Vehicle control strategy calibration method and system, electronic equipment and storage medium

Abstract

The invention discloses a vehicle control strategy calibration method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining vehicle dynamics parameters, and constructing a preset driving simulation vehicle dynamics model; coupling the preset control strategy model with a preset driving simulation vehicle dynamics model, and constructing a target driving simulation vehicle dynamics model; acquiring a user driving scene; according to the user driving scene, flexible control parameter calibration is carried out through the target driving simulation vehicle dynamics model, and a preset control parameter change rule table is obtained; according to the user driving scene, vehicle control strategy parameter calibration is carried out through the target driving simulation vehicle dynamics model, and vehicle control calibration data are obtained; and determining target vehicle control strategy data according to a preset control parameter change rule table and the vehicle control calibration data. According to the embodiment of the invention, the development accuracy and efficiency of the vehicle control strategy can be improved, and the vehicle driving stability is improved. The method can be widely applied to the technical field of vehicle control.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle control strategy calibration method, system, electronic device and storage medium. Background Technology

[0002] With the development of driving simulators, millimeter-level motion accuracy and millisecond-level response speeds have been achieved. Combined with a six-degree-of-freedom motion platform, high-resolution vision system, and real-time physics engine, they can highly reproduce dynamic feedback such as acceleration, steering, and bumps in real driving scenarios. Some high-end devices even support haptic feedback technology to simulate tactile experiences such as steering wheel vibration and seat pressure changes. Simultaneously, by integrating weather simulation systems, road texture projection, and traffic flow simulation algorithms, complex environments such as heavy rain, heavy snow, nighttime, and tunnels can be generated, and the behavior of traffic participants can be dynamically generated, achieving all-weather, all-road-condition training conditions. However, among these technologies, the accuracy of vehicle control strategy development is relatively low, vehicle driving stability is low, and the development cycle of vehicle control strategies is often long.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a vehicle control strategy calibration method, system, electronic device, and storage medium, which can effectively improve the accuracy and efficiency of vehicle control strategy development, thereby effectively improving vehicle driving stability.

[0005] To achieve the above objectives, one aspect of this application proposes a vehicle control strategy calibration method, the method comprising the following steps: Obtain vehicle dynamics parameters to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters; The preset control strategy model is coupled with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model; Obtain the user's driving scenario; Based on the user driving scenario, the flexible control parameters are calibrated using the target driving simulation vehicle dynamics model to obtain a preset control parameter variation rule table. Based on the user driving scenario, the vehicle control strategy parameters are calibrated using the target driving simulation vehicle dynamics model to obtain vehicle control calibration data. The target vehicle control strategy data is determined based on the preset control parameter change rule table and the vehicle control calibration data.

[0006] In some embodiments, obtaining vehicle dynamics parameters to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters includes: Obtain the vehicle dynamics parameters; wherein, the vehicle dynamics parameters include the coordinates of the vehicle's center of gravity, the vehicle's moment of inertia, hard point data, bushing stiffness, and steering assist characteristic curve data; The preset driving simulation vehicle dynamics model is constructed based on the vehicle dynamics parameters and the preset vehicle dynamics multibody model.

[0007] In some embodiments, coupling the preset control strategy model with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model includes: The preset control strategy model is constructed based on the vehicle stability control algorithm; The preset control strategy model is coupled with the preset driving simulation vehicle dynamics model to obtain the target driving simulation vehicle dynamics model.

[0008] In some embodiments, the step of calibrating flexible control parameters based on the user driving scenario using the target driving simulation vehicle dynamics model to obtain a preset control parameter variation rule table includes: According to the user driving scenario, the preset stability state quantity difference data is calibrated through the target driving simulation vehicle dynamics model, and then a flexible PID change rule table is constructed based on the preset stability state quantity difference data; wherein, the preset stability state quantity difference data includes the difference between the vehicle stability state quantity under lateral disturbance and under no lateral disturbance, and the vehicle stability state quantity includes the vehicle yaw rate and the center of gravity sideslip angle.

[0009] In some embodiments, the step of calibrating vehicle control strategy parameters based on the user driving scenario using the target driving simulation vehicle dynamics model to obtain vehicle control calibration data includes: The target driving simulation vehicle dynamics model is used to perform trigger calibration in the user driving scenario to obtain policy trigger threshold data. The influence factor ratio of the control strategy control parameter object under the user driving scenario is calibrated by the target driving simulation vehicle dynamics model to obtain preset influence factor ratio data. The target steering angle scale factor is obtained by calibrating the wheel angle scale factor corresponding to each vehicle driving mode using the target driving simulation vehicle dynamics model.

[0010] In some embodiments, the step of obtaining policy trigger threshold data by performing trigger calibration in the user driving scenario using the target driving simulation vehicle dynamics model includes: Based on the user's driving scenario, the vehicle speed at which the strategy is activated is calibrated using the target driving simulation vehicle dynamics model to obtain preset calibrated vehicle speed data. Based on the user driving scenario, the vehicle stability state threshold is calibrated using the target driving simulation vehicle dynamics model to obtain preset stability state threshold data; wherein, the preset stability state threshold data includes thresholds corresponding to the vehicle yaw rate difference and the center of gravity sideslip angle difference.

[0011] In some embodiments, the step of calibrating the proportion of influencing factors of the control strategy control parameters in the user driving scenario using the target driving simulation vehicle dynamics model to obtain preset influencing factor proportion data includes: The proportion of influencing factors on the yaw rate difference is determined by the target driving simulation vehicle dynamics model to obtain the first proportion data. The influencing factors of the center of gravity sideslip angle difference are calibrated using the target driving simulation vehicle dynamics model to obtain the second proportion data. The preset influencing factor percentage data is determined based on the first percentage data and the second percentage data.

[0012] To achieve the above objectives, another aspect of this application proposes a vehicle control strategy calibration system, the system comprising: The first module is used to acquire vehicle dynamics parameters in order to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters; The second module is used to couple the preset control strategy model with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model. The third module is used to obtain the user's driving scenario; The fourth module is used to calibrate the flexible control parameters based on the target driving simulation vehicle dynamics model according to the user driving scenario, and obtain a preset control parameter change rule table. The fifth module is used to calibrate vehicle control strategy parameters based on the user driving scenario using the target driving simulation vehicle dynamics model, and obtain vehicle control calibration data. The sixth module is used to determine the target vehicle control strategy data based on the preset control parameter change rule table and the vehicle control calibration data.

[0013] To achieve the above objectives, another aspect of this application provides an electronic device, the electronic device comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a vehicle control strategy calibration method, system, electronic device, and storage medium. This scheme acquires vehicle dynamics parameters, constructs a preset driving simulation vehicle dynamics model based on these parameters, and then couples a preset control strategy model with the preset driving simulation vehicle dynamics model to construct a target driving simulation vehicle dynamics model. Next, the embodiments of this invention calibrate flexible control parameters using the target driving simulation vehicle dynamics model based on the acquired user driving scenario, obtaining a preset control parameter change rule table. Simultaneously, the embodiments of this invention calibrate vehicle control strategy parameters using the target driving simulation vehicle dynamics model based on the user driving scenario, obtaining vehicle control calibration data. Therefore, the target vehicle control strategy data is determined based on the preset control parameter change rule table and the vehicle control calibration data, realizing the calibration and development of the vehicle control strategy. Accordingly, by constructing a target driving simulation vehicle dynamics model, the embodiments of this invention can jointly develop and calibrate the vehicle driving simulator and control strategy, improving the accuracy and efficiency of vehicle control strategy development, thereby effectively improving vehicle driving stability. Attached Figure Description

[0016] Figure 1 This is a flowchart of the vehicle control strategy calibration method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the calibration process of the vehicle stability control strategy provided in this embodiment of the invention under a dynamic driving simulator; Figure 3 This is a block diagram illustrating the principle of a vehicle stability compensation strategy based on a dynamic driving simulator for calibrating active front wheel steering, as provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the vehicle control strategy calibration system provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0022] Vehicle Stability Control (VSC) is an active safety system that monitors the vehicle's status in real time and actively intervenes to prevent the vehicle from skidding, losing control, or overturning under emergency driving conditions such as sharp turns, emergency braking, or slippery surfaces. Accordingly, the goal of VSC is to ensure that the vehicle travels stably as intended by the driver, thereby improving handling and safety.

[0023] Proportional-Integral-Derivative (PID) control algorithm: This is a control algorithm used in automation and process control. It calculates the error between the system setpoint (target value) and the actual output value, and then linearly combines the three components of the error—proportional (P), integral (I), and derivative (D)—to form the control quantity, thereby precisely regulating the controlled object.

[0024] With the development of driving simulators, millimeter-level motion accuracy and millisecond-level response speeds have been achieved. Combined with a six-degree-of-freedom motion platform, high-resolution vision system, and real-time physics engine, they can highly reproduce dynamic feedback such as acceleration, steering, and bumps in real driving scenarios. Some high-end devices even support haptic feedback technology to simulate tactile experiences such as steering wheel vibration and seat pressure changes. Simultaneously, by integrating weather simulation systems, road texture projection, and traffic flow simulation algorithms, complex environments such as heavy rain, heavy snow, nighttime, and tunnels can be generated, and the behavior of traffic participants can be dynamically generated, achieving all-weather, all-road-condition training conditions. However, among these technologies, the accuracy of vehicle control strategy development is relatively low, vehicle driving stability is low, and the development cycle of vehicle control strategies is often long.

[0025] In view of this, this application provides a vehicle control strategy calibration method, system, electronic device, and storage medium. This scheme acquires vehicle dynamics parameters, constructs a preset driving simulation vehicle dynamics model based on these parameters, and then couples the preset control strategy model with the preset driving simulation vehicle dynamics model to construct a target driving simulation vehicle dynamics model. Next, based on the acquired user driving scenario, this embodiment calibrates flexible control parameters using the target driving simulation vehicle dynamics model to obtain a preset control parameter change rule table. Simultaneously, based on the user driving scenario, it calibrates vehicle control strategy parameters using the target driving simulation vehicle dynamics model to obtain vehicle control calibration data. Therefore, based on the preset control parameter change rule table and the vehicle control calibration data, the target vehicle control strategy data is determined, realizing the calibration and development of the vehicle control strategy. This effectively improves the accuracy and efficiency of vehicle control strategy development, thereby effectively improving vehicle driving stability.

[0026] The vehicle control strategy calibration method provided in this application relates to the field of vehicle control technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the vehicle control strategy calibration method, but is not limited to the above forms.

[0027] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0028] Figure 1 This is an optional flowchart of the vehicle control strategy calibration method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S110 to S160.

[0029] Step S110: Obtain vehicle dynamics parameters to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters.

[0030] Step S120: Couple the preset control strategy model with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model.

[0031] Step S130: Obtain the user's driving scenario.

[0032] Step S140: Based on the user's driving scenario, calibrate the flexible control parameters using the target driving simulation vehicle dynamics model to obtain a preset control parameter change rule table.

[0033] Step S150: Based on the user's driving scenario, calibrate the vehicle control strategy parameters using the target driving simulation vehicle dynamics model to obtain vehicle control calibration data.

[0034] Step S160: Determine the target vehicle control strategy data based on the preset control parameter change rule table and vehicle control calibration data.

[0035] In this specific embodiment, the present invention first obtains vehicle dynamics parameters to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters. Then, a preset control strategy model is coupled with the preset driving simulation vehicle dynamics model to construct a target driving simulation vehicle dynamics model. Specifically, in this embodiment, vehicle dynamics parameters refer to the parameter data corresponding to the vehicle dynamics model, such as mass parameters, tire parameters, and suspension parameters. Accordingly, this embodiment establishes a high-precision dynamic driving simulator vehicle dynamics model, i.e., the preset driving simulation vehicle dynamics model, using the vehicle dynamics parameters. Simultaneously, in this embodiment, the preset control strategy model refers to the control model constructed based on the control strategy to be calibrated. Accordingly, this embodiment constructs the target driving vehicle dynamics model by coupling and embedding the control strategy algorithm model to be calibrated (the preset control strategy model) into the preset driving simulation dynamics model. Further, this embodiment obtains the user's driving scenario and performs flexible control parameter calibration using the target driving simulation vehicle dynamics model based on the user's driving scenario to obtain a preset control parameter change rule table. Simultaneously, it calibrates the vehicle control strategy parameters using the target driving simulation vehicle dynamics model based on the user's driving scenario to obtain the corresponding preset control parameter change rule table and vehicle control calibration data. Specifically, in this embodiment of the invention, the user driving scenario refers to the user scenario data required for vehicle control strategy calibration, such as scenarios required for vehicle stability control model calibration, such as high-speed lane changing, slippery road surfaces, and emergency avoidance. Correspondingly, the preset control parameter change rule table in this embodiment refers to the optimal control parameter change rule table under different vehicle motion parameters. For example, when a flexible PID control algorithm is used as the vehicle control strategy, the preset control parameter change rule table can be a flexible PID controller parameter change rule table. Furthermore, in this embodiment of the invention, the vehicle control calibration data refers to the vehicle motion control data obtained through calibration, such as vehicle speed and wheel angle parameters. Accordingly, after constructing the target driving simulation vehicle dynamics model, this embodiment of the invention performs flexible control parameter calibration based on the determined user driving scenario. For example, relevant test personnel perform simulation according to the corresponding user driving scenario and modify the calibration based on the feedback data from the target driving simulation vehicle dynamics model, thereby constructing the preset control parameter change rule table. Simultaneously, this embodiment of the invention performs scenario simulation according to the user driving scenario required for calibration and calibrates the parameters of the vehicle control strategy based on the feedback data from the target driving simulation vehicle dynamics model to obtain vehicle control calibration data. Finally, by combining the preset control parameter change rule table and vehicle control calibration data, the present invention obtains the target vehicle control strategy data, realizing the in-loop development of people, vehicles, scenarios, and control strategies, effectively improving the accuracy and efficiency of vehicle control strategy development, and thus effectively improving vehicle driving stability.

[0036] In some embodiments of the present invention, vehicle dynamics parameters are obtained to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters, including but not limited to the following steps: Obtain vehicle dynamics parameters. These parameters include the vehicle's center of gravity coordinates, vehicle moment of inertia, hard point data, bushing stiffness, and steering assist characteristic curve data.

[0037] A preset driving simulation vehicle dynamics model is constructed based on vehicle dynamics parameters and a preset multibody vehicle dynamics model.

[0038] In this specific embodiment, the present invention constructs a preset driving simulation vehicle dynamics model by combining the acquired vehicle dynamics parameters with a preset vehicle dynamics multibody model. Specifically, the vehicle dynamics parameters acquired in this embodiment are the parameter data required to establish the vehicle dynamics multibody model, including the vehicle's center of mass coordinates, vehicle moment of inertia, hard point data, bushing stiffness, and steering assist characteristic curve data. The vehicle's center of mass coordinates refer to the position of the vehicle's center of mass in the vehicle coordinate system. The vehicle's moment of inertia refers to the moment of inertia of the vehicle rotating around each coordinate axis, which affects the vehicle's rotational motion and stability. Additionally, hard point data refers to the coordinates of the mounting points of components such as the vehicle's suspension and steering system, which can define the spatial position and relative motion relationship of the components. Bushing stiffness refers to the stiffness characteristics of bushings (such as rubber or elastic elements). The steering assist characteristic curve data refers to the curve showing the relationship between the output torque of the steering assist motor and the steering wheel angle or vehicle speed, which affects the driver's steering feel and the vehicle's handling. Accordingly, in this embodiment of the invention, a preset vehicle dynamics multibody model is constructed based on the acquired data such as the vehicle's center of gravity coordinates, vehicle rotational inertia, hard point data, bushing stiffness, and steering assist characteristic curve data. Then, by transforming the preset vehicle dynamics multibody model, a preset driving simulation vehicle dynamics model is obtained.

[0039] In some embodiments of the present invention, a preset control strategy model is coupled with a preset driving simulation vehicle dynamics model to construct a target driving simulation vehicle dynamics model, including but not limited to the following steps: A preset control strategy model is constructed based on the vehicle stability control algorithm.

[0040] The preset control strategy model is coupled with the preset driving simulation vehicle dynamics model to obtain the target driving simulation vehicle dynamics model.

[0041] In this specific embodiment, the present invention first constructs a preset control strategy model using a vehicle stability control algorithm, and then couples the preset control strategy model with a preset driving simulation vehicle dynamics model to construct a target driving simulation vehicle dynamics model. Specifically, in this embodiment, the vehicle stability control algorithm refers to the wheel-active steering vehicle stability control strategy algorithm to be calibrated. Accordingly, the present invention constructs a corresponding preset control strategy model based on the vehicle stability control algorithm, namely, the wheel-active steering vehicle stability control strategy model. Then, the present invention integrates the preset control strategy model into the preset driving simulation vehicle dynamics model to embed the vehicle stability control strategy to be calibrated into the preset driving simulation vehicle dynamics model, thereby obtaining the target driving simulation vehicle dynamics model. In this embodiment, the target driving simulation vehicle dynamics model integrates a vehicle model (vehicle dynamics multibody model) and corresponding control strategies, such as uncalibrated vehicle stability control logic.

[0042] In some embodiments of the present invention, flexible control parameters are calibrated using a target driving simulation vehicle dynamics model based on the user's driving scenario to obtain a preset control parameter variation rule table, including but not limited to the following steps: Based on the user's driving scenario, the preset stability state quantity difference data is calibrated using a target driving simulation vehicle dynamics model. Then, a flexible PID change rule table is constructed based on this preset stability state quantity difference data. The preset stability state quantity difference data includes the difference between vehicle stability state quantities under lateral disturbance and without lateral disturbance conditions. Vehicle stability state quantities include vehicle yaw rate and sideslip angle.

[0043] In this specific embodiment, the present invention uses a target driving simulation vehicle dynamics model to calibrate preset stability state quantity difference data under corresponding user driving scenarios, thereby obtaining corresponding preset stability state quantity difference data, and constructing a flexible PID change rule table based on the preset stability state difference data. Specifically, the preset stability difference data in the present invention refers to the difference between vehicle stability state quantities under different states. For example, the preset stability state quantity difference data in the present invention includes the difference between the vehicle stability state quantity under lateral disturbance and the vehicle stability state quantity without lateral disturbance. In addition, the vehicle stability state quantities in the present invention include the vehicle yaw rate and the center of gravity sideslip angle. Accordingly, the present invention uses a target driving simulation vehicle dynamics model to calibrate the difference between the actual vehicle yaw rate and center of gravity sideslip angle under lateral disturbance and the theoretical vehicle yaw rate and the executed sideslip angle without lateral disturbance under user driving scenarios, thereby establishing a flexible PID controller parameter change rule table, i.e., a flexible PID change rule table. Exemplarily, the control strategy in the present invention adopts a flexible PID control method, which sets... To account for the errors in yaw rate and sideslip angle between the actual vehicle model and the theoretical vehicle model, and simultaneously set... For large error range, , as well as There are three small error intervals, and the controller parameters can be adjusted in real time according to the error magnitude. Accordingly, the parameter change rule table of the flexible PID controller constructed in this embodiment is shown in Table 1 below, where... These are the initial values ​​for the PID controller. To adjust the parameters, and .

[0044] Table 1

[0045] In some embodiments of the present invention, vehicle control strategy parameters are calibrated based on the user driving scenario using a target driving simulation vehicle dynamics model to obtain vehicle control calibration data, including but not limited to the following steps: The triggering threshold data of the strategy is obtained by using the vehicle dynamics model of the target driving simulation in the user driving scenario to perform trigger calibration.

[0046] By using the vehicle dynamics model of the target driving simulation, the proportion of influencing factors of the control strategy control parameters in the user driving scenario is calibrated to obtain the preset proportion data of influencing factors.

[0047] The target steering angle scale factor is obtained by calibrating the wheel angle scale factor corresponding to each vehicle driving mode through the target driving simulation vehicle dynamics model.

[0048] In this specific embodiment, the present invention first performs trigger calibration based on the target driving simulation vehicle dynamics model according to the user driving scenario to obtain strategy trigger threshold data. Specifically, the strategy trigger threshold data in this embodiment refers to the threshold data for the activation of the control strategy under the corresponding user driving scenario. By calibrating the trigger data of the control strategy based on the target driving simulation vehicle dynamics model under the user driving scenario, the present invention determines the timing of control strategy access, thereby alleviating the problem of poor control effect caused by unsuitable strategy activation timing, such as the control strategy starting too early or too late. At the same time, the present invention calibrates the proportion of influencing factors of the control strategy control parameters under the user driving scenario through the target driving simulation vehicle dynamics model to obtain preset influencing factor proportion data. Specifically, the preset influencing factor proportion data in this embodiment refers to the influence weight of each factor on the control parameters of the control strategy obtained by calibration. By calibrating the preset influencing factor proportion data, the dominant factors of vehicle instability can be quantified. For example, the influencing factors in this embodiment can be steering wheel angle rate, road surface adhesion coefficient, or vehicle load state, etc. For example, if the calibrated steering wheel angle rate is higher than a preset percentage threshold, it indicates that the driver's emergency steering is the main reason affecting the control parameters of the control strategy under the corresponding operating condition, thus providing a basis for resource allocation in the control strategy. Further, this embodiment of the invention calibrates the wheel angle scaling factor corresponding to each vehicle driving mode using a target driving simulation vehicle dynamics model to obtain the target angle scaling factor. Specifically, this embodiment of the invention calibrates the wheel angle scaling factor under different driving modes based on the target driving simulation vehicle dynamics model to obtain the target angle scaling factor. This improves the accuracy of the theoretical yaw rate calculation, ensuring that the theoretical yaw rate can accurately reflect the actual driving intention and achieve different compensation effects for different driving modes.

[0049] In some embodiments of the present invention, trigger calibration is performed in a user driving scenario using a target driving simulation vehicle dynamics model to obtain policy trigger threshold data, including but not limited to the following steps: Based on the user's driving scenario, the vehicle speed at which the strategy is activated is calibrated using a target driving simulation vehicle dynamics model to obtain preset calibrated vehicle speed data.

[0050] Based on the user's driving scenario, the vehicle stability state threshold is calibrated using a target driving simulation vehicle dynamics model to obtain preset stability state threshold data. This preset stability state threshold data includes thresholds corresponding to the vehicle yaw rate difference and the center-of-gravity sideslip angle difference.

[0051] In this specific embodiment, the present invention first calibrates the vehicle speed at which the strategy is activated based on the user's driving scenario. Specifically, the present invention uses a target driving simulation vehicle dynamics model to calibrate the vehicle speed at which the strategy is activated for various user driving scenarios, such as congested following and highway emergency obstacle avoidance, thereby calibrating the activation speed of the control strategy and mitigating the problem of false triggering of the control strategy. Next, the present invention uses the target driving simulation vehicle dynamics model to calibrate the vehicle stability state thresholds for various user scenarios, obtaining preset stability state threshold data. Specifically, the preset stability state threshold data in the present invention includes thresholds corresponding to the vehicle yaw rate difference and the center of gravity sideslip angle difference. Accordingly, based on the target driving simulation vehicle dynamics model, the present invention calibrates the thresholds set for the vehicle yaw rate difference and the center of gravity sideslip angle difference for activating the control strategy in the user's driving scenario, thereby obtaining the preset stability state threshold data. In this embodiment of the invention, the vehicle yaw rate difference refers to the deviation between the actual yaw rate and the theoretical yaw rate (the desired yaw rate), and the center of gravity sideslip angle difference refers to the deviation between the actual center of gravity sideslip angle and the desired center of gravity sideslip angle. By calibrating the threshold values ​​corresponding to the vehicle yaw rate difference and the center of gravity sideslip angle difference, the triggering conditions of the stability control system can be determined, that is, the intervention time of the stability control system can be determined, thereby improving the accuracy of control strategy development.

[0052] In some embodiments of the present invention, the proportion of influencing factors of the control strategy control parameters in the user driving scenario is calibrated using a target driving simulation vehicle dynamics model to obtain preset influencing factor proportion data, including but not limited to the following steps: The proportion of influencing factors on the yaw rate difference was determined by using a vehicle dynamics model in a target driving simulation, and the first proportion data was obtained.

[0053] The influence factors of the center of gravity sideslip angle difference were calibrated by using the vehicle dynamics model of the target driving simulation, and the second proportion data was obtained.

[0054] The preset influencing factor percentage data is determined based on the first percentage data and the second percentage data.

[0055] In this specific embodiment, the control strategy control parameters include the yaw rate difference and the center of gravity sideslip angle difference. This embodiment uses a target driving simulation vehicle dynamics model to calibrate the proportion of influencing factors for the yaw rate difference and the center of gravity sideslip angle difference, respectively, to obtain preset influencing factor proportion data. Specifically, this embodiment first calibrates the proportion of influencing factors for the yaw acceleration difference using the target driving simulation vehicle dynamics model to determine the causes affecting the yaw acceleration difference, obtaining first proportion data. Next, this embodiment calibrates the proportion of influencing factors for the center of gravity sideslip angle difference using the target driving simulation vehicle dynamics model to determine the causes affecting the center of gravity sideslip angle difference, obtaining second proportion data. This embodiment quantifies and decomposes the factors causing vehicle imbalance by calibrating the proportion of influencing factors for the yaw rate difference or the center of gravity sideslip angle difference, transforming empirical parameter tuning into data-driven, precise control strategy optimization, effectively improving the accuracy of control strategy development. Finally, this embodiment uses the calibrated first and second proportion data as preset influencing factor proportion data.

[0056] The following section provides a detailed introduction and explanation of the solutions in this embodiment of the invention, using specific examples of vehicle control strategy calibration: For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the calibration process of the vehicle stability control strategy provided in this embodiment of the invention under a dynamic driving simulator. Specifically, this embodiment first acquires vehicle dynamic parameters to construct a high-precision dynamic driving simulator vehicle dynamic model. Next, this embodiment integrates a control strategy onto the dynamic driving simulator vehicle model. Simultaneously, this embodiment acquires the user's driving scenario and then simulates the user's driving scenario using the integrated driving simulator vehicle dynamic model to establish a flexible PID change rule table and calibrate parameters such as vehicle speed and wheel angle when the control strategy is activated, thereby achieving a human-vehicle-scenario-strategy in-loop development. Correspondingly, as... Figure 3 As shown, Figure 3This invention provides a block diagram illustrating the principle of an active front-wheel steering vehicle stability compensation strategy based on a dynamic driving simulator calibration, as provided in this embodiment. Specifically, this embodiment compares a theoretical two-degree-of-freedom vehicle model with a high-precision driving simulator model, using the high-precision driving simulator model in the user's scenario as the actual vehicle model. By comparing the yaw rate and sideslip angle of the theoretical and simulator models, the difference is input into a PID controller. The PID controller then determines the required increase in front wheel steering angle, thereby achieving the goal of controlling vehicle stability. The proportional factor adjusts the increase in front wheel steering angle, achieving personalized vehicle stability compensation. It is easy to understand that this invention replaces the simplified vehicle model in the control strategy with a high-precision driving simulator vehicle model. Drivers conduct multi-scenario driving tests in the simulator, subjectively evaluating the control effect of the vehicle stability control strategy. Based on driver feedback, the parameters of the flexible PID controller are optimized and adjusted, establishing a human-vehicle-control strategy-scenario-in-the-loop development process to achieve rapid and accurate development covering high-frequency user scenarios. Based on a high-precision driving simulator vehicle model and immersive scene, immersive human-computer interaction is achieved, which can significantly shorten the strategy development cycle. At the same time, the flexible PID feedforward control concept is adopted, the strategy is simple, the response is fast, and it can meet personalized needs.

[0057] It should be noted that this invention, through the concept of human-vehicle-scenario-control strategy loop development, enables immersive control strategy development, significantly shortening the development cycle and saving development costs. Furthermore, this invention is developed based on a high-precision dynamic driving simulator's dynamic vehicle model, with virtual calibration before real-vehicle testing. A weighted allocation method is used in the PID controller, and calibration allows for the determination of the proportion of different vehicle response parameters (such as sideslip angle and yaw rate) to the front wheel compensation angle. Specifically, when comparing the theoretical model and the driving simulator model, the yaw rate difference and sideslip angle difference are input to the PID controller. For example, the yaw rate difference is used to calculate the required increase in front wheel angle α, and the sideslip angle difference is used to calculate the required increase in front wheel angle β. The angles α and β are weighted, primarily by setting the weights of the yaw rate and sideslip angle's impact on the vehicle, for example, a 50% weighting. These weights vary depending on the vehicle and are calibrated according to the vehicle's priorities.

[0058] Please see Figure 4 This application also provides a vehicle control strategy calibration system that can implement the above-described vehicle control strategy calibration method. The system includes: The first module 210 is used to acquire vehicle dynamics parameters in order to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters.

[0059] The second module 220 is used to couple the preset control strategy model with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model.

[0060] The third module 230 is used to obtain the user's driving scenario.

[0061] The fourth module 240 is used to calibrate the flexible control parameters based on the user's driving scenario using the target driving simulation vehicle dynamics model, and obtain a preset control parameter change rule table.

[0062] The fifth module 250 is used to calibrate vehicle control strategy parameters based on the user's driving scenario using the target driving simulation vehicle dynamics model, and obtain vehicle control calibration data.

[0063] The sixth module 260 is used to determine the target vehicle control strategy data based on the preset control parameter change rule table and vehicle control calibration data.

[0064] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0065] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described vehicle control strategy calibration method. This electronic device can be any smart terminal, including a tablet computer or an in-vehicle computer.

[0066] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0067] Please see Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 310 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 320 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 320 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310 to execute the vehicle control strategy calibration method of the embodiments of this application. The input / output interface 330 is used to implement information input and output; The communication interface 340 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 350 transmits information between various components of the device (e.g., processor 310, memory 320, input / output interface 330, and communication interface 340); The processor 310, memory 320, input / output interface 330 and communication interface 340 are connected to each other within the device via bus 350.

[0068] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described vehicle control strategy calibration method.

[0069] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0070] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0071] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0072] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0073] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0075] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0076] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0078] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for calibrating a vehicle control strategy, characterized in that, The method includes the following steps: Obtain vehicle dynamics parameters to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters; The preset control strategy model is coupled with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model; Obtain the user's driving scenario; Based on the user driving scenario, the flexible control parameters are calibrated using the target driving simulation vehicle dynamics model to obtain a preset control parameter variation rule table. Based on the user driving scenario, the vehicle control strategy parameters are calibrated using the target driving simulation vehicle dynamics model to obtain vehicle control calibration data. The target vehicle control strategy data is determined based on the preset control parameter change rule table and the vehicle control calibration data.

2. The method according to claim 1, characterized in that, The step of acquiring vehicle dynamics parameters and constructing a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters includes: Obtain the vehicle dynamics parameters; wherein, the vehicle dynamics parameters include the coordinates of the vehicle's center of gravity, the vehicle's moment of inertia, hard point data, bushing stiffness, and steering assist characteristic curve data; The preset driving simulation vehicle dynamics model is constructed based on the vehicle dynamics parameters and the preset vehicle dynamics multibody model.

3. The method according to claim 1, characterized in that, The step of coupling the preset control strategy model with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model includes: The preset control strategy model is constructed based on the vehicle stability control algorithm; The preset control strategy model is coupled with the preset driving simulation vehicle dynamics model to obtain the target driving simulation vehicle dynamics model.

4. The method according to claim 1, characterized in that, The step of calibrating flexible control parameters based on the user driving scenario using the target driving simulation vehicle dynamics model to obtain a preset control parameter change rule table includes: According to the user driving scenario, the preset stability state quantity difference data is calibrated through the target driving simulation vehicle dynamics model, and then a flexible PID change rule table is constructed based on the preset stability state quantity difference data; wherein, the preset stability state quantity difference data includes the difference between the vehicle stability state quantity under lateral disturbance and under no lateral disturbance, and the vehicle stability state quantity includes the vehicle yaw rate and the center of gravity sideslip angle.

5. The method according to claim 1, characterized in that, The step of calibrating vehicle control strategy parameters based on the user driving scenario using the target driving simulation vehicle dynamics model to obtain vehicle control calibration data includes: The target driving simulation vehicle dynamics model is used to perform trigger calibration in the user driving scenario to obtain policy trigger threshold data. The influence factor ratio of the control strategy control parameter object under the user driving scenario is calibrated by the target driving simulation vehicle dynamics model to obtain preset influence factor ratio data. The target steering angle scale factor is obtained by calibrating the wheel angle scale factor corresponding to each vehicle driving mode using the target driving simulation vehicle dynamics model.

6. The method according to claim 5, characterized in that, The step of triggering and calibrating the target driving simulation vehicle dynamics model under the user driving scenario to obtain policy triggering threshold data includes: Based on the user's driving scenario, the vehicle speed at which the strategy is activated is calibrated using the target driving simulation vehicle dynamics model to obtain preset calibrated vehicle speed data. Based on the user driving scenario, the vehicle stability state threshold is calibrated using the target driving simulation vehicle dynamics model to obtain preset stability state threshold data; wherein, the preset stability state threshold data includes thresholds corresponding to the vehicle yaw rate difference and the center of gravity sideslip angle difference.

7. The method according to claim 5, characterized in that, The step involves calibrating the proportion of influencing factors for the control strategy control parameters in the user driving scenario using the target driving simulation vehicle dynamics model, thereby obtaining preset influencing factor proportion data, including: The proportion of influencing factors on the yaw rate difference is determined by the target driving simulation vehicle dynamics model to obtain the first proportion data. The influencing factors of the center of gravity sideslip angle difference are calibrated using the target driving simulation vehicle dynamics model to obtain the second proportion data. The preset influencing factor percentage data is determined based on the first percentage data and the second percentage data.

8. A vehicle control strategy calibration system, characterized in that, The system includes: The first module is used to acquire vehicle dynamics parameters in order to construct a preset driving simulation vehicle dynamics model based on the vehicle dynamics parameters; The second module is used to couple the preset control strategy model with the preset driving simulation vehicle dynamics model to construct the target driving simulation vehicle dynamics model. The third module is used to obtain the user's driving scenario; The fourth module is used to calibrate the flexible control parameters based on the target driving simulation vehicle dynamics model according to the user driving scenario, and obtain a preset control parameter change rule table. The fifth module is used to calibrate vehicle control strategy parameters based on the user driving scenario using the target driving simulation vehicle dynamics model, and obtain vehicle control calibration data. The sixth module is used to determine the target vehicle control strategy data based on the preset control parameter change rule table and the vehicle control calibration data.

9. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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