Method and apparatus for vehicle control

Abstract

A method of controlling a vehicle includes: receiving a plurality of data related to vehicle steering; detecting target data which is determined to meet a preset condition in the multiple pieces of data; obtaining an optimization model parameter that minimizes a cumulative error between a predicted yaw rate of a yaw rate model of the vehicle and a measured yaw rate of the vehicle based on the target data and the optimization model; and updating a yaw rate model of the vehicle by using the optimized model parameters.

Description

[0001] This application claims the benefit of Korean Patent Application No. 10-2023-0140714 filed in the Korean Intellectual Property Office on October 19, 2023, the disclosure of which is incorporated herein by reference for all purposes. Technical Field

[0002] The following description relates to methods and apparatus for vehicle control. Background Art

[0003] When designing and applying vehicle steering control, it is beneficial to accurately predict the yaw rate of the steering angle. In terms of controller design, when the actual motion of the vehicle is accurately predicted based on the control command, the motion of the vehicle can be predicted and controlled very finely. The accuracy of the control command on the vehicle steering control can be calibrated for situations such as changing tires, modifying the vehicle, and changing the road environment. Summary of the invention

[0004] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be an aid in determining the scope of the claimed subject matter.

[0005] In one general aspect, a method for controlling a vehicle includes: receiving a plurality of data related to vehicle steering; detecting target data determined to satisfy a predetermined condition in the plurality of data; obtaining optimized model parameters based on the target data and an optimized model, the optimized model parameters minimizing a cumulative error between a predicted yaw rate of a yaw rate model of the vehicle and a measured yaw rate of the vehicle; and updating the yaw rate model of the vehicle by using the optimized model parameters.

[0006] Obtaining the optimized model parameters may include applying a learning-based optimization to a yaw rate model of the vehicle to determine the optimized model parameters.

[0007] The method may further include determining whether to perform a calibration optimization system, and the determination may be based on a cumulative error between a predicted yaw rate of the vehicle and a measured yaw rate value of the vehicle.

[0008] Determining whether to execute the calibration optimization system may be based on user input indicating that the calibration optimization system is to be executed, which user input may be received via a user interface of the vehicle.

[0009] The method may further include updating a control model of the vehicle based on the updated vehicle yaw rate model.

[0010] Updating the control model of the vehicle may include applying the updated yaw rate model to a steering wheel controller of the vehicle.

[0011] The predetermined condition may include a lateral acceleration threshold of the vehicle and a yaw rate change threshold of the vehicle.

[0012] The plurality of data related to the steering of the vehicle may include a lateral acceleration of the vehicle, a yaw rate of the vehicle, a speed of the vehicle, and a steering angle.

[0013] The yaw rate model may include a dynamic model or a kinematic model, either of which may be used to perform calibration corresponding to the turning of the vehicle.

[0014] The calibration may increase the accuracy of the predicted yaw rate used to control the steering of the vehicle.

[0015] In another general aspect, an apparatus for controlling a vehicle includes one or more processors configured to: receive a plurality of data related to steering of the vehicle; detect target data in the plurality of data that is determined to satisfy a predetermined condition; obtain optimized model parameters based on the target data and an optimized model, the optimized model parameters minimizing a cumulative error between a predicted yaw rate of a yaw rate model of the vehicle and a measured yaw rate of the vehicle; and update the yaw rate model of the vehicle by using the optimized model parameters.

[0016] The one or more processors may also be configured to apply a learning-based optimization to a yaw rate model of the vehicle to determine optimized model parameters.

[0017] The one or more processors may be configured to execute an error detector configured to determine whether to execute a calibration optimization system based on a cumulative error between a predicted yaw rate of the vehicle and a measured yaw rate value of the vehicle.

[0018] The one or more processors may also be configured to receive user input through a user interface of the vehicle indicating that a calibration optimization system is to be performed.

[0019] The one or more processors may also be configured to update a control model of the vehicle based on the updated yaw rate model of the vehicle.

[0020] The control model may include a steering wheel controller for the vehicle.

[0021] The predetermined condition may include a lateral acceleration threshold of the vehicle and a yaw rate change threshold of the vehicle.

[0022] The plurality of data related to the steering of the vehicle may include the lateral acceleration of the vehicle, the yaw rate of the vehicle, the speed of the vehicle, and the driver steering angle.

[0023] The yaw rate model may include a dynamic model or a kinematic model, either of which may be used to perform calibration corresponding to the turning of the vehicle.

[0024] In another general aspect, an electronic device of a vehicle includes: one or more processors; a memory storing instructions, the instructions being configured to, when executed by the one or more processors: set a target yaw rate of a vehicle for rotationally driving the vehicle, control the yaw rate of the vehicle to achieve the target yaw rate by using a yaw rate model of the vehicle, obtain data related to autonomous steering of the vehicle, determine whether to update the yaw rate model, wherein the determination is based on a cumulative error between a predicted yaw rate of the yaw rate model and a measured yaw rate of the autonomous vehicle, and based on the determination, update the yaw rate model according to the data; a display configured to display a user interface according to the instructions; wherein the instructions are further configured to cause the one or more processors to: receive a plurality of data related to steering of the vehicle, obtain optimized model parameters based on determining that target data in the plurality of data satisfies a predetermined condition, the optimized model parameters minimizing the cumulative error between the predicted yaw rate and the measured yaw rate, and update the yaw rate model of the autonomous vehicle by using the optimized model parameters.

[0025] Other features and aspects will become apparent from the following detailed description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1A An example of a steering calibration system is shown in accordance with one or more embodiments.

[0027] Figure 1B Sliding window yaw rate error is shown in accordance with one or more embodiments.

[0028] Figure 2 Example operations of a steering calibration system in accordance with one or more embodiments are shown.

[0029] Figure 3 An example of a steering calibration system is shown in accordance with one or more embodiments.

[0030] Figure 4 An example vehicle path associated with a steering control system is shown according to one or more embodiments.

[0031] Figure 5 Examples of kinematic models and dynamic models are shown in accordance with one or more embodiments.

[0032] Figure 6 An example of the relationship between steering angle and lateral acceleration for different conditions of an object data detector according to one or more embodiments is shown.

[0033] Figure 7 An example of the operation of a dynamic model in accordance with one or more embodiments is shown.

[0034] Figure 8An example of a controller for a vehicle according to one or more embodiments is shown.

[0035] Fig. 9 An example of operation of a vehicle according to one or more embodiments is shown.

[0036] Fig.10 An example of an electronic device in accordance with one or more embodiments is shown.

[0037] Throughout the drawings and detailed description, unless otherwise described or provided, the same or similar reference numerals should be understood to refer to the same or similar elements, features, and structures. The drawings may not be drawn to scale, and the relative sizes, proportions, and depictions of the elements in the drawings may be exaggerated for clarity, illustration, and convenience. DETAILED DESCRIPTION

[0038] The following detailed description is provided to help the reader obtain a comprehensive understanding of the method, device and / or system described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the method, device and / or system described herein will be apparent. For example, the order of operations described herein is merely an example and is not limited to those order of operations set forth herein, but can be significantly changed after understanding the disclosure of the present application, except for operations that must be performed in a certain order. In addition, for greater clarity and brevity, the description of features known after understanding the disclosure of the present application may be omitted.

[0039] The features described herein may be implemented in different forms and should not be construed as being limited to the examples described herein. Rather, the examples described herein are provided merely to illustrate some of the many possible ways to implement the methods, devices, and / or systems described herein, which will be apparent after understanding the disclosure of the present application.

[0040] The terms used herein are only used to describe various examples and are not used to limit the present disclosure. Unless the context clearly indicates otherwise, the articles "a", "an" and "the" are also intended to include plural forms. As used herein, the term "and / or" includes any one of the items listed in the association and any combination of any two or more. As non-limiting examples, the terms "include", "comprise" and "have" represent the presence of the described features, numbers, operations, components, elements and / or combinations thereof, but do not exclude the presence or addition of one or more other features, numbers, operations, components, elements and / or combinations thereof.

[0041] Throughout the specification, when a component or element is described as being "connected to," "coupled to," or "engaged to" another component or element, it may be directly "connected to," "coupled to," or "engaged to" another component or element, or one or more other components or elements may reasonably be present in between. When a component or element is described as being "directly connected to," "directly coupled to," or "directly engaged to" another component or element, no other elements may intervene. Similarly, expressions such as "between" and "immediately between," and "adjacent to" and "immediately adjacent to" may also be interpreted as described above.

[0042] Although terms such as "first", "second" and "third" or A, B, (a), (b) may be used herein to describe various components, assemblies, regions, layers or parts, these components, components, regions, layers or parts are not limited by these terms. For example, each of these terms is not used to define the nature, order or sequence of the corresponding component, component, region, layer or part, but is only used to distinguish the corresponding component, component, region, layer or part from other components, components, regions, layers or parts. Therefore, without departing from the teachings of the examples described herein, the first component, component, region, layer or part mentioned in the examples may also be referred to as the second component, component, region, layer or part.

[0043] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those commonly understood by a person of ordinary skill in the art to which the present disclosure belongs based on an understanding of the disclosure of the present application. Terms such as those defined in commonly used dictionaries should be interpreted as having the same meaning as in the context of the relevant technology and / or the present application, and should not be interpreted as ideal or overly formal meanings unless explicitly defined as such herein. In this article, the use of the term "may" with respect to an example or embodiment (e.g., with respect to what an example or embodiment may include or implement) means that there is at least one example or embodiment in which such a feature is included or implemented, but all examples are not limited thereto.

[0044] Figure 1A An example of a steering calibration system is shown in accordance with one or more embodiments.

[0045] Reference Figure 1A, one or more blocks and combinations thereof may be implemented by a combination of a computer and / or processor executable instructions and dedicated hardware based on dedicated hardware that performs a predetermined function. The dedicated hardware may be a general-purpose processor configured by processor executable instructions. The information disclosed herein is sufficient to enable people to physically implement the disclosed embodiments and examples using hardware / software development tools. For example, the information disclosed herein may be easily translated by engineers, etc. into source code and / or high-level circuit designs, which may be translated (e.g., in the former case, by a compiler) into machine executable code or translated into low-level circuit configurations (e.g., in the latter case, using integrated circuit design tools).

[0046] According to an embodiment of the invention, a processor (eg, Fig.10 The processor 1030 of the steering calibration system 100 may perform operations of the steering calibration system 100 (“processor” is used herein as a shorthand to refer to any combination of processing hardware described later). The steering calibration system 100 may include a yaw rate model 104, an error detector 110, and a calibration optimization system 120.

[0047] According to one or more embodiments, the steering calibration system 100 may receive a plurality of data related to the steering of the vehicle from any one of a plurality of sensors (e.g., an inertial measurement unit (IMU) 101, a global positioning system (GPS) 102, and / or a steering angle sensor 103). The steering calibration system 100 may receive a lateral acceleration a of the vehicle from the IMU 101. y and the yaw rate γ of the vehicle. The yaw rate may be, for example, an angular velocity. The steering calibration system 100 may receive the longitudinal velocity v of the vehicle from the GPS 102. x (speed directly ahead). The steering calibration system 100 can receive the driver's steering angle δ from the steering angle sensor 103 f,SWA (e.g., the angle the steering wheel is turned from center) to distinguish it from the actual direction / heading of the vehicle. In some places, the term "driver steering angle" will be referred to as "steering angle" to distinguish it from "wheel steering angle" (WSA).

[0048] According to one or more embodiments, the yaw rate model 104 may be configured to predict a yaw rate response to a steering angle of the vehicle. The yaw rate response may be a change in yaw rate as a function of the steering angle of the vehicle. For example, if a steering angle is input to the yaw rate model 104 (in addition to other possible inputs such as forward speed), the yaw rate model 104 may predict a change in yaw rate. For example, a model that may be used for a steering controller for the vehicle may be a kinematic model (see Equation 6) or a dynamic model (or bicycle model) (see Equations 1 and 2). In some cases, a kinematic model may not accurately predict / express the motion of the vehicle. For example, a kinematic model typically does not take into account the understeer characteristics of the vehicle and tire slippage of the vehicle (e.g., when turning at high speeds or in slippery conditions) and may not be accurate under such conditions. However, a kinematic model can be easily used to account for vehicle model parameters (e.g., such as C r and C f In contrast, a dynamic model can have superior vehicle motion prediction performance, especially when compared to a kinematic model. However, a dynamic model may require additional measurement equipment that a kinematic model does not, because the dynamic model requires the above vehicle model parameters, while the kinematic model does not. Therefore, it may be helpful to determine when a kinematic model can be reliably used (e.g., to indicate conditions without slip or understeer / oversteer). Regarding the above vehicle model parameters that a dynamic model requires but a kinematic model does not, compare Figure 5 The kinetic model 520 in Figure 5 Kinematic model 510 in.

[0049] When the longitudinal velocity v x and the driver steering angle δ f,SWA When input into the yaw rate model 104 in the steering calibration system 100, the yaw rate model 104 can derive / infer the model yaw rate γ SS as predicted values ​​(where “SS” stands for “steady state”).

[0050] According to an embodiment, the error detector 110 may calculate the yaw rate γ received from the IMU 101 and the model yaw rate γ received from the yaw rate model. SS For example, the error detector 110 may maintain pairs of yaw rates γ and model yaw rates γ corresponding to each other in time over a moving time window (or sample count window). SSThe calculated difference between the yaw differences and the yaw differences are maintained within a moving window. The error detector 110 may determine whether calibration is needed by using a threshold value for the cumulative error. The error detector 110 may calculate the cumulative error in real time and may monitor the need for calibration, for example, by repeatedly adding and removing differences from a set of differences included in a moving window, by updating the sum of the differences in the window accordingly, and repeatedly comparing the cumulative error to its threshold value. The cumulative error may also be implemented as an average error over a moving window.

[0051] Figure 1B FIG. 4 shows a sliding window yaw rate error according to one or more embodiments. Figure 1B As shown, the error detector 110 calculates the cumulative error of each sliding time window and compares the cumulative error with the error threshold in real time, so as to monitor whether the yaw rate model calibration is needed. The cumulative error is used to distinguish the time error caused by the white noise of the sensor from the bias error caused by the incorrect calibration of the yaw rate model, because the time error caused by the white noise can usually be ignored in the cumulative error.

[0052] According to one or more embodiments, the calibration optimization system 120 may receive a calibration request from the error detector 110 , for example based on determining that the accumulated error has exceeded a threshold.

[0053] When receiving the calibration request, the target data detector 121 may receive the lateral acceleration a y , yaw rate γ, speed v x and the driver steering angle δ f,SWA (These driving data may be provided continuously or may be sent in response to error detection). The received driving data may correspond to the time of the error detected by the error detector 110 in chronological order. The target data detector 121 may detect data whose value is determined (by calculation described later) in the received data to satisfy (i) the lateral acceleration a of the vehicle y The threshold values ​​are (i) a threshold value for the absolute value of the vehicle's yaw rate γ, and / or (ii) a threshold value for the absolute value of the change (δ) in the vehicle's yaw rate γ (typically data related to understeer / oversteer and / or lateral slip). As described below, when these conditions are met (e.g., when the vehicle has sufficiently low lateral acceleration and / or yaw rate), simplifying assumptions related to modeling the yaw rate can be made.

[0054] According to an embodiment, the optimization model 122 as part of the calibration optimization system 120 can obtain parameters of the optimization model 122 that minimize the cumulative error between (i) the measured yaw rate γ (measured by using the data received from the target data detector 121) and (ii) the predicted model yaw rate (derived from the yaw rate model 104). In this case, the optimization model 122 can obtain the parameters of the optimization model 122 through learning-based optimization, which can be performed by, for example, a genetic algorithm described after some explanation of the parameters of the optimization model.

[0055] The parameters of the optimization model are the parameters used to update the yaw rate model in the direction that minimizes the accumulated error. The operation of the optimization model can be explained using the following equation. To further explain, in Equation 4, (written below "argmin") p = (K us , SGR) is the optimization variable. In other words, it is a variable that can be obtained from the results of optimization. us represents the understeer gradient, and SGR represents the ratio between the driver’s steering angle and the wheel angle. In the case of the bicycle model, replacing K originally in Eq. us and the values ​​obtained in SGR, replacing the variables obtained through optimization. us The previous values ​​of SGR and yaw rate do not match the measured values. It can be seen that an optimization is performed to match the measured values ​​and the newly obtained K us and SGR instead of K us and the previous value of SGR.

[0056] In the case of the kinematic model, assuming that K us = 0, repeat the same process; that is, for Equation 3 and Equation 4, optimize to K us = 0 and replace it equally.

[0057] For example, the processor 1030 using the steering calibration system 100 may apply the optimization model 122 to the dynamic model by using the algorithm expressed by equations 1 to 5. Regarding applying the optimization model 122, for example, when the processor drives the steering calibration system, if the yaw rate model uses the dynamic model, the algorithm by which the optimization model performs optimization may be expressed by equations 1 to 5 described below. Before describing equations 1 to 5, some preliminary explanations of the types and characteristics of the sliding model are discussed.

[0058] Front / rear tire slip angle and sideslip angle are different concepts used in vehicle dynamics to describe different aspects of a vehicle's motion and direction relative to the direction it is traveling. Here are the definitions of these terms and an explanation of their differences:

[0059] Front Tire Slip Angle - This is the angle between the direction the front tire is pointing (due to steering input) and the actual direction the tire is moving relative to the ground. This happens because the tire does not change direction instantaneously and it deforms laterally as it starts to turn.

[0060] Rear Tire Slip Angle - Similar to the front tire slip angle, this is the angle between the direction the rear tires are aligned and the actual direction the tires are moving relative to the ground. Since the rear tires are not usually turned directly, their slip angle is affected by the overall motion of the vehicle, including any yaw motion.

[0061] Sideslip Angle (β) - Sideslip angle is the angle between the vehicle's heading (the direction it is pointing or desired to go) and the vehicle's actual path (the direction it is traveling) as it moves forward. This can be affected by factors such as road conditions, speed, and vehicle dynamics. Sideslip angle is a measure of how much a vehicle "slips" sideways as it moves forward.

[0062] Front and rear tire slip angles relate to the angle at which the tire itself slips relative to its direct direction of motion. Conversely, sideslip angle refers to the overall direction of the vehicle relative to its path of travel.

[0063] Tire slip angle is directly affected by steering inputs (for the front tires) and vehicle dynamics (for the front and rear tires) such as acceleration, deceleration, and cornering. Side slip angle can be viewed as the effect of these inputs and dynamics, representing the vehicle's response to them in a larger context.

[0064] Tire slip angle helps understand how a tire behaves under various cornering and dynamic conditions, which is critical to grip and handling. Side slip angle provides insight into vehicle stability and directional control.

[0065] In summary, while tire slip angle is specific to the direction of the tire relative to its motion, sideslip angle is about the direction of the vehicle relative to its overall path of travel. Each of these angles provides valuable information for analyzing and improving vehicle performance and safety.

[0066] The equations herein are shorthand descriptions of hardware and / or machine executable code / instructions. Engineers can easily convert the equations disclosed herein into source code, high-level circuit designs, etc., which can be translated into code / instructions and / or circuits that function as described by the equations by using software or hardware engineering tools.

[0067] Equation 1 (kinetic model)

[0068]

[0069] in, represents the change of β (sideslip angle), Indicates the change in γ (yaw rate).

[0070] Equation 2 (steady-state kinetic model)

[0071]

[0072] Equation 3 (Optimization model using steady-state yaw rate model based on dynamic model)

[0073]

[0074] Equation 4 (Optimization problem using Equation 3)

[0075]

[0076] in

[0077] Equation 5

[0078] b′=b 0 +mutation*(population[rand0]-population[rand1])

[0079] Equation 1 can represent a kinetic model (e.g., Figure 5 The implementation of the dynamic model 520). Figure 7 The state of the dynamic model 520 can be defined by the sideslip angle β and the yaw rate γ. The sideslip angle β is the angle between the vehicle's orientation (forward direction) and the vehicle's actual moving direction. The yaw rate γ is the rotation rate of the vehicle around the z-axis. The various vehicle model parameters may include the following parameters. Front tire cornering stiffness C f It can be the front tire lateral force ratio, F y =C f ×Front tire slip angle, formed by the front tire slip angle (discussed later). Rear tire cornering stiffness C r is the rear tire lateral force ratio, F y =C r ×Rear tire slip angle, formed by the rear tire slip angle. m represents the total mass of the vehicle. v x The longitudinal velocity v of the vehicle x I z Represents the vehicle's moment of inertia around the z-axis. f Represents the distance from the vehicle's center of mass to the front tire axle. r represents the distance from the vehicle's center of mass to the rear tire axle. As mentioned above, δ f,WSA Indicates the vehicle's wheel steering angle.

[0080] γ ss can be provided by Equation 2, which indicates that when and The steady-state kinetic model for represents the change of β, represents the change of γ. L represents the distance from the front tire axis to the rear tire axis. K us is the wheel steering angle δ f,WSA Relative to the lateral acceleration a y The instantaneous ratio of the change in the wheel steering angle relative to the lateral acceleration. Figure 6 K us Describe in detail.

[0081] Before describing Equation 3, some descriptions of various steering angles and slip angles are provided.

[0082] Front / rear tire slip angle and sideslip angle are different concepts used in vehicle dynamics to describe different aspects of a vehicle's motion and direction relative to the direction of travel. Here are some explanations of these terms and their differences:

[0083] Front Tire Slip Angle - This is the angle between the direction the front tire is pointing (due to steering input) and the actual direction the tire is moving relative to the ground. This happens because the tire does not change direction instantaneously and it deforms laterally as it starts to turn.

[0084] Rear Tire Slip Angle - Similar to the front tire slip angle, this is the angle between the direction the rear tires are aligned and the actual direction the tires are moving relative to the ground. The rear tires are not directly steered, and their slip angle is affected by the overall motion of the vehicle, including any yaw motion.

[0085] Sideslip Angle (β) - Sideslip angle is the angle between the vehicle's heading (the direction it is pointing or desired to go) and the actual path the vehicle is taking as it moves forward. This can be affected by factors such as road conditions, speed and vehicle dynamics. Sideslip angle is a measure of how much a vehicle "slips" sideways as it moves forward.

[0086] Front and rear tire slip angles relate to the angle at which the tire itself slips relative to its direct direction of motion. Conversely, sideslip angle refers to the overall direction of the vehicle relative to its path of travel.

[0087] Tire slip angle is directly affected by steering inputs (for the front tires) and vehicle dynamics (for the front and rear tires) such as acceleration, deceleration, and cornering. Side slip angle can be viewed as the effect of these inputs and dynamics, representing the vehicle's response to them in a larger context.

[0088] Tire slip angle helps understand how a tire behaves under various cornering and dynamic conditions, which is critical to grip and handling. Side slip angle provides insight into vehicle stability and directional control.

[0089] In summary, while tire slip angle is specific to the direction of the tire relative to its motion, sideslip angle is about the direction of the vehicle relative to its overall path of travel. Each of these angles provides valuable information for analyzing and improving vehicle performance and safety.

[0090] Next, let's discuss steering and wheel angles.

[0091] Driver Steering Angle – This term refers to the angle at which the driver turns the steering wheel. It reflects the driver’s input or command to change the direction of the vehicle. This angle can be measured directly and can be used to understand the driver’s intention for the vehicle’s movement. It is a critical parameter in vehicle dynamics, driver assistance systems, and autonomous vehicle control algorithms as it directly affects the vehicle’s trajectory and handling characteristics.

[0092] Driver steering angle and steering wheel angle (SWA) (discussed below) are virtually identical in that both represent the driver's direct input relative to the vehicle's steering system to achieve the desired direction of travel. This parameter is key to analyzing steering behavior and implementing control strategies in various vehicle applications.

[0093] SWA (Steering Wheel Angle) – This term refers to the angle at which the driver turns the steering wheel. It is a direct measure of the driver’s input used to steer the vehicle. SWA is a measurable quantity that indicates how much the driver intends to steer the vehicle. This angle is critical to understanding the intended direction of motion of the vehicle and is often used in vehicle dynamics research and driver assistance systems to predict or control vehicle behavior.

[0094] WSA (Wheel Steering Angle) – This term refers to the actual angle of the wheels relative to the longitudinal axis of the vehicle. It is the effective angle that results in the direction of movement of the tires during steering. Unlike SWA, WSA is usually not measured directly but is calculated or estimated based on other measurable quantities because it directly affects the trajectory of the vehicle. WSA determines how the vehicle actually moves in response to steering inputs and affects the handling characteristics of the vehicle.

[0095] In dynamic modeling and control systems, since WSA and SWA are usually linearly related, the steering gear ratio (SGR) or other vehicle-specific parameters are often used to convert WSA to SWA (or vice versa). This conversion is necessary because SWA is a measurable parameter that can be directly used in optimization and control algorithms, while WSA may not be directly measurable but is critical to understanding and influencing the vehicle's steering behavior.

[0096] Therefore, Equation 3 for optimization uses SGR to convert δ f,WSA Value converted to delta f,SWA (Because SWA is measurable).

[0097] Equation 3 expresses the optimization model 122 by using a steady-state yaw rate model based on a dynamic model, which is close to the model of Equation 2, but uses (i) the steering gear ratio (SGR) (driver steering angle δ f,SWA and wheel steering angle δ f,WSA ), and using (ii) the driver steering angle δ f,SWA Instead of the wheel steering angle δ f,WSA For example, when the driver turns the steering wheel 450 degrees and the front wheels turn 30 degrees, the SGR may be calculated as 15.

[0098] Equation 4 defines an optimization problem whose solution minimizes the cumulative sum of yaw rate errors relative to the data. Processor 1030 may execute a learning-based optimization algorithm as described in Equation 4, where n represents the number of data to be applied to the optimization model 122, p represents the lower bound of the optimization variable, and represents the upper limit of the optimization variables, which can be calculated based on the SGR or K of general vehicles. us For example, p Can be set to 'K us : 0.0030 <rad / (m / s 2 )>, SGR: 10<dimensionless number>', and Can be set to 'K us : 0.0060 <rad / (m / s 2 )>, SGR: 20<dimensionless number>'.

[0099] Equation 5 describes a genetic algorithm equation for obtaining an optimal solution to the optimization problem of Equation 4. The algorithm for obtaining an optimal solution to Equation 4 is not limited to the described embodiment; the optimal solution may be obtained by using other algorithms such as a particle swarm optimization algorithm or a Levenberg-Marquardt algorithm.

[0100] The steering calibration system 100 may apply the obtained parameters of the optimization model 122 to the yaw rate model 104 and may update the yaw rate model 104. The obtained parameters may be the wheel steering angle δ f,WSA Relative to the lateral acceleration (a y ) changes. Essentially, K us It quantifies how the wheel steering angle (WSA) changes in response to lateral forces acting on the vehicle, which is directly related to how the vehicle handles understeer conditions.

[0101] Understeer refers to a condition in a vehicle's handling characteristics where the vehicle tends to steer less than or "understeer" the amount commanded by the driver. This condition typically occurs when the front wheels do not have enough grip to execute a turn at the speed required, causing the vehicle to continue to travel in a straighter line than intended.

[0102] K us The concept of is crucial to understanding and optimizing vehicle dynamics, especially in the context of designing control systems aimed at improving handling and stability. By adjusting parameters such as the steering gear ratio (SGR) and taking into account the understeer coefficient (K us ), engineers can develop systems that better predict and control the vehicle's behavior in a variety of driving conditions, thereby improving safety and performance.

[0103] In short, K us It is a parameter that reflects the vehicle's steering responsiveness and stability, especially under lateral acceleration conditions, and is used for the optimization of vehicle dynamics models and control systems.

[0104] Figure 2 Example operations of a steering calibration system in accordance with one or more embodiments are shown.

[0105] Although operations 210 to 240 are described as being performed by Fig.10 2 is performed by the electronic device 1000 shown in FIG. 2 , but operations 210 to 240 may be performed by other suitable electronic devices in any suitable system.

[0106] Reference Figure 1A The description provided generally applies to Figure 2 .

[0107] According to one or more embodiments, the processor 1030 may determine whether to automatically execute / activate the calibration optimization system 120 based on the accumulated error between the predicted value of the yaw rate model 104 (associated with vehicle steering) and the measured yaw rate value of the vehicle. Alternatively, the processor 1030 may determine whether to automatically execute / activate the calibration optimization system 120 through an interface of the vehicle (e.g., Figure 3 The vehicle interface 130 of the vehicle receives input from the driver of the vehicle indicating that calibration is to be performed. Figure 3 The vehicle interface 130 is described.

[0108] In operation 210, the processor 1030 may receive data related to the actual steering of the vehicle. The data related to the steering of the vehicle may include the acceleration of the vehicle (e.g., Figure 1A The lateral acceleration a y ), the vehicle's yaw rate γ, and its speed (e.g., Figure 1A The longitudinal velocity v x ), and the driver steering angle δf,SWA For example, these data may be provided by IMU 101 , GPS 102 , and steering angle sensor 103 .

[0109] In operation 220, the processor 1030 may detect / select target data corresponding to a predetermined condition (eg, satisfying the predetermined condition) from the plurality of data. The detected / selected target data may also be referred to as filtered data (eg, Figure 1A and Figure 3 ). The predetermined conditions may include a threshold condition for the lateral acceleration of the vehicle and a threshold condition for the change in the yaw rate of the vehicle. In general, the predetermined conditions may relate to how "difficult" it is for the vehicle to turn or whether skidding or oversteering / understeering may occur. Figure 6 To describe the selection of target data.

[0110] In operation 230, the processor 1030 may obtain parameters of the optimization model 122 that minimize the cumulative error between the predicted value of the yaw rate model 104 of the vehicle and the yaw rate measurement value of the vehicle. The parameters may be obtained by the optimization model 122 based on the target data. The yaw rate model 104 may include at least one of a dynamic model and / or a kinematic model, and may be used to perform calibration corresponding to the steering of the vehicle. That is, the yaw rate change output by the yaw rate model 104 may be used (e.g., by a steering controller).

[0111] Regarding the functional role of the yaw rate change output by the yaw rate model 104, some explanation of yaw rate and yaw rate model 104 is provided next, and then return to Figure 2 Description.

[0112] Yaw rate model functionality.

[0113] Predicting yaw rate change: The yaw rate model 104 predicts the change in the vehicle's yaw rate based on various inputs, including the driver steering angle δ f,SWA , vehicle speed (v x ) and potentially other factors such as vehicle load, road conditions and lateral acceleration (a y ). This prediction helps understand how the vehicle will respond to steering inputs, which is useful for maintaining control and stability during maneuvers.

[0114] Steering Control System Calibration: The yaw rate model 104 is used within the steering calibration system to calibrate the steering control. Calibration involves adjusting the parameters of the steering control system so that the actual vehicle behavior (in terms of yaw rate in response to steering input) matches the desired or predicted behavior as closely as possible. This process may include adjusting software parameters within the steering control algorithm, or in some cases even hardware adjustments.

[0115] Back to Figure 2 , the processor 1030 may obtain parameters of the optimized model 122 by applying learning-based optimization to the yaw rate model 104 of the vehicle (thereby updating the yaw rate model 104 to reduce the difference between the measured yaw rate and the yaw rate predicted by the yaw rate model 104).

[0116] In operation 240, the processor 1030 may update the yaw rate model 104 of the vehicle by using the parameters of the optimization model 122. The processor may update the control model of the processor 1030 of the vehicle based on the updated yaw rate model 104. For example, the processor 1030 may apply the updated yaw rate model 104 to the steering wheel controller of the vehicle. Figure 8 This is described in detail.

[0117] The above “parameters of the optimization model 122” refer to the variables or coefficients within the model that are adjusted or optimized to minimize the difference between the measured yaw rate (γ) received from the sensor (such as the IMU 101) and the predicted yaw rate (γ) output by the yaw rate model 104. SS ). These parameters are critical to the calibration process because they ensure that the vehicle steering response predicted by the yaw rate model closely matches the actual steering response observed through the sensor data.

[0118] Regarding the description that “the processor 1030 can update the yaw rate model 104 of the vehicle by using the parameters of the optimization model 122”, this means that the processor uses the optimization parameters obtained through the calibration optimization process to refine or adjust the yaw rate model. This adjustment helps to improve the accuracy of the model prediction, thereby achieving better steering control and vehicle handling.

[0119] The outputs of the model 122 refer to the steering gear ratio (SGR) and the instantaneous understeer coefficient (K us ) are determined through an optimization process to minimize the error between the predicted and actual yaw rate. These outputs are the “optimization parameters” that directly influence how the yaw rate model predicts the vehicle’s response to steering inputs.

[0120] With respect to the weights of model 122, in the context of an optimization and calibration model such as model 122, the term "weight" refers to the parameters (e.g., SGR and K) that are adjusted. us ) to make the model’s predictions consistent with real-world data.

[0121] In summary, the phrase "optimizing parameters of the model 122" above most directly refers to the optimized values ​​of the variables within the model, which are adjusted to reduce prediction errors. These optimized parameters are then applied to update the yaw rate model 104, thereby enhancing its accuracy and reliability for steering control applications.

[0122] Figure 3 An example of a steering calibration system is shown in accordance with one or more embodiments.

[0123] Reference Figure 1A and Figure 2 The description provided generally applies to Figure 3 .

[0124] According to an embodiment, the processor 1030 may display a signal (e.g., a user interface element) for allowing a person to confirm whether to execute the calibration optimization system 120. In response to a determination that calibration may be required, the signal / user interface element may be displayed on the vehicle user interface (human-machine interface (HMI), such as a touch-based interface) 130. Such a determination may be in response to a vehicle condition, such as a tire change, a vehicle modification, or a change in the road environment. The determination of calibration may also be driven by a signal from the error detector 110 indicating that execution of the calibration optimization system 120 is necessary. In any case, when calibration is required, the calibration may be confirmed by a person (e.g., a driver) providing input confirming execution of the calibration optimization system 120 through the vehicle user interface 130 (e.g., an electronic device, such as a display or audio built into the vehicle). The processor 1030 may receive input confirming execution of the calibration optimization system 120 through the vehicle user interface 130, and may respond by running the calibration optimization system 120.

[0125] The vehicle user interface 130 may be provided by the vehicle itself. For example, the vehicle user interface 130 may be provided by the vehicle's operating system (OS) or may be provided by an application installed in the vehicle. In addition, the vehicle interface 130 may be provided by a server, wherein the vehicle may simply receive and display the vehicle interface 130 provided by the server.

[0126] As described above, calibration may be driven in the following manner: the processor 1030 determines that the cumulative error between the predicted value of the vehicle's yaw rate model 104 and the measured value of the vehicle's yaw rate exceeds a predetermined threshold (e.g., a threshold of 3 degrees per second (deg / s)). In this case, the processor 1030 may determine that it is necessary to execute the calibration optimization system 120, and may notify the driver through the vehicle user interface 130 as described above. However, once a determination is made that calibration is to be executed, the vehicle user interface 130 may display the reason why the calibration optimization system 120 needs to be executed and circumstances such as tire replacement, vehicle modification, or changes in the road environment. On the other hand, when the driver determines that the vehicle environment has changed, the driver may initiate calibration by inputting a signal to the vehicle user interface 130 that executes the calibration optimization system 120. In this case, the processor 1030 may receive an input signal from the vehicle interface 130 and may execute the calibration optimization system 120.

[0127] Figure 4 Example vehicle paths are shown for a steering control system according to one or more embodiments.

[0128] The processor 1030 may set the yaw rate according to a target path 410 (e.g., in a steering wheel controller) of the rotational motion of the vehicle 420 (the target path 410 may be an expected driving path of the vehicle determined by, for example, an autonomous driving system). However, due to changes in the vehicle environment, when the yaw rate is set to the yaw rate provided by the yaw rate model 104 (predicted using current / recent data), the vehicle 420 may travel along a driving path 412 that is significantly different from the target path 410 without calibration. However, when the processor 1030 (i) calibrates the yaw rate model 104 so that the cumulative error between the predicted value of the yaw rate model 104 according to the target path 410 and the measured value of the yaw rate according to the driving path 412 is minimized, and (ii) sets the yaw rate of the vehicle (e.g., of the steering wheel controller) using the yaw rate of the updated yaw rate model 104, the vehicle 420 may travel along an adjusted path 411 that is closer to the target path 410.

[0129] Figure 5 Examples of kinematic models and dynamic models are shown in accordance with one or more embodiments.

[0130] Reference Figures 1A to 4 The description provided generally applies to Figure 5 .

[0131] The kinematic model 510 may be implemented as represented by Equation 6. Under the assumption that the vehicle moves like a rigid body, the kinematic model 510 may model the entire vehicle rotating about a pivot point where the normal to the rear wheels intersects the normal to the front wheels (here, "normal" is a line perpendicular to the direction of the front wheels and a line perpendicular to the direction of the rear wheels, i.e., the direction of the axis of rotation).

[0132] Equation 6

[0133]

[0134] Reference Figure 1A The steering calibration system 100 described may be a dynamic model 520 similar to the kinematic model 510. γ in Equation 6 ss A kinematic model 510 in steady state is shown.

[0135] Equation 7 represents the steady-state optimization model 122 based on the kinematic model 510 .

[0136] Equation 7

[0137]

[0138] According to one or more embodiments, the above reference Figure 1A The kinetic model 520 is described in detail.

[0139] Figure 6 An example of the relationship between steering angle and lateral acceleration for different conditions of an object data detector according to one or more embodiments is shown.

[0140] Reference Figure 1A and Figure 2 The description provided generally applies to Figure 6 .

[0141] According to one or more embodiments, data from, for example, IMU 101, GPS 102, and / or steering angle sensor 103 (control / measurement components) may flow through target data detector 121. Target data detector 121 may filter / detect target data corresponding to a predetermined condition from data from control / measurement components related to the steering of the vehicle. The predetermined condition may include the lateral acceleration a of the vehicle. y The threshold conditions of the lateral acceleration a and the threshold conditions of the change of the yaw rate of the vehicle. y threshold condition.

[0142] Graph 600 shows an understeer curve generated by dynamic model 520. In graph 600, the x-axis represents the lateral acceleration a y , the y-axis represents the wheel steering angle δf,WSA (Notice, Figure 6 The "f,ack" in f,WSA Another term is the y-axis for delta f,WSA ). With respect to graph 600, K us is the lateral acceleration a y Function of wheel steering angle δ f,WSA The instantaneous gradient of K us It is expressed by the above equation 2.

[0143] Before discussing graph 600, it is important to us Some discussion of its properties follows. us is the slope of the graph 600. Therefore, when a y When it is low enough (e.g., less than 0.7 g), this means that K us can be defined as a constant. In the following graph 600 and K us In the description of the behavior in different steering situations, due to K us Relative to the lateral acceleration (a y ) constant gradient, K us In both linear understeer and oversteer cases, the wheel steering angle δ f,WSA Relative to a y The change in (lateral acceleration) can be approximated as a constant value.

[0144] Therefore, this paper focuses on K us with a y The discussion of involves the direct relationship between steering angle change and lateral acceleration in the context of vehicle dynamics modeling. In this case, K us represents the coefficient or parameter describing this relationship under certain conditions (such as understeer and oversteer in linear modeling), a y is in the analysis with K us Variables to be compared or correlated with.

[0145] Back to Figure 6 , in graph 600, when (and because) K us When K has a constant gradient in the following cases, us can be effectively considered as a constant: (i) in the understeer case where the linear tire model is applied (hereinafter, referred to as the linear understeer case 610), and (ii) in the oversteer case where the linear tire model is applied (hereinafter, referred to as the linear oversteer case 630). In other words, in the linear understeer case 610, K us can be considered as a constant, since the wheel steering angle δ f,WSA Relative to the lateral acceleration ay On the other hand, in the following cases K us The shape of the nonlinear curve graph that does not have a constant gradient may be: (i) in an understeering situation (hereinafter, referred to as a nonlinear understeering situation 620) to which a nonlinear tire model is applied and (ii) in an oversteering situation (hereinafter, referred to as a nonlinear oversteering situation 640) to which a nonlinear tire model is applied. In other words, in the nonlinear understeering situation 620, K us can not be considered as a constant, because K us With the lateral acceleration a y increases rather than increasing linearly.

[0146] The nonlinear tire model shows that in the case of rotation (i.e., when the yaw changes), as the lateral acceleration a y The required steering angle increases nonlinearly with the increase of . The linear tire model shows that in the case of rotation, as the lateral acceleration a y As the lateral acceleration a increases, the required steering angle increases linearly. Nonlinear understeer condition 620 is typically the response of most real vehicles (during automatic steering). Additionally, nonlinear oversteer condition 640 is typically the response when the vehicle is sliding sideways, such as in a drift turn (e.g., at high speeds or on slippery surfaces). However, when the lateral acceleration a y When K is low enough, us can be defined as a constant in all four cases (i.e., linear understeer case 610, nonlinear understeer case 620, linear oversteer case 630, and nonlinear oversteer case 640). As shown in graph 600, when the lateral acceleration a y When the lateral acceleration a is less than or equal to a specific value 601, it is assumed that in both the linear understeer situation 610 and the nonlinear understeer situation 620, when the lateral acceleration a y When K is less than a specific value of 601 us Approximately constant increase, K us can be defined as a constant. In addition, when the lateral acceleration a y When it is less than or equal to a certain level of 601, under the assumption that K us In the case of approximately constant decrease in the linear oversteer case 630 and the nonlinear oversteer case 640, K us can be defined as a constant. In an exemplary embodiment, when the lateral acceleration a y When the absolute value of is less than or equal to 0.7 gravitational acceleration (g), K us This technique can be used in the target data detector 121. When the target data detector 121 evaluates / filters the data from (for example) the IMU 101, it can be defined as a constant by converting the lateral acceleration a of the vehicle into yThe threshold is set to 0.7g to detect when the lateral acceleration a y Target data when less than or equal to 0.7 g. The value of 0.7 g is just an example; the specific level 601 may be set to various values ​​based on the amount of data, the performance of the steering calibration system 100, etc.

[0147] The size of needs to be small enough to satisfy the assumptions in Equation 2 above. because is the absolute value of the change in yaw rate, so the target data detector 121 can detect data that satisfies the threshold condition that the change in yaw rate is sufficiently small. The method is to use the Kalman filter of Equations 8 to 10 below and differentiate the measured value of the yaw rate including the noise (sampled at the time step). t is the sampling rate, for example every 0.010 seconds (sec). z(k) is the measurement value at step k. X(k) is the state at step k.

[0148] Equation 8

[0149] x(k+1)=A d x(k),z(k+1)=H d x(k+1)

[0150] Equation 9

[0151] in z(k+1)=γ(k),

[0152] Equation 10

[0153] H d =[1 0 0].

[0154] In summary, the target data detector 121 can detect the driver's steering angle δ based on the data that simultaneously satisfies the following conditions: f,SWA , longitudinal speed v x and yaw rate γ: (i) lateral acceleration a y The absolute value of the lateral acceleration a is less than or equal to y The threshold value (e.g., 0.7 g) and (ii) the absolute value of the change in yaw rate is less than or equal to the threshold value of the change in yaw rate (e.g., 5 deg / s 2 ). You can select data that meets the conditions at the same time as the target data.

[0155] Figure 7 An example of the operation of a dynamic model in accordance with one or more embodiments is shown.

[0156] Reference Figure 1A The description provided generally applies to Figure 7 . In discussion Figure 7 Earlier, some explanations of "motion" and "torque" were provided. Figure 7 In the context of , the term "inward / outward moment" refers to the yaw moment required to resolve understeer and oversteer situations. This represents the torque about the yaw axis required for correction. In contrast, the terms "inward movement" and "outward movement" refer to corrections to the vehicle's direction.

[0157] Reference Figure 7 , the direction of travel of the vehicle 700 may vary according to the yaw rate and the side slip angle. The inward movement 710 may be the direction of travel of the vehicle 700 in an understeering situation, and the outward movement 720 may be the direction of travel of the vehicle 700 in an oversteering situation. In addition, the side slip angle may be calculated along the advancing direction 730 of the vehicle 700 and the moving direction 740 of the vehicle 700 of Equation 1.

[0158] Figure 8 An example of a controller of a vehicle according to one or more embodiments is shown. The vehicle can operate in an autonomous driving mode, a driver assistance mode, etc. That is, the controller can partially or fully control the operation of the vehicle.

[0159] Reference Figures 1A to 7 The description provided generally applies to Figure 8 .

[0160] According to one or more embodiments, the controller 800 of the vehicle may include one or more processors 1030 .

[0161] The vehicle's controller 800 may use information received from the planner, positioning unit, and perception unit as input. However, the planner, positioning unit, and perception unit may also be included in the autonomous vehicle's controller 800. The controller 800 may control the vehicle's steering (i.e., may be / include a steering controller).

[0162] The controller 800 of the vehicle may include, for example, one or a combination of two or more of hardware, software, and firmware. The controller 800 may be used interchangeably with terms such as unit, logic, logic block, component, or circuit. The controller 800 may be the smallest unit of an integrally formed component or a part thereof. The controller 800 may be the smallest unit for performing one or more functions or a part thereof. For example, the controller 800 may include any one or any combination of an application specific integrated circuit (ASIC) chip, a field programmable gate array (FPGA), or a programmable logic device that performs known operations or operations to be developed.

[0163] The controller 800 of the autonomous vehicle may receive a target path, a target speed, and / or a target yaw rate from the planner, may receive information such as the current position of the vehicle (x, y position) or the attitude of the vehicle (heading angle), and may receive information about the position and size of surrounding objects from the perception unit. For example, the vehicle may be operated in an autonomous mode or a driver assistance mode.

[0164] The controller 800 of the vehicle may determine command values ​​of the deceleration / acceleration actuator and the steering actuator to follow the target speed and the target yaw rate by using the received information.

[0165] If the yaw rate model 104 applied to the steering wheel controller 810 is inaccurate, an error may result when generating a steering angle corresponding to a target yaw rate (resulting in a difference between the planned driving path and the actual driving path). Therefore, the controller 800 of the autonomous vehicle may use the yaw rate model 104 updated by executing the calibration optimization system 120 (according to the updates received from the steering calibration system 100), and may calculate the steering angle to generate a target yaw rate that brings the actual driving path closer to the planned driving path.

[0166] Fig. 9 An example of operation of a vehicle according to one or more embodiments is shown.

[0167] Reference Figures 1A to 8 The description provided generally applies to Fig. 9 For ease of description, operations 910 to 950 are described as being performed by the controller 800. However, operations 910 to 950 may be performed by another suitable electronic device in any suitable system.

[0168] In operation 910 , the vehicle may set a target yaw rate for rotationally driving the ego vehicle.

[0169] In operation 920 , the vehicle may control the steering angle of the vehicle to follow the target yaw rate by using the yaw rate model 104 of the ego vehicle.

[0170] In operation 930, the autonomous vehicle may determine the range of the autonomous vehicle (eg, K us can be considered as a constant range, i.e. a y <0.7g) to obtain data related to the steering of the autonomous vehicle.

[0171] In operation 940 , the autonomous vehicle may determine whether the yaw rate model 104 needs to be updated based on, for example, the accumulated error between the predicted yaw rate value predicted by the yaw rate model 104 and the measured yaw rate value.

[0172] In operation 950 , the autonomous vehicle may update the yaw rate model 104 based on the data (eg, steering angle, speed, and yaw rate), and this operation may be performed according to the determination result of operation 940 .

[0173] Fig.10 An example of an electronic device according to one or more embodiments is shown. Fig.10 , the electronic device 1000 may include a processor 1030 , a memory 1050 , and an output device 1070 (eg, a display). The processor 1030 , the memory 1050 , and the output device 1070 may be connected to each other via a communication bus 1005 .

[0174] The output device 1070 may display a user interface configured to enable a user to control whether to execute the calibration optimization system provided by the processor 1030 .

[0175] The memory 1050 may store the yaw rate model 104 updated by the processor 1030. In addition, the memory 1050 may store various information generated in the processing of the above-mentioned processor 1030. In addition, the memory 1050 may store various data, programs, etc. The memory 1050 may include a volatile memory and / or a non-volatile memory (but not the signal itself). The memory 1050 may include a storage medium such as a hard disk, and store various data.

[0176] In addition, the processor 1030 may execute reference Figures 1A to 9 At least one method described or an algorithm corresponding to the at least one method. The processor 1030 may be a data processing device implemented by hardware including a circuit having a physical structure for performing the desired operation. For example, the desired operation may include a code or instruction in a program. The processor 1030 may be implemented as, for example, a central processing unit (CPU), a graphics processing unit (GPU), or a neural network processing unit (NPU). The hardware-implemented electronic device 1000 may include, for example, a microprocessor, a central processing unit (CPU), a processor core, a multi-core processor, a multiprocessor, an application-specific integrated circuit (ASIC), and a field-programmable gate array (FPGA).

[0177] The processor 1030 may execute a program and control the electronic device 1000. Codes of the program executed by the processor 1030 may be stored in the memory 1050.

[0178] Computing device, vehicle, electronic device, processor, memory, image sensor, vehicle / operating function hardware, autonomous / assisted driving system, display, information output system and hardware, storage device, and the like Figures 1A to 10Other equipment, devices, units, modules and components described are implemented by hardware components or represent hardware components. Examples of hardware components that can be used to perform the operations described in this application include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application where appropriate. In other examples, one or more hardware components for performing the operations described in this application are implemented by computing hardware (e.g., by one or more processors or computers). Processors or computers can be implemented by one or more processing elements (e.g., logic gate arrays, controllers and arithmetic logic units, digital signal processors, microcomputers, programmable logic controllers, field programmable gate arrays, programmable logic arrays, microprocessors, or any other equipment or combination of equipment configured to respond and execute instructions in a defined manner to achieve the desired result). In one example, a processor or computer includes (or is connected to) one or more memories storing instructions or software executed by a processor or computer. The hardware components implemented by a processor or a computer can execute instructions or software, for example, an operating system (OS) and one or more software applications running on the OS to perform the operations described in this application. The hardware components can also access, manipulate, process, create and store data in response to the execution of instructions or software. For the sake of brevity, the singular term "processor" or "computer" can be used in the description of the examples described in this application, but multiple processors or computers can be used in other examples, or the processor or computer can include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components can be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components can be implemented by one or more processors, or a processor and a controller, and one or more other hardware components can be implemented by one or more other processors or another processor and another controller. One or more processors or a processor and a controller can implement a single hardware component, or two or more hardware components. The hardware components may have any one or more of different processing configurations, examples of which include a single processor, independent processors, parallel processors, single instruction single data (SISD) multiprocessing, single instruction multiple data (SIMD) multiprocessing, multiple instruction single data (MISD) multiprocessing, and multiple instruction multiple data (MIMD) multiprocessing.

[0179] Perform the operations described in this application Figures 1A to 10The method shown in is performed by computing hardware, for example, by one or more processors or computers implemented as described above, executing instructions or software to perform the operations described in the present application (operations performed by the method). For example, a single operation or two or more operations can be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations can be performed by one or more processors or a processor and a controller, and one or more other operations can be performed by one or more other processors or another processor and another controller. One or more processors or a processor and a controller can perform a single operation or two or more operations.

[0180] The instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement hardware components and perform the methods described above can be written as computer programs, code segments, instructions, or any combination thereof, for individually or collectively indicating or configuring one or more processors or computers to operate as machines or special-purpose computers to perform the operations performed by the above-mentioned hardware components and methods. In one example, the instructions or software include machine codes directly executed by one or more processors or computers, such as machine codes generated by a compiler. In another example, the instructions or software include more advanced codes executed by one or more processors or computers using an interpreter. Instructions or software can be written in any programming language based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding descriptions of this article (which disclose algorithms for performing operations performed by hardware components and methods described above).

[0181] Instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement hardware components and perform the methods described above, as well as any associated data, data files, and data structures, may be recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media. Examples of non-transitory computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage device, hard disk drive (HDD), solid state drive (SSD), flash memory, card-type memory (such as, multimedia card or micro card (for example, secure digital (SD) or extreme digital (XD))), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, and any other device configured as follows: store instructions or software and any associated data, data files and data structures in a non-temporary manner, and provide instructions or software and any associated data, data files and data structures to one or more processors or computers so that one or more processors or computers can execute the instructions. In one example, the instructions or software and any associated data, data files and data structures are distributed on a networked computer system so that one or more processors or computers store, access and execute the instructions and software and any associated data, data files and data structures in a distributed manner.

[0182] Although the present disclosure includes specific examples, it will be apparent after understanding the disclosure of the present application that various changes in form and detail may be made to these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein should be considered to be descriptive only and not for limiting purposes. The description of features or aspects in each example is considered to be applicable to similar features or aspects in other examples. Suitable results may be achieved if the described techniques are performed in a different order and / or if components in the described systems, architectures, devices, or circuits are combined in different ways and / or replaced or supplemented by other components or their equivalents.

[0183] Therefore, the scope of the disclosure may be defined by the claims and their equivalents in addition to the above disclosure, and all variations within the scope of the claims and their equivalents should be construed as being included in the disclosure.

Claims

1. A method for controlling a vehicle, the method comprising: receiving a plurality of data related to steering of the vehicle; detecting target data determined to satisfy a predetermined condition among the plurality of data; Based on the target data and the optimization model, obtaining optimization model parameters, the optimization model parameters minimizing a cumulative error between a predicted yaw rate of a yaw rate model of the vehicle and a measured yaw rate of the vehicle; as well as The yaw rate model of the vehicle is updated by using the optimized model parameters.

2. The method according to claim 1, wherein: Obtaining optimized model parameters includes applying a learning-based optimization to a yaw rate model of the vehicle to determine the optimized model parameters.

3. The method according to claim 1, further comprising: A determination is made whether to perform a calibration optimization system, the determination being based on the accumulated error between a predicted yaw rate of the vehicle and a measured yaw rate value of the vehicle.

4. The method according to claim 3, wherein: Determining whether to execute the calibration optimization system is based on user input indicating that the calibration optimization system is to be executed, the user input being received via a user interface of the vehicle.

5. The method according to claim 1, further comprising: A control model of the vehicle is updated based on the updated yaw rate model of the vehicle.

6. The method according to claim 5, wherein: Updating the control model of the vehicle includes applying the updated yaw rate model to a steering wheel controller of the vehicle.

7. The method according to claim 1, wherein: The predetermined condition includes a lateral acceleration threshold of the vehicle and a yaw rate change threshold of the vehicle.

8. The method according to claim 1, wherein: The plurality of data related to the steering of the vehicle include a lateral acceleration of the vehicle, a yaw rate of the vehicle, a speed of the vehicle, and a steering angle.

9. The method according to claim 1, wherein: The yaw rate model includes a dynamic model or a kinematic model, either of which is used to perform calibration corresponding to the turning of the vehicle.

10. The method according to claim 9, wherein: The calibration increases the accuracy of a predicted yaw rate used to control steering of the vehicle.

11. A device for controlling a vehicle, the device comprising: One or more processors configured to: receiving a plurality of data related to steering of the vehicle; detecting target data determined to satisfy a predetermined condition among the plurality of data; Based on the target data and the optimization model, obtaining optimization model parameters, the optimization model parameters minimizing a cumulative error between a predicted yaw rate of a yaw rate model of the vehicle and a measured yaw rate of the vehicle; as well as The yaw rate model of the vehicle is updated by using the optimized model parameters.

12. The device according to claim 11, wherein The one or more processors are further configured to apply a learning-based optimization to a yaw rate model of the vehicle to determine the optimized model parameters.

13. The device according to claim 11, wherein: The one or more processors are configured to execute an error detector configured to determine whether to execute a calibration optimization system based on a cumulative error between a predicted yaw rate of the vehicle and a measured yaw rate value of the vehicle.

14. The device according to claim 13, wherein: The one or more processors are further configured to receive, via a user interface of the vehicle, a user input indicating that the calibration optimization system is to be executed.

15. The device according to claim 11, wherein The one or more processors are further configured to update a control model of the vehicle based on the updated yaw rate model of the vehicle.

16. The device according to claim 15, wherein: The control model includes a steering wheel controller for the vehicle.

17. The device according to claim 11, wherein: The predetermined condition includes a lateral acceleration threshold of the vehicle and a yaw rate change threshold of the vehicle.

18. The device according to claim 11, wherein The plurality of data related to the steering of the vehicle include a lateral acceleration of the vehicle, a yaw rate of the vehicle, a speed of the vehicle, and a driver steering angle.

19. The device according to claim 11, wherein: The yaw rate model includes a dynamic model or a kinematic model, either of which is used to perform calibration corresponding to the turning of the vehicle.

20. An electronic device for a vehicle, the electronic device comprising: one or more processors; A memory storing instructions, the instructions being configured to, when executed by the one or more processors: setting a target yaw rate of the vehicle for rotationally driving the vehicle, controlling the yaw rate of the vehicle to achieve the target yaw rate by using a yaw rate model of the vehicle, acquiring data related to autonomous steering of the vehicle based on the data related to vehicle steering satisfying a predetermined condition, determining whether to update the yaw rate model, wherein the determination is based on a cumulative error between a predicted yaw rate of the yaw rate model and a measured yaw rate of the vehicle, and based on the determination, updating the yaw rate model according to the data; a display configured to display a user interface according to the instructions; The instructions are further configured to cause the one or more processors to: receiving a plurality of data related to the steering of the vehicle, obtaining an optimized model parameter based on determining that the target data among the plurality of data satisfies a predetermined condition, the optimized model parameter minimizing a cumulative error between the predicted yaw rate and the measured yaw rate, and The yaw rate model of the vehicle is updated by using the optimized model parameters.

21. The electronic device according to claim 20, wherein: The steering related data includes an understeer gradient of the vehicle.

Images