Vehicle control method and device

Abstract

Embodiments of the present invention disclose a vehicle control method and device capable of improving the user's driving experience. The method includes: establishing a database based on a driver's driving habits, the database comprising at least one of a first database related to ride comfort, a second database related to dynamics, and a third database related to operational stability; and performing at least one of the following control operations: adjusting a first control parameter of the vehicle based on the first database to ensure that the vehicle's ride comfort conforms to the driver's habits; adjusting a second control parameter of the vehicle based on the second database to ensure that the vehicle's dynamics conforms to the driver's habits; and adjusting a third control parameter of the vehicle based on the third database to ensure that the vehicle's operational stability conforms to the driver's habits.

Description

Technical Field

[0001] The present invention relates to vehicle control technology, and in particular to a vehicle control method and device. Background Art

[0002] With the rapid development of automotive technology, people's demand for a better driving experience is also increasing. The personalized matching of driving styles with intelligent vehicle performance has become a key direction in current automotive technology development. Different drivers have different driving habits, some preferring aggressive driving, while others prefer a smooth ride. Therefore, vehicle performance parameters such as power output and suspension damping need to be able to intelligently adjust to different driving styles to provide the best driving experience. Summary of the Invention

[0003] A vehicle control method and device according to an embodiment of the present invention can better match the ride comfort, power, and operational stability of the vehicle to the user.

[0004] A vehicle control method according to an embodiment of the present invention includes: establishing a database based on a driver's driving habits, the database including at least one of a first database related to smoothness, a second database related to dynamics, and a third database related to operational stability; and performing at least one of the following control operations: based on the first database, adjusting a first control parameter of the vehicle so that the smoothness of the vehicle conforms to the driver's habits; based on the second database, adjusting a second control parameter of the vehicle so that the dynamics of the vehicle conforms to the driver's habits; based on the third database, adjusting a third control parameter of the vehicle so that the operational stability of the vehicle conforms to the driver's habits.

[0005] Among them, the first database is used to record the driver's preferences for the range of changes in the pressure of the vehicle's air suspension and damper under different road conditions, as well as the range of changes in the output speed of the vehicle's power system; the first control parameters include: the target pressure of the air suspension and damper, and the target output speed of the power system.

[0006] Among them, adjusting the first control parameter of the vehicle based on the first database includes: determining the actual road surface condition; obtaining the actual pressure of the air suspension and the damper and the actual output speed of the power system; compensating the actual pressure and the actual output speed based on the road surface condition and the first database to obtain a target pressure and a target output speed; and controlling the air suspension and the damper and the power system respectively based on the target pressure and the target output speed.

[0007] The second database is used to record the driver's preference for the dynamic performance evaluation coefficient under different preceding vehicle states, own vehicle states and road curvature ranges, and the dynamic performance evaluation coefficient is at least related to the torque change gradient of the power system.

[0008] Among them, adjusting the second control parameter of the vehicle based on the second database includes: confirming the current leading vehicle status, own vehicle status and road curvature; determining the user's preferred dynamic evaluation coefficient in the current scenario based on the leading vehicle status, own vehicle status, road curvature and the second database; and controlling the vehicle's power system based on the dynamic evaluation coefficient.

[0009] The third database is used to record the driver's preferences for the steering angle variation range and the yaw angular velocity variation range under different steering conditions, and to record the torque compensation value range calculated based on the steering angle variation range and the yaw angular velocity variation range.

[0010] Among them, adjusting the third control parameter of the vehicle based on the third database includes: determining the current steering condition; determining the torque compensation value based on the current steering condition and the third database; compensating the target torque based on the torque compensation value; and controlling the steering actuator based on the compensated target torque.

[0011] Among them, adjusting the third control parameter of the vehicle based on the third database includes: determining the current steering condition; obtaining the steering angle and yaw angular velocity of the vehicle; determining a torque compensation value based on the current steering condition, the steering angle, the yaw angular velocity and the third database; compensating the target torque based on the torque compensation value; and controlling the steering actuator based on the compensated target torque.

[0012] A computer device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to the embodiment of the present invention.

[0013] A computer program product according to an embodiment of the present invention includes a computer program / instruction, which implements the steps of the method according to the embodiment of the present invention when executed by a processor.

[0014] Beneficial effects of the embodiments of the present invention:

[0015] By establishing databases related to ride comfort, power, and handling stability based on driving habits, and adjusting relevant vehicle control parameters based on the driving preferences reflected in these databases, the vehicle's ride comfort, power, and handling stability can be adaptively adjusted to better suit the user's habits. In this embodiment, relevant data can be collected based on actual driving to complete database establishment. The relevant databases and control parameters are then updated based on actual driving, enabling adaptive database and control parameter updates. This entire process does not require pre-calibration, thus saving a significant amount of work and better meeting user requirements for vehicle ride comfort, power, and handling stability, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other details and advantages of the present invention will become apparent from the detailed description provided below. It should be understood that the following drawings are merely illustrative and thus cannot be considered as limiting the present invention. The following detailed description will be given with reference to the accompanying drawings, in which:

[0017] Figure 1 is a flow chart of an embodiment of a vehicle control method of the present invention;

[0018] Figure 2 is a flow chart of an embodiment of a method for controlling vehicle ride comfort;

[0019] Figure 3 is a flow chart of an embodiment of a method for controlling vehicle dynamics;

[0020] Figure 4 is a flow chart of an embodiment of a method for controlling vehicle handling stability;

[0021] Figure 5 2 is a schematic structural diagram of an embodiment of a computer device capable of controlling a vehicle according to the present invention. DETAILED DESCRIPTION

[0022] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0023] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. Moreover, the terms "first", "second", etc. are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0024] In some schemes, there have been some attempts to identify the driver's driving style and adjust the vehicle performance parameters accordingly to improve the driving experience.

[0025] For example, some solutions first acquire driving characteristics data through driving mode learning, determine the driver's driving style, and select a target power output curve that matches that style. However, this approach presents a significant problem: the power output curve is pre-stored and primarily based on subjective calibration data by engineers, which cannot fully meet the individual needs of all drivers. Different drivers may have different requirements for power response sensitivity, acceleration preferences, and other factors. Therefore, this fixed, subjective power output curve matching approach has limitations.

[0026] Other solutions use driver style analysis and road condition detection modules to control and adjust the damping and stiffness of the vehicle's suspension and damping systems in real time. However, these road condition detection modules primarily focus on normal driving conditions, high- and low-speed conditions, and extreme speeds, and do not fully consider the impact of different road surfaces on the vehicle's suspension and damping systems. In reality, factors such as road surface smoothness, material, and friction coefficient can significantly affect a vehicle's driving stability and ride comfort.

[0027] Other approaches collect driving style coefficients by defining static, acceleration, braking, and steering conditions. These factors are then weighted and calculated to derive judgment factors, which are then used to assign corresponding control strategies. However, this method of obtaining current driving style information by calculating judgment factors for different conditions suffers from subjective definition. Different drivers may have significantly different approaches and preferences for various conditions. Therefore, judgment factors based on subjective definitions by engineers may not necessarily accurately reflect the actual driving style of a specific driver.

[0028] Based on this, in an embodiment of the present invention, by establishing multiple databases related to driving habits, including databases related to ride comfort, power, and operational stability, and dynamically adjusting the vehicle's control parameters based on the driving preferences reflected in these databases, personalized adaptation of vehicle performance can be achieved. The solution of the embodiment of the present invention updates the database in real time based on actual driving data, and further updates the control parameters, thereby eliminating the tedious pre-calibration work and being more in line with the user's actual situation. Specifically, the solution of the embodiment of the present invention may include:

[0029] Database establishment: Relevant data from users during actual driving, including acceleration, speed, steering angle, etc., is collected through on-board sensors. The collected data is processed and analyzed to extract features related to smoothness, power, and operational stability. Based on these features, corresponding databases are established. Each database reflects the user's preferences and habits in different driving scenarios.

[0030] Database-Based Control Parameter Adjustment: Based on the driving preference information in the database, algorithms or models are used to calculate control parameters suitable for the current driving scenario. These control parameters are applied to relevant vehicle control systems, such as chassis control and steering control, to achieve personalized adjustments to vehicle performance.

[0031] Real-time updates of the database and control parameters: During vehicle use, the system continuously collects user driving data and updates the database based on the new data. As the database is updated, control parameters are adjusted in real time to adapt to changes in user driving habits.

[0032] This adaptive method in this embodiment of the present invention dynamically adjusts vehicle performance based on the user's actual driving behavior and preferences, ensuring that the vehicle's ride, power, and operational stability better meet the user's expectations. Furthermore, because the entire process is based on real-world data, it more accurately reflects user needs and improves the user experience.

[0033] The embodiments of the present invention are described in more detail below with reference to the accompanying drawings.

[0034] like Figure 1 FIG2 is a flow chart of an embodiment of a vehicle control method of the present invention, which can adaptively control various parameters of the vehicle so that the ride comfort, power and operational stability of the vehicle can better match the user. The method includes:

[0035] Step S10: Establishing a database based on the driver's driving habits.

[0036] Step S12: Based on a first database related to ride comfort, adjusting a first control parameter of the vehicle so that the ride comfort of the vehicle meets the driver's habits.

[0037] Step S14: Based on a second database related to dynamics, adjusting a second control parameter of the vehicle so that the dynamics of the vehicle conforms to the driver's habits.

[0038] Step S16: Based on a third database related to operational stability, adjusting a third control parameter of the vehicle so that the operational stability of the vehicle conforms to the driver's habits.

[0039] In step S10, the first database may be used to record the driver's preferences for the range of pressure changes of the vehicle's air suspension and damper, and the range of output speed changes of the vehicle's power system under different road conditions.

[0040] For example, the first database can be implemented in the form of Table 1:

[0041] Table 1:

[0042]

[0043] In actual vehicle use, the changes in air suspension and damper pressure, as well as the powertrain's output speed, on different road surfaces can be recorded. Probabilistic statistical methods can then be used to calculate the driver's preferred range. For example, when driving on cement roads, the smoothness factor is concentrated around the smoothness factors k and w corresponding to Δp1 and Δn2. In other words, on cement roads, users may be more accustomed to pressure and speed variations within the range represented by Δp1 and Δn2.

[0044] Specifically, when establishing the first database, actual air suspension and damper pressures, as well as actual powertrain output speeds, can be collected while the user is driving on different road surfaces. Based on these actual pressures and speeds, a smoothness factor K (corresponding to the pressure change per unit time, Δp / s, and the number of occurrences of this value per unit time, n1) and a smoothness factor W (corresponding to the speed change per unit time, Δn / s, and the number of occurrences of this value per unit time, n2) are calculated. A higher number of occurrences is assigned a higher weight.

[0045] The second database can be used to record the driver's preferences for the dynamics evaluation coefficient under different preceding vehicle conditions, the driver's own vehicle conditions, and road curvature ranges. The dynamics evaluation coefficient is related to the torque gradient of the powertrain. In other words, when the dynamics evaluation coefficient is known, the torque gradient can be derived.

[0046] For example, the second database can be implemented in the form of Table 2:

[0047] Status of the preceding vehicle Vehicle status Curvature range Dynamic performance evaluation coefficient A State_A1 State_B1 Rang1 A1, A2...

[0048] During vehicle use, the movement state of the preceding vehicle, such as speed, acceleration, and distance, and the movement state of the own vehicle, such as speed, acceleration, and road curvature range, can be recorded and analyzed according to actual driving conditions, thereby obtaining the range interval preferred by the driver and the preferred dynamic evaluation coefficient A.

[0049] The third database can be used to record the driver's preferences for steering angle variation range, yaw rate variation range, and torque compensation range under different steering conditions. The torque compensation range can be calculated based on the steering angle variation range and yaw rate variation range.

[0050] For example, the third database can be implemented in the form of Table 3:

[0051] Table 3:

[0052] Working conditions Steering angle change range Yaw angular velocity change range Torque compensation Power assist, return, damping XX XX XX

[0053] During vehicle use, the system records and analyzes the ranges of steering angle and yaw rate variations for three operating conditions: power assist, self-centering, and damping. This determines user preferences for these ranges under different conditions. Based on these values, a Bayesian network is used to calculate the torque compensation for the steering system actuator, establishing a relationship between the steering angle, yaw rate, and torque compensation.

[0054] In step S12, the first control parameters may include, for example, target pressures for the air suspension and damper, and a target output speed for the powertrain. In step S12, based on the user preferences recorded in the first database, the target pressures for the air suspension and damper may be adjusted to ensure that pressure variations in the air suspension and damper are consistent with the user's habits, thereby meeting the user's personalized requirements for ride stability. Similarly, in step S12, based on the user preferences recorded in the first database, the target output speed for the powertrain may be adjusted to ensure that variations in the powertrain's output speed are consistent with the user's habits.

[0055] Specifically, if Figure 2 FIG. 1 is a flow chart of an embodiment of step S12, which includes:

[0056] Step S20: Determine the actual road surface condition.

[0057] In step S20, the actual road surface condition may include, for example, cement road, asphalt road, or gravel road, and those skilled in the art may make more detailed classifications based on actual needs. In some embodiments, the actual road surface condition may be determined based on the road surface condition captured by an image sensor. In other embodiments, the actual road surface condition of the current driving situation may be calculated based on a convolutional neural network, where the input data of the convolutional neural network may include, for example, wheel speed, wheel speed difference, output speed and torque of the power system, and the output data is the current road condition, i.e., the actual road surface condition.

[0058] Step S22: Acquire the actual pressure of the air suspension and the damper and the actual output speed of the power system.

[0059] In step S22 , relevant pressure sensors may be provided to detect actual pressures of the air suspension and the damper, and relevant speed sensors may be provided to detect actual output speed of the power system.

[0060] Step S24: Compensating the actual pressure and the actual output speed based on the actual road surface state and the first database.

[0061] In step S24, based on the actual road conditions, Δp / s and Δn / s that match the current actual road conditions are output from the data set in the first database. Pressure and speed compensation values Poffset and Noffset are then calculated based on Δp / s and Δn / s to compensate for the current actual pressure and output speed values. Target pressure and speed values (target value = actual value + compensation value) are then calculated and used as target parameters for the control system.

[0062] Step S26: Based on the compensated actual pressure and actual output speed, the air suspension, the damper and the power system are controlled respectively.

[0063] In this embodiment, by adjusting the pressure of the air suspension and damper and the output speed of the power system, the changes in pressure (such as the speed of change) and the output speed can be closer to the user's habits in the current situation, so that the smoothness of the entire vehicle can automatically adapt to the user to improve the user experience.

[0064] In step S14 , the second control parameter may include, for example, a torque change gradient of the power system.

[0065] Specifically, if Figure 3 FIG. 1 is a flow chart of an embodiment of step S14, which includes:

[0066] Step S30: Determine the current state of the preceding vehicle, the current state of the own vehicle, and the curvature of the road.

[0067] In step S30, the state of the preceding vehicle may include, for example, the speed of the preceding vehicle, the acceleration of the preceding vehicle, and the distance between the preceding vehicle and the vehicle, which may be acquired by the radar sensor carried by the vehicle. The state of the vehicle may include, for example, the speed and acceleration of the vehicle, and the curvature of the road may be calculated based on the vehicle speed and the yaw rate using the formula It is calculated that, is the vehicle yaw rate, V ego is the vehicle speed.

[0068] Step S32: Based on the second database, as well as the current state of the preceding vehicle, the state of the own vehicle, and the road curvature, determine the user-preferred dynamics evaluation coefficient in the current scenario, that is, determine the user-preferred torque change gradient.

[0069] In step S32, the following relationship can be fitted in advance based on the data in the second database:

[0070] ΔTorque=b1f(State A )+b2f(State B )+b3f(k)+e1

[0071] Among them, ΔTorque is the torque change gradient, State A State is the motion state of the front vehicle. B is the motion state of the vehicle, k is the curvature, b1b2b3 are the regression coefficients, and e1 is the random error.

[0072] Then, the current state of the preceding vehicle, the state of the own vehicle, and the road curvature are substituted into the above equation to obtain the torque change gradient preferred by the user under the current state.

[0073] Step S34: Controlling the power system of the vehicle based on the power performance evaluation coefficient.

[0074] In this step, if the dynamics evaluation coefficient indicates that the user is accustomed to a steeper torque change gradient, the torque change will be controlled at a faster speed.

[0075] In this embodiment, users may have different torque variation preferences in different scenarios. For example, when following a car on a curve, a user may prefer a smoother torque variation. When overtaking on a straight road, a user may prefer a more responsive torque variation. Through the adaptive torque control of this embodiment, torque can be controlled in a manner that suits the user's preferences based on different scenarios, thereby improving the user's driving experience.

[0076] In step S16, the third control parameter includes: the output torque of the steering actuator. Specifically, Figure 4FIG. 1 is a flow chart of an embodiment of step S16, which includes:

[0077] Step S40: Determine the current steering condition.

[0078] Steering conditions include power assist, self-centering, and damping. Power assist refers to the steering system providing power assist to assist the driver in turning the steering wheel. Self-centering refers to the steering system automatically returning the steering wheel to the center position after the vehicle completes the turn. Damping refers to the steering system providing a certain amount of damping force during the steering process to improve driving feel and stability.

[0079] In some implementations, the current steering condition may be determined based on the steering wheel angle and steering wheel torque signals.

[0080] Step S42: Determine a torque compensation value based on the current steering condition and the third database to compensate for the target torque.

[0081] Specifically, a torque compensation value can be determined based on the current steering condition by referring to the third database. The third database provides information on user-preferred steering angle and yaw rate ranges for various operating conditions. Therefore, the torque compensation value corresponding to the user-preferred steering angle and yaw rate ranges can be used as the torque compensation value.

[0082] Additionally, in some embodiments, a torque compensation value is determined based on the current steering condition, the current steering angle, the current yaw rate, and the contents of a third database. For example, a first torque compensation value may be determined based on the current steering condition, and then a second torque compensation value may be determined based on the current actual steering angle and yaw rate. Finally, the first and second torque compensation values are combined to determine a final torque compensation value.

[0083] Step S44: Control the steering actuator based on the compensated target torque.

[0084] In this step, the target torque is compensated using the torque compensation value, and then the steering actuator is controlled.

[0085] In this embodiment, the user's preference is taken into consideration when controlling the steering actuator. That is, the steering actuator is controlled with reference to the user's preferred steering angle and yaw angular velocity, thereby enabling the vehicle's operational stability to better suit the user's preference.

[0086] like Figure 5FIG2 is a schematic diagram of the structure of an embodiment of a computer device of the present invention. The computer device 5 includes a memory 50, a processor 52, and a computer program stored in the memory 50. The processor 52 executes the computer program to implement the steps of the method of the embodiment of the present invention.

[0087] The computer device 5 of the present invention may be a controller in a vehicle, such as a domain controller, an HPC (High Performance Computer), an ECU (Electronic Control Unit), a chip, and the like.

[0088] In addition, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method described in the embodiment of the present invention are implemented.

[0089] In addition, an embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the method described in the embodiment of the present invention when executed by a processor.

[0090] The descriptions of the above device, storage medium, and program product embodiments are similar to the descriptions of the above method embodiments and have similar beneficial effects as the method embodiments. For technical details not disclosed in the device, storage medium, and program product embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0091] The processor may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic device that implements the functions of the processor may also be other electronic devices, which are not specifically limited in the embodiments of the present application.

[0092] The above-mentioned computer storage medium / memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it can also be various terminals including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0093] It should be noted that the above description is only an example and not a limitation of the present invention. In other embodiments of the present invention, the method may have more, fewer or different steps, and the order, inclusion and function of the steps may be different from those described and illustrated. For example, multiple steps can usually be combined into a single step, and a single step can also be split into multiple steps. For those of ordinary skill in the art, without paying creative work, changes in the order of the steps are also within the scope of protection of the present invention.

[0094] The technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor or a microcontroller to execute all or part of the steps of the method described in each embodiment of the present invention.

[0095] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments.

[0096] Although the present invention has been disclosed above with reference to preferred embodiments, the present invention is not limited thereto. Any changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope defined by the claims.

Claims

1. A vehicle control method, characterized in that: include: Establishing a database based on the driver's driving habits, the database comprising: a first database related to ride comfort, a second database related to power, and a third database related to handling stability, wherein features related to ride comfort, power, and handling stability are extracted by collecting relevant data from the user during actual driving, processing and analyzing the collected data; establishing the first to third databases based on these features, each database reflecting the user's preferences and habits in different driving scenarios; and Perform the following control operations: Adjusting, based on a first database, a first control parameter of the vehicle so that the ride comfort of the vehicle conforms to the driver's habits, wherein the first database is used to record the driver's preferences for the range of pressure changes of the vehicle's air suspension and damper under different road conditions, and the first control parameter includes target pressures of the air suspension and damper, wherein the pressures of the air suspension and damper are adjusted so that the speed of pressure changes can be more consistent with the user's habits under the current conditions; adjusting a second control parameter of the vehicle based on a second database so that the vehicle's dynamics conforms to the driver's habits, wherein the second database is used to record the driver's preferences for a dynamics evaluation coefficient under different preceding vehicle states, own vehicle states, and road curvature ranges, the dynamics evaluation coefficient representing a torque change gradient accustomed to the user; Based on a third database, a third control parameter of the vehicle is adjusted so that the operational stability of the vehicle conforms to the driver's habits, wherein the third database is used to record the driver's preferences for the steering angle variation range and the yaw angular velocity variation range under different steering conditions, and to record the torque compensation value range calculated based on the steering angle variation range and the yaw angular velocity variation range. The third control parameter includes: the output torque of the steering actuator.

2. The vehicle control method according to claim 1, wherein: The first database is further used to record the driver's preference for the range of change of the output speed of the power system of the vehicle under different road conditions; The first control parameter further includes: a target output speed of the power system.

3. The vehicle control method according to claim 2, wherein: The adjusting the first control parameter of the vehicle based on the first database includes: Determine the actual road surface condition; Acquiring actual pressures of the air suspension and the damper and an actual output speed of the power system; Determining, based on the road surface condition and a first database, a first smoothness factor and a second smoothness factor that conform to the road surface condition, the first smoothness factor corresponding to a pressure change per unit time, and the second smoothness factor corresponding to a speed change per unit time; Calculating a pressure compensation value based on the first smoothness factor, and calculating a speed compensation value based on the second smoothness factor; Compensating the actual pressure according to the pressure compensation value to obtain a target pressure, and compensating the actual output speed according to the speed compensation value to obtain a target output speed; Based on the target pressure and the target output speed, the air suspension and the damper, and the power system are controlled respectively.

4. The vehicle control method according to claim 1, wherein: The adjusting the second control parameter of the vehicle based on the second database includes: Confirm the current status of the preceding vehicle, the vehicle itself, and the road curvature; Determining a user-preferred dynamics evaluation coefficient in a current scenario based on the preceding vehicle state, the own vehicle state, the road curvature, and a second database; Based on the power performance evaluation coefficient, a power system of the vehicle is controlled.

5. The vehicle control method according to claim 1, wherein: The adjusting, based on the third database, the third control parameter of the vehicle includes: Determine the current steering condition; determining a torque compensation value based on the current steering condition and a third database; compensating the target torque based on the torque compensation value; and The steering actuator is controlled based on the compensated target torque.

6. The vehicle control method according to claim 1, wherein: The adjusting, based on the third database, the third control parameter of the vehicle includes: Determine the current steering condition; Get the vehicle's steering angle and yaw rate; determining a torque compensation value based on the current steering condition, the steering angle, the yaw rate, and a third database; compensating the target torque based on the torque compensation value; and The steering actuator is controlled based on the compensated target torque.

7. The vehicle control method according to claim 6, wherein: Determining a torque compensation value based on the current steering condition, the steering angle, the yaw rate, and a third database includes: A first torque compensation value is determined based on the current steering condition, and then a second torque compensation value is determined based on the current actual steering angle and yaw rate. Finally, the first torque compensation value and the second torque compensation value are combined to determine a final torque compensation value.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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