Control method, system and vehicle for a vehicle

Abstract

This application discloses a vehicle control method, system, and vehicle, belonging to the field of vehicle technology. The vehicle control method includes: determining an estimated disturbance value of the vehicle during parking based on the vehicle's driving state information; performing control based on the remaining driving distance between the vehicle and the parking position, the driving state information, and the estimated disturbance value to determine a target control command; and controlling the vehicle to park based on the target control command, thereby increasing the vehicle's acceleration during obstacle crossing. The vehicle control method of this application enables the vehicle control system to proactively anticipate and counteract the impact caused by obstacles such as speed bumps or steps, significantly improving longitudinal speed stability during obstacle crossing, increasing vehicle acceleration during obstacle crossing, effectively avoiding problems such as wheel jamming, difficulty starting, and repeated vehicle bounce, and enhancing the reliability, success rate, and ride comfort of the automatic parking system on complex road surfaces.

Description

Technical Field

[0001] This application belongs to the field of vehicle technology, and in particular relates to a vehicle control method, system and vehicle. Background Technology

[0002] Automated parking systems are a fundamental component of intelligent driving vehicles. However, due to external disturbances such as uneven road surfaces and execution delays, the longitudinal speed control performance of automated parking faces challenges. When encountering obstacles during parking, the longitudinal speed becomes unstable, leading to a poor driving experience for occupants. Existing longitudinal speed control methods for obstacle crossing are too simplistic, relying on prior knowledge and visual positioning information. Sensors have measurement errors, and actuators also experience execution delays. These uncertainties increase the difficulty of engineering control strategies. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a vehicle control method, system, and vehicle, enabling the vehicle control system to actively anticipate and counteract the impact caused by obstacles such as speed bumps or steps, significantly improving the longitudinal speed stability during obstacle crossing, increasing the vehicle's acceleration during obstacle crossing, effectively avoiding problems such as wheel jamming, difficulty starting, and repeated body rebound, and enhancing the reliability, success rate, and ride comfort of the automatic parking system on complex road surfaces.

[0004] In a first aspect, this application provides a method for controlling a vehicle, including: Based on the vehicle's driving status information, the estimated interference value of the vehicle during the parking and driving process is determined; Control is performed based on the remaining driving distance between the vehicle and the parking position, the driving status information, and the estimated disturbance value to determine the target control command; The vehicle is controlled to park based on the target control command, thereby increasing the vehicle's acceleration during obstacle crossing.

[0005] According to the vehicle control method provided in the embodiments of this application, during automatic parking, an estimated disturbance value representing the sum of external disturbances such as uneven road surfaces is estimated using vehicle driving state information. Then, when generating vehicle control commands based on the remaining distance, the estimated disturbance value is actively introduced for feedforward compensation to obtain the target control command used to drive the vehicle. This enables the vehicle control system to proactively anticipate and counteract the impact caused by obstacles such as speed bumps or steps, significantly improving the longitudinal speed stability during obstacle crossing and increasing the vehicle's acceleration during obstacle crossing. This effectively avoids problems such as wheel jamming, difficulty starting, and repeated body rebound, enhancing the reliability, success rate, and ride comfort of the automatic parking system on complex road surfaces.

[0006] One embodiment of the vehicle control method of this application, wherein the control is performed based on the remaining driving distance between the vehicle and the parking position, the driving state information, and the estimated disturbance value, and a target control command is determined, including: Determine the desired speed information based on the remaining driving distance; Based on the error between the desired speed information and the actual speed in the driving status information, a reference control quantity is generated; The target control command is obtained by controlling the reference control quantity based on the estimated disturbance value.

[0007] One embodiment of the vehicle control method of this application, wherein determining the desired speed information based on the remaining driving distance includes: The remaining driving distance is input to the proportional controller to obtain the desired speed information output by the proportional controller.

[0008] A vehicle control method according to an embodiment of this application, wherein controlling the reference control quantity based on the estimated disturbance value to obtain the target control command includes: The estimated disturbance value is used as a feedforward compensation term, and the estimated disturbance value is subtracted from the reference control value to obtain the target control command.

[0009] One embodiment of the vehicle control method of this application includes determining an estimated disturbance value of the vehicle during parking and driving based on the vehicle's driving state information, comprising: A longitudinal dynamics model of the vehicle is established, which includes a disturbance term to characterize the total external disturbance. Based on the vehicle longitudinal dynamics model, the driving state information, and the control input, the estimated interference value corresponding to the interference term is determined by the interference observer.

[0010] One embodiment of the vehicle control method of this application, wherein controlling the vehicle to park based on the target control command includes: Based on the target control command, the total required longitudinal force for the vehicle is determined; With the goal of minimizing the slip ratio of each drive wheel of the vehicle, the total required longitudinal force is distributed to each of the drive wheels to control the parking of the vehicle.

[0011] A vehicle control method according to an embodiment of this application, wherein distributing the total required longitudinal force to each of the drive wheels includes: Based on the driving status information, the yaw moment requirement of the vehicle is determined; With the goal of meeting the yaw moment requirements of the vehicle, the total required longitudinal force is distributed to each of the drive wheels.

[0012] One embodiment of this application describes a vehicle control method, wherein distributing the total required longitudinal force to each of the drive wheels with the objective of minimizing the slip ratio of each drive wheel includes: Based on the total required longitudinal force and the required yaw moment, a multi-objective optimization function is constructed, which includes a slip ratio optimization term and a moment tracking term. Solve the multi-objective optimization function to determine the torque information corresponding to each of the drive wheels.

[0013] One embodiment of the vehicle control method of this application includes solving the multi-objective optimization function to determine the torque information corresponding to each of the drive wheels, comprising: The multi-objective optimization function is iteratively solved to determine the torque information corresponding to each of the drive wheels.

[0014] Secondly, this application provides a vehicle control system, including: The first processing module is used to determine the estimated interference value of the vehicle during the parking process based on the vehicle's driving status information. The second processing module is used to perform control based on the remaining driving distance between the vehicle and the parking position, the driving status information, and the estimated interference value, and to determine the target control command. The third processing module is used to control the vehicle to park based on the target control command, thereby increasing the vehicle's acceleration during obstacle crossing.

[0015] According to the vehicle control system provided in the embodiments of this application, during automatic parking, an estimated disturbance value representing the sum of external disturbances such as uneven road surfaces is estimated using vehicle driving status information. Then, when generating vehicle control commands based on the remaining distance, the estimated disturbance value is actively introduced for feedforward compensation to obtain the target control command used to drive the vehicle. This enables the vehicle control system to proactively anticipate and counteract the impact caused by obstacles such as speed bumps or steps, significantly improving the longitudinal speed stability during obstacle crossing, increasing the vehicle's acceleration during obstacle crossing, effectively avoiding problems such as wheel jamming, difficulty starting, and repeated body rebound, and enhancing the reliability, success rate, and ride comfort of the automatic parking system on complex road surfaces.

[0016] Thirdly, this application provides a vehicle including a controller for performing the vehicle control method as described in the first aspect.

[0017] One embodiment of the vehicle in this application includes: Multiple drive wheels, the drive wheels being configured to move based on the vehicle control method as described in the first aspect, to control the parking of the vehicle.

[0018] Fourthly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle control method described in the first aspect above.

[0019] Fifthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle control method as described in the first aspect above.

[0020] Sixthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle control method as described in the first aspect above.

[0021] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects: During automatic parking, an estimated disturbance value representing the sum of external disturbances such as uneven road surfaces is estimated using vehicle driving status information. Then, when generating vehicle control commands based on the remaining distance, this estimated disturbance value is actively introduced for feedforward compensation to obtain the target control command used to drive the vehicle. This allows the vehicle control system to proactively anticipate and counteract the impact caused by obstacles such as speed bumps or steps, significantly improving longitudinal speed stability when overcoming obstacles and increasing vehicle acceleration during obstacle crossing. It effectively avoids problems such as wheel jamming, difficulty starting, and repeated body rebound, enhancing the reliability, success rate, and ride comfort of the automatic parking system on complex road surfaces.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is one of the flowcharts illustrating the vehicle control method provided in the embodiments of this application; Figure 2 This is a second schematic flowchart of the vehicle control method provided in the embodiments of this application; Figure 3 This is the third flowchart illustrating the vehicle control method provided in the embodiments of this application; Figure 4 This is the fourth flowchart illustrating the vehicle control method provided in the embodiments of this application; Figure 5This is the fifth flowchart illustrating the vehicle control method provided in the embodiments of this application; Figure 6 This is a schematic diagram of the test results of the vehicle control method and related technologies provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the vehicle control system provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0024] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0025] The following description, in conjunction with the accompanying drawings, details the vehicle control method, vehicle control system, vehicle, electronic equipment, and readable storage medium provided in this application through specific embodiments and application scenarios.

[0026] The vehicle control method can be applied to the terminal, and can be executed by the hardware or software in the terminal.

[0027] The vehicle control method provided in this application embodiment can be executed by an electronic device or a functional module or entity in an electronic device that can implement the vehicle control method. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablets, computers, cameras, and wearable devices. The vehicle control method provided in this application embodiment will be described below using an electronic device as the execution subject.

[0028] like Figure 1 As shown, the vehicle control method includes steps 110, 120 and 130.

[0029] Step 110: Based on the vehicle's driving status information, determine the estimated disturbance value of the vehicle during the parking process; In this step, the driving status information refers to the physical quantities that are directly measured by sensors (such as wheel speed sensors and inertial measurement units, IMUs) or calculated by a state estimator during the vehicle's movement.

[0030] The driving status information may include at least one of vehicle speed, wheel speed, longitudinal acceleration, heading angle and yaw rate, or may include other physical quantities, which are not limited in this application.

[0031] The estimated disturbance value is a quantified value of the concentrated disturbances acting on the vehicle, calculated by an algorithm. Uncertainties such as crosswind disturbances, road excitations (e.g., speed bumps, grass-paved surfaces, or small steps), execution delays, and unmodeled dynamics can be packaged into a single total disturbance term, i.e., concentrated disturbance. The estimated disturbance value is the estimate of this concentrated disturbance.

[0032] The system uses sensors and state parameter estimators to estimate key state information during vehicle operation, and calculates concentrated interference during operation using an interference estimation module.

[0033] During the research and development process, the inventors discovered that related technologies rely too heavily on prior knowledge (such as the location of speed bumps) or visual positioning, and uncertainties such as sensor errors and actuator delays increase the difficulty of engineering control strategies.

[0034] In this application, instead of precisely distinguishing and measuring each type of interference, external interference is treated as a concentrated interference for estimation, and a corresponding estimation algorithm is established. This concentrated interference encompasses all unknown and difficult-to-model influences and estimates them in real time. It does not rely on the precise identification and localization of obstacle types, can adapt to more complex scenarios, and improves the stability and adaptability of the system in uncertain environments.

[0035] Step 120: Based on the remaining driving distance between the vehicle and the parking position, driving status information, and estimated disturbance value, control is performed to determine the target control command; In this step, the remaining driving distance is the length that needs to be traveled from the vehicle's current position to the final parking space along the planned parking trajectory. For example, the remaining driving distance can be obtained through the trajectory planning module.

[0036] When generating control commands, estimated disturbance values ​​can be taken into account in advance to counteract or reduce the impact of disturbances on the control effect.

[0037] The target control command is the command obtained after compensation control and is finally prepared to be issued to the vehicle actuators (such as drive motors or braking systems). It may include longitudinal acceleration or other control quantities (such as total driving force), which are not limited in this application.

[0038] Step 130: Control the vehicle to park based on the target control command, and increase the vehicle's acceleration during obstacle crossing.

[0039] In this step, the tire force to be distributed to each wheel can be obtained based on the target control command (such as acceleration), and then applied to the vehicle through the underlying controller (such as the hub motor) to make it complete the parking movement.

[0040] During the research and development process, the inventors discovered that for road surface disturbances such as speed bumps or small steps, the relevant technologies rely on precise perception and positioning, or have a single control method. Under complex disturbances, the performance will decrease, resulting in unstable longitudinal speed during parking, which may lead to unpleasant experiences such as wheel jamming, difficulty starting, or multiple rebounds.

[0041] In this application, an interference feedforward compensation mechanism based on real-time estimation is introduced in the longitudinal speed control of automatic parking. The estimated interference value is obtained based on the vehicle's driving state information, and then the estimated interference value is used to generate compensation control commands. Through active compensation of interference, longitudinal impact and speed fluctuation when passing through obstacles such as speed bumps can be effectively suppressed, thereby reducing wheel jamming and starting difficulties.

[0042] By unifying various uncertainties into a centralized disturbance for estimation and compensation, this application does not rely on the precise identification and location of obstacle types, and can adapt to more complex scenarios, thereby improving the stability and adaptability of the system in uncertain environments.

[0043] like Figure 6 The test results are illustrated in the graph, which compares the acceleration curves of this application (PID + interference compensation, red curve) with those of related technologies (PID, blue curve). The horizontal axis represents time (s), and the vertical axis represents vehicle acceleration (m / s²). 2 ).

[0044] like Figure 6 As shown, during the automatic parking control process using the methods in related technologies, when the vehicle encounters an obstacle (such as a speed bump) (as shown in the curve segment corresponding to the period of sudden acceleration in the figure), the vehicle's acceleration is relatively small, and the time required to overcome the obstacle is relatively long.

[0045] In the process of automatic parking control using the method provided in the embodiments of this application, by adding interference compensation, the vehicle's acceleration can be increased, enabling the vehicle to overcome obstacles in a shorter time.

[0046] It should be noted that, in the absence of obstacles during parking, the estimated interference value in this application may approach zero, that is, the red curve and the blue curve in the figure almost overlap.

[0047] The inventors' tests revealed that vehicles using the control method provided in this application outperform vehicles using related technologies in automatic parking obstacle-crossing performance. This application can solve technical problems such as unstable speed, wheel jamming, and poor user experience when crossing obstacles such as speed bumps.

[0048] According to the vehicle control method provided in the embodiments of this application, during automatic parking, an estimated disturbance value representing the sum of external disturbances such as uneven road surfaces is estimated using vehicle driving state information. Then, when generating vehicle control commands based on the remaining distance, the estimated disturbance value is actively introduced for feedforward compensation to obtain the target control command used to drive the vehicle. This enables the vehicle control system to proactively anticipate and counteract the impact caused by obstacles such as speed bumps or steps, significantly improving the longitudinal speed stability during obstacle crossing and increasing the vehicle's acceleration during obstacle crossing. This effectively avoids problems such as wheel jamming, difficulty starting, and repeated body rebound, enhancing the reliability, success rate, and ride comfort of the automatic parking system on complex road surfaces.

[0049] In some embodiments, step 110 may include: A longitudinal dynamics model of the vehicle is established, which includes a disturbance term to characterize the total external disturbance. Based on the vehicle's longitudinal dynamics model, driving state information, and control input, the estimated disturbance value corresponding to the disturbance term is determined through a disturbance observer.

[0050] In this embodiment, the vehicle's longitudinal dynamics model is a mathematical equation describing the vehicle's longitudinal (forward / reverse) motion. It can be a simplified physical model used to characterize the relationship between the vehicle's mass, driving force, drag, and acceleration.

[0051] The disturbance term used to characterize the total external disturbance is a specific term in the model. It is used to represent all external influences that are not explicitly described in other parts of the model. It is a "packaged" unknown quantity, such as a concentrated disturbance representing uncertainties such as crosswind interference, road excitation, execution delay, and unmodeled dynamics.

[0052] The control input can be the instruction sent by the controller to the vehicle actuators (such as motors or brakes) from the previous control cycle or the current moment, i.e., the reference control quantity or the target control instruction.

[0053] An interference observer is a model-based estimator or state observer that can be used to estimate unknown or unmeasurable states or interferences in a system in real time. The magnitude of the interference can be inferred by comparing the model's predicted behavior with the actual measurements from sensors (driving state information) under known inputs.

[0054] The observer algorithm can be used to calculate the real-time estimated value of the disturbance term in the model, i.e., the estimated disturbance value.

[0055] In this application, known information (such as models, control commands, and measurement outputs) is used to infer position information (disturbance). The actual measured vehicle behavior is compared with the behavior that the model should produce if there is no disturbance. The difference between the two, after deducting the uncertainty of the model itself, is determined to be the influence of the disturbance. The algorithm calculates the estimated value of this difference in real time, that is, the estimated disturbance value. It can infer the overall effect of the disturbance from the vehicle's motion response without relying on the direct and accurate perception of the disturbance source (such as without visually identifying the specific height and location of the speed bump). It has strong adaptability and robustness.

[0056] In actual implementation, such as Figure 3 As shown, external interference can be treated as concentrated interference for estimation.

[0057] A dynamic model considering disturbances can be established. The model can be a longitudinal dynamic model, with the following equations: Formula 1: .

[0058] Formula 2: .

[0059] in, This is the sum of the longitudinal forces of all wheels. air density, For windward area, Where m is the air drag coefficient, m is the mass, and g is the acceleration due to gravity. For road surface slope, This is the sum of all wheel rolling resistances and other unknown resistances.

[0060] External disturbances can be treated as concentrated disturbances, and the control equation for longitudinal velocity is: Formula 3: .

[0061] in, For longitudinal acceleration, The longitudinal velocity (actual velocity) measured by the sensor. For the output matrix, To control the input, This refers to the concentrated disturbances (disturbance terms) caused by uncertainties such as crosswind interference, road excitation, execution delay, and unmodeled dynamics.

[0062] An estimation algorithm for concentrated interference can be designed, and the observation error of the interference can be reduced. for: Formula 4: .

[0063] in, As a distractor, This is an estimate of the interference (i.e., the estimated interference value).

[0064] Observational error of velocity for: Formula 5: .

[0065] in, for The estimated value, This refers to the actual speed.

[0066] The sliding surface can be designed as follows: Formula 6: .

[0067] in, For the observation error of interference, This represents the observation error of velocity.

[0068] Differentiate Equation 6 and set it equal to the reaching rate - Formula 7 can be obtained: .

[0069] in, To interfere with the rate of change over time, The rate of change of the disturbance estimate, For actual acceleration, To estimate acceleration, As a distractor, This is an estimate of the interference (i.e., the estimated interference value). For actual speed, for The estimated value is L, where L is the reaching law gain.

[0070] Assuming the disturbance changes slowly, substituting Equation 3 into Equation 7 and simplifying, we get Equation 8: .

[0071] in, Let L be the rate of change of the disturbance estimate, and L be the reaching law gain. For actual acceleration, For the output matrix, To control the input, This is an estimate of the interference (i.e., the estimated interference value). For actual speed, for The estimated value, To estimate acceleration.

[0072] Intermediate variables can be introduced: Formula 9: .

[0073] in, As an intermediate variable, This is an estimate of the interference (i.e., the estimated interference value). Let L be the actual velocity and L be the reaching law gain.

[0074] Differentiating Equation 9 and substituting it into Equation 8, we get: Formula 10: .

[0075] in, Let L be the derivative of the intermediate variable, and L be the reaching law gain. As an intermediate variable, For the output matrix, To control the input, For actual speed, for The estimated value, To estimate acceleration.

[0076] After transformation, we get: Formula 11: .

[0077] in, Let L be the derivative of the intermediate variable, and L be the reaching law gain. As an intermediate variable, For the output matrix, To control the input, For actual speed, This is for speed control error.

[0078] Fit the observation terms using the velocity control error. Where L is the reaching law gain, for The estimated value, To estimate acceleration, k is the gain. This is for speed control error.

[0079] The value of the intermediate variable z can be obtained by integrating Equation 11, and the disturbance of the observation can be obtained based on Equation 9: Formula 12: .

[0080] In the formula, The longitudinal velocity measured by the sensor. To estimate the interference value, Let L be the intermediate variable and L be the reaching law gain.

[0081] In some embodiments, step 120 may include: Determine the desired speed information based on the remaining driving distance; Based on the error between the desired speed information and the actual speed in the driving status information, a reference control quantity is generated; The target control command is obtained by controlling the reference control quantity based on the estimated disturbance value.

[0082] In this embodiment, the desired speed information is the longitudinal speed target value that the vehicle should reach under ideal, undisturbed conditions, calculated by the control algorithm based on the current remaining parking distance. It is a set value that varies with time or position.

[0083] For example, based on a simple proportional relationship, the remaining driving distance can be converted into the desired speed information as a control target, forming the basis for subsequent speed tracking control. By planning the speed in real time according to the distance, the parking process can be ensured to be smooth and the car can be accurately parked in the parking space.

[0084] The actual speed is the vehicle's true longitudinal speed at the current moment, which can be measured based on wheel speed sensors, etc.

[0085] Error can be the difference between the expected speed information and the actual speed, and can intuitively reflect the gap between the current state of the vehicle and the control target.

[0086] The reference control quantity is the initial control command calculated by the controller to eliminate the aforementioned speed error without introducing interference compensation.

[0087] Closed-loop feedback control can be used to ensure that the actual speed of the vehicle can track the desired speed. For example, proportional, integral and lead compensation links can be used to enable the controller to calculate the reference acceleration to be applied to the vehicle based on the magnitude, accumulation and historical trend of the error.

[0088] Based on the estimated disturbance value, the baseline control quantity can be corrected to obtain the target control command. The target control command is the final control output after compensation and correction.

[0089] In this application, feedforward compensation is adopted. The reference control quantity is controlled based on the estimated disturbance value to obtain the target control command. By feeding forward the estimated disturbance value obtained from the observer to the controller output, the driving / braking force can be actively increased or decreased to cancel the disturbance (such as the resistance of speed bump) before it actually affects the vehicle speed, thereby achieving smooth obstacle crossing and reducing impact.

[0090] In some embodiments, determining the desired speed information based on the remaining driving distance may include: The remaining driving distance is input to the proportional controller to obtain the desired speed information output by the proportional controller.

[0091] In this embodiment, the output of the proportional controller has a simple proportional relationship with the input error. The input can be the remaining driving distance, and the output can be the desired speed information.

[0092] The remaining driving distance acquired in real time can be used as an input signal and directly sent to a pre-designed proportional controller. The controller can calculate and output a corresponding speed value, i.e., the desired speed information, based on its internally set proportional coefficient (gain).

[0093] The proportional controller can convert spatial path planning (remaining driving distance) into speed commands in the time dimension. The longer the remaining driving distance, the higher the allowable expected speed information. As the vehicle approaches the parking position (e.g., the remaining distance approaches zero), the expected speed also smoothly approaches zero, thus enabling smooth deceleration and precise stopping at the end of the parking process.

[0094] In some embodiments, controlling a reference control quantity based on an estimated disturbance value to obtain a target control command may include: The estimated disturbance value is used as a feedforward compensation term. The estimated disturbance value is subtracted from the reference control value to obtain the target control command.

[0095] In this embodiment, the feedforward compensation term can be a control signal that acts directly on the controller based on the measurement or estimation of the disturbance, so as to cancel it out before the disturbance takes effect.

[0096] The baseline control value can be algebraically subtracted from the estimated disturbance value. The baseline control value is the theoretical control requirement calculated to achieve the desired speed tracking, while the estimated disturbance value is the predicted external resistance (such as the obstruction of speed bumps). Subtracting the predicted resistance from the theoretical requirement yields the actual output target control command that can overcome the resistance and complete the tracking simultaneously.

[0097] In this application, the output of the interference observer is directly and without delay converted into control actions, enabling the system to proactively counteract interference in advance, rather than passively waiting for errors to appear before correction. This achieves smooth obstacle crossing and reduces impact. Because the interference is partially canceled out before affecting the system output, the dynamic fluctuations of the system are significantly suppressed, resulting in a smoother vehicle parking process.

[0098] In actual implementation, such as Figure 4 As shown, the desired speed information can be obtained by calculating the remaining driving distance through proportional control: Formula 13: .

[0099] in, For the planned remaining driving distance, For proportional gain; This refers to the desired speed information.

[0100] The desired acceleration is obtained by calculating the velocity error using a PI lead compensator. The PID controller calculates the control output by performing proportional, integral, and derivative operations on the control error, and its control law is as follows: Formula 14: .

[0101] in, For controller output, This is the proportionality coefficient. To control error, That is, the error between the expected speed information and the actual speed. The integral coefficient is... is the differential coefficient.

[0102] It should be noted that this application uses a lead compensator instead of a differential element, which can reduce the impact of random noise in practical applications and avoid the impact of execution delay on control performance. The resulting new control law is as follows: Formula 15: .

[0103] in, The controller outputs (reference control quantity). This is the proportionality coefficient. To control error, The integral coefficient is... denoted as the gain coefficient of the lead compensator, a as the design parameter of the lead compensator, T as the time constant, and s as the Laplace operator.

[0104] Once the estimated disturbance value is determined, it can be superimposed onto the controller's output. Formula 16: .

[0105] in, Output to the controller (target control command). This is the proportionality coefficient. To control error, The integral coefficient is... denoted by , where 'a' is the gain coefficient of the lead compensator, 'a' is the design parameter of the lead compensator, 'T' is the time constant, and 's' is the Laplace operator. To estimate the interference value.

[0106] The disturbance estimation algorithm and the PI lead compensator can be discretized for engineering applications. A bilinear transformation can be used to discretize Equations 11, 12, and 15 for easier programming implementation.

[0107] In some embodiments, step 130 may include: Based on the target control command, determine the total required longitudinal force for the vehicle; With the goal of minimizing the slip ratio of each drive wheel of the vehicle, the total required longitudinal force is distributed to each drive wheel to control vehicle parking.

[0108] In this embodiment, the total required longitudinal force refers to the longitudinal acceleration required to achieve the target control command, calculated as the resultant force that needs to be applied to the entire vehicle. The acceleration command from the upper layer can be converted into a physical quantity of force that can be understood and executed by the lower-level actuators (such as motors and brakes).

[0109] Slip ratio is a parameter describing the motion state of a wheel. The slip ratio is zero when the wheel is rolling ideally. An excessively large slip ratio (a large positive value indicates drive slippage, and a large negative value indicates brake lock-up) indicates a decrease in the adhesion between the tire and the ground, resulting in poor vehicle stability.

[0110] When distributing wheel torque, the optimization goal is to achieve the optimal (usually the minimum) total slip ratio of the four drive wheels, so as to ensure that each wheel is in the best possible state of adhesion.

[0111] The calculated total longitudinal force can be decomposed into four independent forces, corresponding to the left front, right front, left rear, and right rear wheels, respectively.

[0112] In low-traction or asymmetrical load conditions such as parking and obstacle crossing, the adhesion potential of each wheel is different. Distributing the force with the goal of minimizing the slip ratio means that the system will actively distribute more force to the wheels with good adhesion and less prone to slippage, while reducing the driving force on the wheels that are about to slip. Under the premise of ensuring the total driving force, each tire works in its optimal range of adhesion, thereby improving the vehicle's passability, avoiding wheel jamming or slippage as much as possible, and enhancing the stability of the vehicle during driving.

[0113] By minimizing the slip ratio as the allocation objective, the system can intelligently adjust the torque of the four wheels when encountering obstacles that may cause changes in wheel adhesion, such as speed bumps or uneven road surfaces, to actively prevent and suppress slippage.

[0114] In some embodiments, distributing the total required longitudinal force to each drive wheel may include: Based on driving status information, determine the vehicle's yaw moment requirements; With the goal of meeting the vehicle's yaw moment requirements, the total required longitudinal force is distributed to each drive wheel.

[0115] In this embodiment, the yaw moment requirement refers to the torque required for the vehicle to rotate around its vertical axis (Z-axis) in order to make the vehicle follow the planned trajectory (including turning movements, etc.) during parking. It originates from the vehicle's steering intention and is used to control the rate of change of the vehicle's heading angle (yaw rate), so that the vehicle can travel smoothly and accurately along the curved parking path.

[0116] When a vehicle is moving toward a parking space, it not only needs to accelerate / decelerate longitudinally, but also needs to turn at the appropriate position. The yaw moment requirement can transform this steering geometry requirement into a dynamic command that the underlying actuator can implement.

[0117] When distributing wheel torque, the distribution result should produce a net yaw moment for the whole vehicle that is equal in magnitude and in the same direction as the required yaw moment.

[0118] In distributed drive vehicles, yaw moment can be actively generated by applying different amounts of driving force (or braking force) to the left and right wheels.

[0119] For example, a left-turning torque can be generated by increasing the driving force of the right wheel and decreasing the driving force of the left wheel, or by driving on the right side and braking on the left side.

[0120] In this application, longitudinal speed / distance tracking and lateral trajectory / heading tracking are unified at the final execution layer, avoiding potential conflicts between longitudinal and lateral controllers. By actively generating the required yaw moment, the vehicle can follow the planned curved parking path, reducing trajectory errors. This allows the vehicle to not only smoothly overcome obstacles but also accurately follow the planned trajectory, ensuring the performance of complex automatic parking tasks.

[0121] In some embodiments, distributing the total required longitudinal force to each drive wheel with the objective of minimizing the slip ratio of each drive wheel of the vehicle may include: Based on the total demand for longitudinal force and yaw moment, a multi-objective optimization function is constructed, which includes slip ratio optimization term and torque tracking term. Solve the multi-objective optimization function to determine the torque information corresponding to each drive wheel.

[0122] In this embodiment, the slip ratio optimization term is the mathematical part of the optimization function that directly reflects the minimum slip ratio target. It can be a function of the slip ratio of each wheel. The value of this function increases as the slip ratio increases. The task of the optimization algorithm is to find a set of wheel force distribution schemes that minimizes the value of this term.

[0123] The torque tracking term is the mathematical part of the optimization function that directly reflects the goal of meeting the yaw moment requirement. It can be used to measure the difference between the actual vehicle yaw moment calculated by the current allocation scheme and the yaw moment requirement issued by the upper-level controller. The task of the optimization algorithm is to minimize this error.

[0124] The slip ratio optimization term and the torque tracking term can be combined into a single mathematical expression. For example, the multi-objective optimization function can be represented by a weighted sum. By adjusting the weights, the degree of emphasis on the two objectives can be balanced.

[0125] An optimization algorithm can be used to search within the feasible wheel torque combination space to find a set of specific values ​​that minimizes (or satisfies) the multi-objective optimization function value. For example, it can be the optimal driving or braking torque values ​​corresponding to the four drive wheels: left front, right front, left rear, and right rear.

[0126] In some embodiments, solving a multi-objective optimization function to determine the torque information corresponding to each drive wheel may include: The multi-objective optimization function is solved iteratively to determine the torque information corresponding to each drive wheel.

[0127] In this embodiment, mathematical programming or intelligent heuristic algorithms can be used to iteratively solve the multi-objective optimization function. For example, weighted sum method, Newton's weighted sum method, hybrid frog leap algorithm, particle swarm optimization or differential evolution algorithm can be used to solve the problem. This application does not limit the scope of the solution.

[0128] Taking the use of the hybrid frog leaping algorithm to iteratively solve a multi-objective optimization function as an example, the hybrid frog leaping algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of frog groups in nature. It divides the entire search group into multiple subgroups, and the algorithm alternates between two stages: local deep search within a subgroup and global information exchange between subgroups, in order to balance the algorithm's local mining ability and global exploration ability.

[0129] The algorithm can start with a random initial solution set (a set of possible wheel torque distribution schemes) and update and improve these solutions generation by generation according to its rules (imitating the jumping and competition of frogs) until it finds an optimal or near-optimal solution that satisfies the stopping conditions (such as reaching the maximum number of iterations or the quality of the solution is good enough).

[0130] In practical implementation, the optimal tire force distribution method for distributed drive vehicles can be used to control vehicle parking. For example... Figure 5 As shown, the steering angle and desired acceleration of the front wheels can be obtained. The front wheel steering angle is obtained from the output of the lateral controller. The total output of the longitudinal PI lead compensator and the disturbance observer is the desired acceleration.

[0131] Formula 17: .

[0132] in, The desired acceleration (i.e., the longitudinal acceleration required to achieve the target control command). This is the proportionality coefficient. To control error, The integral coefficient is... denoted by , where 'a' is the gain coefficient of the lead compensator, 'a' is the design parameter of the lead compensator, 'T' is the time constant, and 's' is the Laplace operator. To estimate the interference value.

[0133] Calculate the yaw moment and total longitudinal force required for steering to meet speed control requirements. The equation for the total longitudinal force is as follows: Formula 18: .

[0134] in, For the longitudinal force of total demand, For the desired acceleration, is the control coefficient, and m is the mass.

[0135] In low-speed parking conditions, to meet the heading angle tracking requirement, a model-free adaptive control method is used to solve for the yaw moment requirement: Formula 19: .

[0136] in, Let yaw moment be the expected yaw moment (yaw moment requirement) at time k+1. Let k be the yaw moment value. and Step size factor It is a pseudo-partial derivative. Let the expected heading angle be at time k+1. The actual heading angle at time k.

[0137] The design is based on optimizing the objective function for the optimal slip ratio. The slip ratio is calculated using the following formula: Formula 20: .

[0138] in, This represents the slip ratio of the i-th drive wheel. Let be the linear velocity of the i-th driving wheel. Let be the rotational speed of the i-th drive wheel. This is the effective turning radius of the wheel.

[0139] To minimize the slip ratio, an optimization objective function can be constructed. : Formula 21: .

[0140] in, The longitudinal force allocated to the i-th drive wheel. This represents the slip ratio of the i-th driving wheel.

[0141] To meet both vertical and horizontal control requirements, an optimization objective function can be constructed. : Formula 22: .

[0142] in, The longitudinal force allocated to the i-th drive wheel. The total required longitudinal force is given by B, which is half the wheelbase. Let yaw moment be the expected yaw moment (yaw moment requirement) at time k+1.

[0143] The final multi-objective optimization function is: Formula 23: .

[0144] in, A coefficient ranging from 0 to 1. For slip ratio optimization term, This is the torque tracking term.

[0145] The tire force is iteratively optimized and the desired torque value is output to the underlying execution controller.

[0146] The solution can be found using the hybrid frog leaping algorithm: First, the population is initialized. Let the number of communities be M, the number of individuals in each community be N, the total number of individuals be F = M * N, and the maximum number of iterations be T.

[0147] Individual refers to the vector composed of the optimal tire forces of each wheel: Formula 24: .

[0148] in, This represents the value of the j-th individual in the i-th iteration, which is the optimal torque value output by the left front wheel. Similarly... , and This indicates the optimal output torque values ​​for the right front wheel, left rear wheel, and right rear wheel.

[0149] Assuming that the hub motors of each wheel have exactly the same characteristics, initialize these torque values: Formula 25: .

[0150] in, and These represent the maximum and minimum values ​​of the torque output by the hub motor, respectively. A random number between 0 and 1.

[0151] Calculate the fitness value of each individual, and substitute the generated individuals into formula 23 to obtain: Formula 26: .

[0152] Based on the calculated fitness values, sorting them in descending order can form M communities, each with N individuals.

[0153] Assume that the individual corresponding to the maximum fitness value at this time is .

[0154] Update the positions of individuals in communities 1 to M. Assume the... The individual with the best fitness in a community is As for the other individuals among them Update its position according to the following formula: Formula 27: .

[0155] If the updated Corresponding fitness value Smaller than the original position The position will then be updated to: Formula 28: .

[0156] If at this time Corresponding fitness value It is still smaller than the original position. Then its position will be updated to a random position within the feasible range: Formula 29: .

[0157] Perform the above update operation on each individual in M ​​communities to find the globally optimal fitness value and its location, i.e., the vector corresponding to the maximum fitness value.

[0158] Sort the updated fitness values ​​in descending order and record the corresponding vector values.

[0159] Determine whether the maximum number of iterations has been reached and whether the output torque value satisfies the friction circle constraint. If the above conditions are met, output the torque vector corresponding to the global optimal position; otherwise, return to the step of updating the individual positions in the 1st to Mth communities for re-iteration calculation.

[0160] In this application, a hybrid frog-jumping algorithm is used to iteratively solve the multi-objective optimization function to determine the torque information corresponding to each drive wheel. This method has low computational complexity and strong optimization performance.

[0161] The following is combined with Figure 2 The vehicle control method provided in the embodiments of this application will be described in general.

[0162] like Figure 2As shown, the remaining driving distance can be obtained based on the trajectory planning module.

[0163] The driving state information of the vehicle during driving is estimated by using sensors and state parameter estimators, and the concentrated interference during driving is calculated by the interference estimation module.

[0164] The longitudinal acceleration required during obstacle crossing can be calculated through the control module. For example, the desired speed information can be obtained based on the remaining travel distance. By comparing the desired speed information with the actual speed, the speed error can be obtained. Using a PI controller (with a lead compensator replacing the differential term), the theoretical control quantity (i.e., the reference control quantity, which can be the reference acceleration) required to eliminate the speed error can be obtained.

[0165] The disturbance estimate is used as a feedforward compensation term to compensate the reference control quantity, thus obtaining the final acceleration command (target control command).

[0166] The torque distribution module can convert the target control command into the total longitudinal force required by the vehicle, and at the same time calculate the yaw moment required to maintain trajectory tracking based on the vehicle's steering intention (i.e., the front wheel steering angle).

[0167] In the process of distributing torque, the optimal slip ratio can be used to distribute the tire force of each wheel as an optimization objective, while also meeting the requirements of total longitudinal force and yaw moment. For example, a multi-objective optimization function can be constructed and intelligent algorithms such as the hybrid frog jump algorithm can be used to solve the multi-objective optimization function and distribute the tire force of each wheel.

[0168] The vehicle control method provided in this application can be executed by the vehicle's control system. This application uses the vehicle's control system executing the vehicle control method as an example to illustrate the vehicle control system provided in this application.

[0169] This application also provides a vehicle control system.

[0170] like Figure 7 As shown, the vehicle's control system includes: a first processing module 710, a second processing module 720, and a third processing module 730.

[0171] The first processing module 710 is used to determine the estimated interference value of the vehicle during the parking process based on the vehicle's driving status information. The second processing module 720 is used to perform control based on the remaining driving distance between the vehicle and the parking position, driving status information and estimated disturbance value, and to determine the target control command. The third processing module 730 is used to control vehicle parking based on target control commands, thereby increasing the vehicle's acceleration during obstacle crossing.

[0172] According to the vehicle control system provided in the embodiments of this application, during automatic parking, an estimated disturbance value representing the sum of external disturbances such as uneven road surfaces is estimated using vehicle driving status information. Then, when generating vehicle control commands based on the remaining distance, the estimated disturbance value is actively introduced for feedforward compensation to obtain the target control command used to drive the vehicle. This enables the vehicle control system to proactively anticipate and counteract the impact caused by obstacles such as speed bumps or steps, significantly improving the longitudinal speed stability during obstacle crossing, increasing the vehicle's acceleration during obstacle crossing, effectively avoiding problems such as wheel jamming, difficulty starting, and repeated body rebound, and enhancing the reliability, success rate, and ride comfort of the automatic parking system on complex road surfaces.

[0173] In some embodiments, the second processing module 720 may be used for: Determine the desired speed information based on the remaining driving distance; Based on the error between the desired speed information and the actual speed in the driving status information, a reference control quantity is generated; The target control command is obtained by controlling the reference control quantity based on the estimated disturbance value.

[0174] In some embodiments, the second processing module 720 may be used for: The remaining driving distance is input to the proportional controller to obtain the desired speed information output by the proportional controller.

[0175] In some embodiments, the second processing module 720 may be used for: The estimated disturbance value is used as a feedforward compensation term. The estimated disturbance value is subtracted from the reference control value to obtain the target control command.

[0176] In some embodiments, the first processing module 710 may be used for: A longitudinal dynamics model of the vehicle is established, which includes a disturbance term to characterize the total external disturbance. Based on the vehicle's longitudinal dynamics model, driving state information, and control input, the estimated disturbance value corresponding to the disturbance term is determined through a disturbance observer.

[0177] In some embodiments, the third processing module 730 may be used for: Based on the target control command, determine the total required longitudinal force for the vehicle; With the goal of minimizing the slip ratio of each drive wheel of the vehicle, the total required longitudinal force is distributed to each drive wheel to control vehicle parking.

[0178] In some embodiments, the third processing module 730 may be used for: Based on driving status information, determine the vehicle's yaw moment requirements; With the goal of meeting the vehicle's yaw moment requirements, the total required longitudinal force is distributed to each drive wheel.

[0179] In some embodiments, the third processing module 730 may be used for: Based on the total demand for longitudinal force and yaw moment, a multi-objective optimization function is constructed, which includes slip ratio optimization term and torque tracking term. Solve the multi-objective optimization function to determine the torque information corresponding to each drive wheel.

[0180] In some embodiments, the third processing module 730 may be used for: The multi-objective optimization function is solved iteratively to determine the torque information corresponding to each drive wheel.

[0181] The vehicle control system in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the scope of the electronic device.

[0182] The vehicle control system in this embodiment can be a device with an operating system. This operating system can be a Microsoft (Windows) operating system, an Android operating system, an iOS operating system, or other possible operating systems; this embodiment does not specifically limit the specific operating system.

[0183] The vehicle control system provided in this application embodiment can achieve Figures 1 to 6 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0184] In some embodiments, this application also provides a vehicle, including a controller, which is used to execute the vehicle control method as described in any of the above embodiments.

[0185] In some embodiments, the vehicle includes a plurality of drive wheels.

[0186] In this embodiment, multiple drive wheels are configured to move based on the vehicle control method described in any of the above embodiments to control vehicle parking.

[0187] In some embodiments, such as Figure 8 As shown, this application embodiment also provides an electronic device 800, including a processor 801, a memory 802, and a computer program stored in the memory 802 and executable on the processor 801. When the program is executed by the processor 801, it implements the various processes of the above-described vehicle control method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0188] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0189] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described vehicle control method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0190] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0191] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described vehicle control method.

[0192] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0193] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described vehicle control method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0194] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0195] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0196] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the related technology, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0197] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0198] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0199] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for controlling a vehicle, characterized in that, include: Based on the vehicle's driving status information, the estimated interference value of the vehicle during the parking and driving process is determined; Control is performed based on the remaining driving distance between the vehicle and the parking position, the driving status information, and the estimated disturbance value to determine the target control command; The vehicle is controlled to park based on the target control command, thereby increasing the vehicle's acceleration during obstacle crossing.

2. The vehicle control method according to claim 1, characterized in that, The control method, which determines the target control command based on the remaining driving distance between the vehicle and the parking position, the driving status information, and the estimated disturbance value, includes: Determine the desired speed information based on the remaining driving distance; Based on the error between the desired speed information and the actual speed in the driving status information, a reference control quantity is generated; The target control command is obtained by controlling the reference control quantity based on the estimated disturbance value.

3. The vehicle control method according to claim 2, characterized in that, The determination of the desired speed information based on the remaining driving distance includes: The remaining driving distance is input to the proportional controller to obtain the desired speed information output by the proportional controller.

4. The vehicle control method according to claim 2, characterized in that, The step of controlling the reference control quantity based on the estimated disturbance value to obtain the target control command includes: The estimated disturbance value is used as a feedforward compensation term, and the estimated disturbance value is subtracted from the reference control value to obtain the target control command.

5. The vehicle control method according to any one of claims 1-4, characterized in that, The step of determining the estimated interference value of the vehicle during parking and driving based on the vehicle's driving status information includes: A longitudinal dynamics model of the vehicle is established, which includes a disturbance term to characterize the total external disturbance. Based on the vehicle longitudinal dynamics model, the driving state information, and the control input, the estimated interference value corresponding to the interference term is determined by the interference observer.

6. The vehicle control method according to any one of claims 1-5, characterized in that, The method of controlling the vehicle parking based on the target control command includes: Based on the target control command, the total required longitudinal force for the vehicle is determined; With the goal of minimizing the slip ratio of each drive wheel of the vehicle, the total required longitudinal force is distributed to each of the drive wheels to control the parking of the vehicle.

7. The vehicle control method according to claim 6, characterized in that, The process of distributing the total required longitudinal force to each of the drive wheels includes: Based on the driving status information, the yaw moment requirement of the vehicle is determined; With the goal of meeting the yaw moment requirements of the vehicle, the total required longitudinal force is distributed to each of the drive wheels.

8. The vehicle control method according to claim 7, characterized in that, The method of distributing the total required longitudinal force to each of the drive wheels with the objective of minimizing the slip ratio of each drive wheel of the vehicle includes: Based on the total required longitudinal force and the required yaw moment, a multi-objective optimization function is constructed, which includes a slip ratio optimization term and a moment tracking term. Solve the multi-objective optimization function to determine the torque information corresponding to each of the drive wheels.

9. The vehicle control method according to claim 8, characterized in that, Solving the multi-objective optimization function to determine the torque information corresponding to each of the drive wheels includes: The multi-objective optimization function is iteratively solved to determine the torque information corresponding to each of the drive wheels.

10. A vehicle control system, characterized in that, include: The first processing module is used to determine the estimated interference value of the vehicle during the parking process based on the vehicle's driving status information. The second processing module is used to perform control based on the remaining driving distance between the vehicle and the parking position, the driving status information, and the estimated interference value, and to determine the target control command. The third processing module is used to control the vehicle to park based on the target control command, thereby increasing the vehicle's acceleration during obstacle crossing.

11. A vehicle, characterized in that, Includes a controller for performing the vehicle control method as described in any one of claims 1-9.

12. The vehicle according to claim 11, characterized in that, include: Multiple drive wheels, the drive wheels being configured to move based on the vehicle control method as described in any one of claims 1-9, to control the parking of the vehicle.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the vehicle control method as described in any one of claims 1-9.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the vehicle control method as described in any one of claims 1-9.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle control method as described in any one of claims 1-9.

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