control parameters obtained in each unit of time.

[0186] In an optional embodiment of this application, if QP does not converge within 5ms, the damping is immediately frozen to the previous valid value, and a "transition timeout" fault is reported to the vehicle gateway; if an open / short circuit of the CDC valve current is detected, the mechanical bypass valve is switched to maintain fixed damping, and the air spring height adjustment is turned off.

[0187] In one exemplary embodiment, such as Figure 7 As shown, another method for determining the active suspension control parameters of a vehicle is provided, which includes the following steps:

[0188] Step 701: Acquire first data collected by a first sensor installed on the target vehicle, the first sensor including at least one of a steering wheel angle sensor, an accelerator pedal position sensor and a brake pressure sensor; acquire second data collected by a second sensor installed on the target vehicle, the second sensor including at least one of a camera device, a lidar device and a millimeter-wave radar device;

[0189] Step 702: Determine the target feature vector based on the first data, and use the Bayesian update algorithm to determine the driving style information of the target user of the target vehicle based on the target feature vector; wherein, the target feature vector includes at least one of the following: steering wheel angular velocity entropy, mean accelerator pedal aggression, mean brake pedal aggression, peak lateral acceleration, standard deviation of longitudinal acceleration, and vehicle speed variation coefficient.

[0190] Step 703: Determine the road surface elevation data of the road surface on which the target vehicle is traveling based on the second data; determine the maximum amplitude value based on the road surface elevation data, which is used to characterize the maximum height difference of the road surface undulations; determine the dominant wavelength based on the road surface elevation data, which is used to characterize the period length of the road surface undulations.

[0191] Step 704: Determine the excitation intensity coefficient based on the maximum amplitude and the dominant wavelength, and obtain the mapping relationship list of driving style information-dominant wavelength-excitation intensity coefficient. The mapping relationship list of driving style information-dominant wavelength-excitation intensity coefficient is used to indicate the mapping relationship between different driving style information, different dominant wavelengths, different excitation intensity coefficients and different desired suspension control parameters.

[0192] Step 705: Using the driving style information-dominant wavelength-excitation intensity coefficient mapping relationship list, determine the initial desired suspension control parameters based on the excitation intensity coefficient, dominant wavelength, and driving style information; the initial desired suspension control parameters include the initial damping coefficient, initial air spring stiffness, and initial allowable peak vertical acceleration of the vehicle body;

[0193] Step 706: Obtain the preset comfort envelope equation, and determine whether the initial desired suspension control parameters meet the comfort requirements based on the comfort envelope equation and the initial permissible vertical acceleration peak value of the vehicle body; if not, reduce the initial permissible vertical acceleration peak value of the vehicle body according to the preset ratio, and recalculate the initial damping coefficient and initial air spring stiffness based on the reduced initial permissible vertical acceleration peak value of the vehicle body until the comfort requirements are met.

[0194] Step 707: Determine the initial permissible vertical acceleration peak of the vehicle body that meets the comfort requirements, as well as the initial damping coefficient and initial air spring stiffness corresponding to the permissible vertical acceleration peak of the vehicle body that meets the comfort requirements, as the desired suspension control parameters.

[0195] Step 708: Construct a parameter transition trajectory model based on the current suspension control parameters and the desired suspension control parameters, and determine the transition time based on the current suspension control parameters and the desired suspension control parameters; determine the suspension control parameters of the target vehicle in each unit time within the transition time based on the parameter transition trajectory model and the transition time. The transition time refers to the time it takes for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters.

[0196] The inventors of this application have implemented the active suspension control parameter determination method for vehicles provided in this application, as detailed below:

[0197] 1. Platform

[0198] 1) Vehicle Dynamics: CarSim2023 Passenger Car Template (Sprout Mass) Suspension stiffness Unsprung mass ).

[0199] 2) Control algorithm: Matlab / Simulink2023b, fixed step size 1ms; QP solver OSQP.

[0200] 3) Interface: Simulink-CarSim S-Function, 10ms periodic synchronization.

[0201] 2. Operating conditions

[0202] 1) Vehicle speed 60km / h, ISO 3888-2 double lane change with superimposed sinusoidal undulating road surface: amplitude A=10mm, wavelength =5m, total length 80m.

[0203] 2) Driver Style Setting: Aggressive ( ),neutral( ),mild( Run each of the three scenarios once.

[0204] 3. Evaluation Indicators

[0205] (1) RMS value of vertical acceleration of the mass on the spring ( );

[0206] (2) Peak impact intensity of the human body ( );

[0207] (3) Number of times the CDC valve current exceeds the limit (Nover);

[0208] (4) Lyapunov function maximum drift ( ).

[0209] 4. Expected / Theoretical Verification Data

[0210] The expected results are shown in Table 2, which presents a comparison of the expected simulation indicators.

[0211] Table 2

[0212]

[0213] Reduction rate = (conventional fixed damping - damping of this application) / conventional fixed damping × 100%.

[0214] Example of theoretical derivation (radical scenario):

[0215] 1) 1-DOF vertical transfer function ,exist At 20.9 rad / s, fixed damping =1800 of This application time This corresponds to a 21.6% decrease in acceleration amplitude; the RMS value reduction rate, calculated by integration, is approximately 17%.

[0216] 2) Impact Constrained by QP trajectory , making The theoretical upper limit has decreased by 25%.

[0217] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0218] Based on the same inventive concept, this application also provides a vehicle active suspension control parameter determination device for implementing the above-described method for determining vehicle active suspension control parameters. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more vehicle active suspension control parameter determination device embodiments provided below can be found in the limitations of the vehicle active suspension control parameter determination method described above, and will not be repeated here.

[0219] In one exemplary embodiment, such as Figure 8 As shown, a vehicle active suspension control parameter determination device 800 is provided, comprising: a first acquisition module 801, a second acquisition module 802, a first determination module 803, and a second determination module 804, wherein:

[0220] The first acquisition module 801 is used to acquire first data collected by a first sensor installed on the target vehicle. The first sensor includes at least one of a steering wheel angle sensor, an accelerator pedal position sensor, and a brake pressure sensor.

[0221] The second acquisition module 802 is used to acquire second data collected by a second sensor installed on the target vehicle. The second sensor includes at least one of a camera device, a lidar device, and a millimeter-wave radar device.

[0222] The first determining module 803 is used to determine the driving style information of the target user driving the target vehicle and the road surface elevation data of the road surface on which the target vehicle is driving based on the first data and the second data, and to determine the desired suspension control parameters of the target vehicle based on the driving style information and the road surface elevation data.

[0223] The second determining module 804 is used to obtain the current suspension control parameters of the target vehicle, and determine the suspension control parameters of the target vehicle in each unit time during the transition time based on the current suspension control parameters and the desired suspension control parameters. The transition time refers to the time it takes for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters.

[0224] In one embodiment, the first determining module 803 is specifically used to determine a target feature vector based on first data, and to determine the driving style information of the target user of the target vehicle based on the target feature vector using a Bayesian update algorithm; wherein, the target feature vector includes at least one of steering wheel angular velocity entropy, average accelerator pedal aggression, average brake pedal aggression, peak lateral acceleration, standard deviation of longitudinal acceleration, and vehicle speed variation coefficient; and to determine the road surface elevation data of the road surface on which the target vehicle travels based on second data.

[0225] In one embodiment, the first determining module 803 is specifically used to determine the maximum amplitude value based on road surface elevation data, the maximum amplitude value being used to characterize the maximum height difference of road surface undulations; determine the dominant wavelength based on road surface elevation data, the dominant wavelength being used to characterize the period length of road surface undulations; determine the excitation intensity coefficient based on the maximum amplitude value and the dominant wavelength; and determine the desired suspension control parameters based on the excitation intensity coefficient, the dominant wavelength, and driving style information.

[0226] In one embodiment, the first determining module 803 is specifically used to obtain a mapping relationship list of driving style information-dominant wavelength-excitation intensity coefficient, which indicates the mapping relationship between different driving style information, different dominant wavelengths, different excitation intensity coefficients and different desired suspension control parameters; using the mapping relationship list of driving style information-dominant wavelength-excitation intensity coefficient, determine the initial desired suspension control parameters according to the excitation intensity coefficient, dominant wavelength and driving style information; obtain a preset comfort envelope equation, and determine the desired suspension control parameters according to the comfort envelope equation and the initial desired suspension control parameters.

[0227] In one embodiment, the initial desired suspension control parameters include the initial damping coefficient, the initial air spring stiffness, and the initial permissible peak vertical acceleration of the vehicle body. The second determining module 803 is specifically used to determine whether the initial desired suspension control parameters meet the comfort requirements based on the comfort envelope equation and the initial permissible peak vertical acceleration of the vehicle body. If not, the initial permissible peak vertical acceleration of the vehicle body is reduced by a preset ratio, and the initial damping coefficient and the initial air spring stiffness are recalculated based on the reduced initial permissible peak vertical acceleration of the vehicle body until the comfort requirements are met. The initial permissible peak vertical acceleration of the vehicle body that meets the comfort requirements, as well as the initial damping coefficient and the initial air spring stiffness corresponding to the permissible peak vertical acceleration of the vehicle body that meets the comfort requirements, are determined as the desired suspension control parameters.

[0228] In one embodiment, the second determining module 803 is specifically used to construct a parameter transition trajectory model based on the current suspension control parameters and the desired suspension control parameters, and to determine the transition time based on the current suspension control parameters and the desired suspension control parameters; and to determine the suspension control parameters of the target vehicle in each unit time within the transition time based on the parameter transition trajectory model and the transition time.

[0229] The modules in the aforementioned active suspension control parameter determination device for vehicles can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0230] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for determining active suspension control parameters for a vehicle.

[0231] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for determining active suspension control parameters for a vehicle.

[0232] Those skilled in the art will understand that Figure 9 and Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0233] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above embodiments.

[0234] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0235] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0236] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0237] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining active suspension control parameters for a vehicle, characterized in that, The method includes: Acquire first data collected by a first sensor installed on the target vehicle, wherein the first sensor includes at least one of a steering wheel angle sensor, an accelerator pedal position sensor, and a brake pressure sensor; Acquire second data collected by a second sensor installed on the target vehicle, the second sensor including at least one of a camera device, a lidar device, and a millimeter-wave radar device; Based on the first data, a target feature vector is determined, and using a Bayesian update algorithm, the driving style information of the target user of the target vehicle is determined based on the target feature vector; wherein, the target feature vector includes at least one of the following: steering wheel angular velocity entropy, mean accelerator pedal aggression, mean brake pedal aggression, peak lateral acceleration, standard deviation of longitudinal acceleration, and vehicle speed variation coefficient; based on the second data, the road surface elevation data of the road surface on which the target vehicle travels is determined, and the maximum amplitude value is determined based on the road surface elevation data, the maximum amplitude value being used to characterize the maximum height difference of the road surface undulations; based on the road surface elevation data, the dominant wavelength is determined, the dominant wavelength being used to characterize the period length of the road surface undulations; based on the... The maximum amplitude and the dominant wavelength are used to determine the excitation intensity coefficient. A mapping relationship list of driving style information-dominant wavelength-excitation intensity coefficient is obtained. This mapping relationship list indicates the mapping relationship between different driving style information, different dominant wavelengths, different excitation intensity coefficients, and different desired suspension control parameters. Using the mapping relationship list, the initial desired suspension control parameters are determined based on the excitation intensity coefficient, the dominant wavelength, and the driving style information. A preset comfort envelope equation is obtained, and the desired suspension control parameters of the target vehicle are determined based on the comfort envelope equation and the initial desired suspension control parameters. The current suspension control parameters of the target vehicle are obtained, and the suspension control parameters of the target vehicle are determined at each unit time during the transition time based on the current suspension control parameters and the desired suspension control parameters. The transition time refers to the time taken for the target vehicle to change from the current suspension control parameters to the desired suspension control parameters.

2. The method according to claim 1, characterized in that, The initial desired suspension control parameters include the initial damping coefficient, initial air spring stiffness, and initial permissible peak vertical acceleration of the vehicle body. Determining the desired suspension control parameters based on the comfort envelope equation and the initial desired suspension control parameters includes: The initial desired suspension control parameters are determined based on the comfort envelope equation and the initial permissible peak vertical acceleration of the vehicle body to determine whether they meet the comfort requirements. If not, the initial permissible vertical acceleration peak value of the vehicle body is reduced by a preset ratio, and the initial damping coefficient and initial air spring stiffness are recalculated based on the reduced initial permissible vertical acceleration peak value of the vehicle body until the comfort requirements are met. The initial permissible peak vertical acceleration of the vehicle body that meets the comfort requirements, as well as the initial damping coefficient and initial air spring stiffness corresponding to the permissible peak vertical acceleration of the vehicle body that meets the comfort requirements, are determined as the desired suspension control parameters.

3. The method according to claim 1 or 2, characterized in that, The step of determining the suspension control parameters of the target vehicle within each unit time period during the transition time based on the current suspension control parameters and the desired suspension control parameters includes: A parameter transition trajectory model is constructed based on the current suspension control parameters and the desired suspension control parameters, and the transition time is determined based on the current suspension control parameters and the desired suspension control parameters. Based on the parameter transition trajectory model and the transition time, the suspension control parameters of the target vehicle are determined for each unit time within the transition time.

4. The method according to claim 2, characterized in that, The step of determining whether the initial desired suspension control parameters meet the comfort requirements based on the comfort envelope equation and the initial permissible peak vertical acceleration of the vehicle body includes: The initial permissible peak vertical acceleration of the vehicle body is substituted into the comfort envelope equation, and the initial desired suspension control parameters are determined to meet the comfort requirements by judging whether the comfort envelope equation is valid.

5. The method according to claim 3, characterized in that, The step of constructing a parameter transition trajectory model based on the current suspension control parameters and the desired suspension control parameters includes: Based on the current suspension control parameters and the desired suspension control parameters, a cubic polynomial is used to construct the parameter transition trajectory model.

6. The method according to claim 3, characterized in that, The determination of the suspension control parameters of the target vehicle within each unit time period during the transition time, based on the parameter transition trajectory model and the transition time, includes: Based on the parameter transition trajectory model, the suspension control parameters of the target vehicle are determined in each unit time of the transition time using a quadratic programming algorithm.

7. A device for determining active suspension control parameters for a vehicle, characterized in that, The device includes: The first acquisition module is used to acquire first data collected by a first sensor installed on the target vehicle, wherein the first sensor includes at least one of a steering wheel angle sensor, an accelerator pedal position sensor and a brake pressure sensor. The second acquisition module is used to acquire second data collected by a second sensor installed on the target vehicle, wherein the second sensor includes at least one of a camera device, a lidar device, and a millimeter-wave radar device. The first determining module is used to determine a target feature vector based on the first data, and to determine the driving style information of the target user of the target vehicle based on the target feature vector using a Bayesian update algorithm; wherein, the target feature vector includes at least one of steering wheel angular velocity entropy, mean accelerator pedal aggression, mean brake pedal aggression, peak lateral acceleration, standard deviation of longitudinal acceleration, and vehicle speed variation coefficient; the module determines road surface elevation data of the road surface on which the target vehicle travels based on the second data, determines the maximum amplitude value based on the road surface elevation data, the maximum amplitude value being used to characterize the maximum height difference of the road surface undulations; and determines the dominant wavelength based on the road surface elevation data, the dominant wavelength being used to characterize the period length of the road surface undulations. The excitation intensity coefficient is determined based on the maximum amplitude and the dominant wavelength. A mapping relationship list of driving style information, dominant wavelength, and excitation intensity coefficient is obtained. This mapping relationship list indicates the mapping relationship between different driving style information, different dominant wavelengths, different excitation intensity coefficients, and different desired suspension control parameters. Using the mapping relationship list, the initial desired suspension control parameters are determined based on the excitation intensity coefficient, the dominant wavelength, and the driving style information. A preset comfort envelope equation is obtained, and the desired suspension control parameters of the target vehicle are determined based on the comfort envelope equation and the initial desired suspension control parameters. The second determining module is used to obtain the current suspension control parameters of the target vehicle, and determine the suspension control parameters of the target vehicle in each unit time during the transition time based on the current suspension control parameters and the desired suspension control parameters. The transition time refers to the time when the target vehicle changes from the current suspension control parameters to the desired suspension control parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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