Crosswind identification method, device and equipment

Abstract

The application relates to the technical field of vehicles and discloses a crosswind identification method, device and equipment, which comprises the following steps: in the case that a vehicle meets crosswind identification conditions, determining ideal state parameters of the vehicle in a crosswind-free environment based on state information of the vehicle; determining a crosswind parameter suffered by the vehicle in a current environment based on the state information, the ideal state parameters and actual state parameters of the vehicle; and judging whether the vehicle is in a crosswind environment based on the crosswind parameter. The entire crosswind identification process does not require additional hardware investment, and crosswind identification can be completed only by using the existing state information of the vehicle, so that the requirement for the hardware configuration of the vehicle is reduced, and the reliability of crosswind identification is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a crosswind identification method, device, and equipment. Background Technology

[0002] Existing crosswind recognition technologies mainly rely on non-essential sensors such as visual sensors or lidar to monitor roadside signs and indirectly infer the presence and intensity of crosswinds through complex algorithms. This results in vehicles being highly dependent on non-essential sensors and having high hardware configuration requirements. Summary of the Invention

[0003] The main objective of this application is to provide a crosswind identification method, apparatus, and device that can solve the technical problem that the existing crosswind identification technology relies heavily on non-essential sensors, resulting in high hardware configuration requirements.

[0004] Firstly, this application proposes a crosswind identification method, the method comprising: If the vehicle meets the crosswind recognition conditions, based on the vehicle's state information, the ideal state parameters of the vehicle in a crosswind-free environment are determined, wherein the ideal state parameters include the ideal yaw rate and the ideal lateral acceleration. Based on the state information, the ideal state parameters, and the actual state parameters of the vehicle, determine the crosswind parameters experienced by the vehicle in the current environment; and The vehicle is determined to be in a crosswind environment based on the crosswind parameters.

[0005] In the above implementation process, the ideal state parameters of the vehicle in a crosswind-free environment are first determined as a benchmark, and then the crosswind parameters are determined based on the deviation between the ideal state parameters and the actual state parameters of the vehicle. Finally, the crosswind environment is determined based on the crosswind parameters. The entire crosswind recognition process does not require additional hardware investment and can complete the crosswind recognition using only the vehicle's existing state information, which reduces the requirements for vehicle hardware configuration and improves the reliability of crosswind recognition.

[0006] In some possible implementations of the first aspect, the actual state parameters include actual yaw rate and actual lateral acceleration, and the crosswind parameters include crosswind speed and crosswind force. The step of determining the crosswind parameters experienced by the vehicle in the current environment based on the state information, the ideal state parameters, and the actual state parameters of the vehicle includes: Based on the ideal yaw rate, the ideal lateral acceleration, the actual yaw rate, and the actual lateral acceleration, the crosswind speed experienced by the vehicle is determined; and Based on the status information and the actual yaw rate, the crosswind force experienced by the vehicle is determined.

[0007] In the above implementation process, the crosswind speed is quantified by the deviation between the ideal and the actual state, and the crosswind force is quantified by the state information and the actual yaw rate. The impact of crosswind on vehicles is characterized from two different dimensions, kinematics and dynamics, respectively, avoiding false triggering or missed identification caused by occasional fluctuations of a single signal.

[0008] In some possible implementations of the first aspect, the step of determining the crosswind speed experienced by the vehicle based on the ideal yaw rate, the ideal lateral acceleration, the actual yaw rate, and the actual lateral acceleration includes: Based on the ideal yaw rate, actual yaw rate, ideal lateral acceleration, and actual lateral acceleration, a residual value for the vehicle under the current environment is determined. This residual value is used to quantify the impact of crosswinds on the vehicle. Based on the residual value and the preset calibration curve, the crosswind speed experienced by the vehicle is determined.

[0009] In the above implementation process, the crosswind speed estimation mechanism based on state residuals is used to quantify the crosswind impact by utilizing the deviation between the actual dynamic response of the vehicle and the prediction of the ideal model. Compared with directly measuring the ambient wind speed, residual calculation can directly reflect the actual degree of interference of crosswinds on the vehicle's handling stability, avoiding interference from harsh environments.

[0010] In some possible implementations of the first aspect, the step of determining the residual value of the vehicle based on the ideal yaw rate, the actual yaw rate, the ideal lateral acceleration, and the actual lateral acceleration includes: Based on the ideal yaw rate and the actual yaw rate, the yaw rate deviation of the vehicle is determined; Based on the ideal lateral acceleration and the actual lateral acceleration, determine the lateral acceleration deviation of the vehicle; and The residual value of the vehicle is determined based on the yaw rate deviation, the lateral acceleration deviation, and a preset proportional coefficient.

[0011] In the above implementation process, by setting a preset proportional coefficient to reasonably match the yaw rate deviation and lateral acceleration deviation, the sensitivity of the residual value to different deviation components can be adaptively adjusted according to the vehicle dynamic characteristics. This ensures that the residual value can accurately reflect the actual severity of crosswind interference under different vehicle models and different speed conditions, thereby improving the robustness of the solution.

[0012] In some possible implementations of the first aspect, the step of determining the crosswind force experienced by the vehicle based on the state information and the actual yaw rate includes: Based on the state information and the actual yaw rate, the yaw moment of the vehicle is determined; The rate of change of yaw rate is determined based on the actual yaw rate. Based on the yaw moment, the rate of change of the yaw angular velocity, and the vehicle's preset moment of inertia, the crosswind moment is determined; and The crosswind force experienced by the vehicle is determined based on the crosswind moment and the distance from the vehicle's wind pressure center to its center of mass.

[0013] In the above implementation process, the crosswind moment is determined by the vehicle's yaw moment, yaw rate of change and moment of inertia. Then, the crosswind force is calculated by combining the distance from the wind pressure center to the center of mass. From the perspective of mechanical balance, the actual force of the crosswind on the vehicle is restored. This complements the calculation of the crosswind speed and makes up for the shortcomings of the residual method in the sensitivity of steady-state crosswind identification.

[0014] In some possible implementations of the first aspect, the crosswind recognition conditions include at least one of the following: the steering wheel angle of the vehicle is less than a preset angle threshold, the brake pedal opening of the vehicle is less than a preset opening threshold, the vehicle speed is greater than a preset speed threshold, the vehicle is in drive, and the vehicle's status flag is in an inactive state.

[0015] In the above implementation process, by pre-setting judgment logic, it is ensured that the crosswind recognition function is activated only under the actual need and appropriate working conditions, avoiding meaningless recognition calculations in non-crosswind scenarios such as low-speed driving and driver active operation, thereby improving the utilization efficiency of system resources.

[0016] In some possible implementations of the first aspect, the state information includes vehicle speed and front wheel steering angle, and the step of determining the ideal state parameters of the vehicle in a windless environment based on the vehicle's state information includes: Obtain the configuration parameters of the vehicle, including vehicle weight, wheelbase, distance from center of gravity to front axle, distance from center of gravity to rear axle, front axle lateral stiffness, and rear axle lateral stiffness. Based on the vehicle speed, the front wheel steering angle, and the configuration parameters, determine the ideal yaw rate of the vehicle in a windless environment; and Based on the vehicle speed and the ideal yaw rate, the ideal lateral acceleration of the vehicle in a windless environment is determined.

[0017] In the above implementation process, the ideal yaw rate and ideal lateral acceleration of the vehicle under no crosswind conditions are compared with the actual measured values ​​to separate the vehicle state deviation caused by crosswinds from the total deviation. This method requires no additional environmental perception sensors; the baseline values ​​can be calculated using only the vehicle's existing dynamic parameters and standard sensor signals. It has low hardware dependency, high versatility, and can be quickly ported and adapted to different vehicle platforms.

[0018] In some possible implementations of the first aspect, the step of determining the ideal yaw rate of the vehicle in a windless environment based on the vehicle speed, the front wheel steering angle, and the configuration parameters includes: Based on the vehicle mass, wheelbase, distance from the center of gravity to the front axle, distance from the center of gravity to the rear axle, front axle lateral stiffness, and rear axle lateral stiffness, the stability coefficient of the vehicle is determined; and Based on the vehicle speed, the front wheel steering angle, the wheelbase, and the stability coefficient, the ideal yaw rate of the vehicle in a windless environment is determined.

[0019] In the above implementation process, by introducing a stability coefficient to nonlinearly correct the vehicle's speed and steering input, it is possible to effectively distinguish between the vehicle's normal dynamic response and abnormal crosswind disturbance under high-speed and large-angle conditions, and avoid misjudging the driver's active lane change or aggressive driving behavior as crosswind.

[0020] Secondly, this application proposes a crosswind identification device, the crosswind identification device comprising: The parameter acquisition module is used to determine the ideal state parameters of the vehicle in a crosswind-free environment based on the vehicle's state information, provided that the vehicle meets the crosswind recognition conditions. The ideal state parameters include the ideal yaw rate and the ideal lateral acceleration. The parameter determination module is used to determine the crosswind parameters experienced by the vehicle in the current environment based on the state information, the ideal state parameters, and the actual state parameters of the vehicle; and The environment judgment module is used to determine whether the vehicle is in a crosswind environment based on the crosswind parameters.

[0021] Thirdly, this application proposes a crosswind identification device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the crosswind identification method as described above.

[0022] Regarding the beneficial effects of any of the technical solutions in the second and third aspects mentioned above, refer to the beneficial effects of the corresponding technical solutions in the first aspect; repeated examples will not be listed here. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a feasible embodiment of the crosswind identification method of this application; Figure 2 This is a flowchart illustrating the crosswind identification condition determination process in this application; Figure 3 This is a flowchart illustrating the process of determining crosswind parameters in one feasible embodiment of the crosswind identification method of this application. Figure 4 This is a schematic diagram of the vehicle force model in the crosswind identification method of this application; Figure 5 This is a schematic diagram of the module structure of the crosswind identification device according to an embodiment of this application; Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the crosswind identification method in this application embodiment.

[0026] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0027] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0028] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0029] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a computer or controller, or an electronic device or crosswind detection device capable of performing the above functions. The following description uses a crosswind detection device as an example to illustrate this embodiment and the subsequent embodiments.

[0030] This application provides a method for identifying crosswinds, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a feasible embodiment of the crosswind identification method of this application.

[0031] In this embodiment, the crosswind identification method includes steps S10 to S30: Step S10: If the vehicle meets the crosswind recognition conditions, determine the ideal state parameters of the vehicle in a crosswind-free environment based on the vehicle's state information. The ideal state parameters include the ideal yaw rate and the ideal lateral acceleration.

[0032] The vehicle's crosswind function primarily addresses situations where the vehicle encounters crosswinds and is considered a safety and stability feature. In its implementation, activation of the crosswind function requires meeting crosswind recognition conditions to avoid interference with crosswind recognition during active steering, braking, or other maneuvers.

[0033] Crosswind recognition conditions include conditions used to determine whether the vehicle is currently in a suitable state for crosswind recognition. For example, crosswind recognition conditions may be certain threshold conditions for functions such as steering wheel angle, vehicle speed, gear position, intelligent driving and ESC (Electronic Stability Control). When the conditions are met, the crosswind function is in an active state.

[0034] State information refers to the vehicle's current operating state parameters, which may include parameters reflecting the vehicle's longitudinal and lateral motion characteristics, such as vehicle speed and front wheel steering angle. Ideal state parameters, based on state information, are the state parameters that the vehicle should possess under ideal operating conditions without crosswind interference, and may include ideal yaw rate and ideal lateral acceleration. When determining ideal state parameters, the ideal motion state can be calculated based on the vehicle dynamics model and the vehicle's structural configuration.

[0035] In some feasible implementations, to avoid interference with crosswind recognition results when the vehicle performs active steering, braking, or other operations, the crosswind recognition conditions may include at least one of the following: the vehicle's steering wheel angle is less than a preset angle threshold, the vehicle's brake pedal opening is less than a preset opening threshold, the vehicle's speed is greater than a preset speed threshold, the vehicle is in drive, and the vehicle's status flag is inactive.

[0036] For example, refer to Figure 2 , Figure 2 This is a flowchart illustrating the crosswind detection condition determination process of this application. Before the vehicle's crosswind function is activated, the steering wheel angle output from VSE (Vehicle Speed ​​Estimation) and SAS (Steering Angle Sensor) can be obtained via the bus. Gear information The system monitors the status of intelligent driving and ESC functions, as well as signals from the IMU (Inertial Measurement Unit) and altimeter. These signals are then input into a specific decision-making module, where precondition checks are performed using multiple criteria, such as... By setting the steering wheel angle Less than the preset angle threshold Filtering driver-initiated steering conditions; Set the vehicle's brake pedal opening. Less than the preset opening threshold Filter depth braking conditions; Set the vehicle speed Speed ​​greater than the preset vehicle speed threshold Filter the operating conditions of the vehicle at low speeds. Set the crosswind detection condition to the gear position potentiometer. The current gear of the vehicle is displayed as forward. Functional status bit = The vehicle's intelligent driving and ESC functions are confirmed to be inactive. When at least one of the above conditions is met, the system determines that the crosswind recognition condition is satisfied, thus allowing the vehicle's crosswind recognition function to enter an active state to avoid wasting system resources. Of course, in the specific implementation process, technicians can also choose some or all of the above conditions as the judgment criteria according to actual needs.

[0037] In some feasible implementations, the ideal state parameters of the current vehicle can be determined based on the current vehicle's state information and its fixed configuration (such as mass, wheelbase, center of gravity position, front and rear axle lateral stiffness, etc.). In a specific scenario, the driver changes the front wheel angle by turning the steering wheel, thereby generating yaw and lateral movement of the vehicle. Since the higher the vehicle speed, the more significant the yaw response and lateral acceleration response under the same steering input, the front wheel angle and vehicle speed jointly determine the vehicle's ideal motion state response from the two dimensions of "driving intention" and "motion amplitude," respectively. The vehicle's fixed configuration can be pre-calibrated at the factory. Based on this, in the actual calculation process, only the current vehicle speed and front wheel angle need to be obtained in real time to determine the ideal state parameters. At this time, the state information can include vehicle speed and front wheel angle. The step of determining the ideal state parameters of the vehicle in a windless environment based on the vehicle's state information can include steps S101~S103: Step S101: Obtain the configuration parameters of the vehicle, including the vehicle weight, wheelbase, distance from the center of gravity to the front axle, distance from the center of gravity to the rear axle, front axle lateral stiffness, and rear axle lateral stiffness.

[0038] In this embodiment, the state information refers to the vehicle's real-time motion parameters, including vehicle speed and front wheel steering angle. The configuration parameters, on the other hand, are inherent attributes of the vehicle itself, including vehicle mass. Wheelbase Distance from center of gravity to front axle Distance from center of gravity to rear axle Front axle lateral stiffness and rear axle lateral stiffness The aforementioned configuration parameters can be pre-calibrated and stored in the storage medium of the vehicle control unit at the factory, or they can be obtained from external devices (such as cloud servers) via the vehicle communication interface.

[0039] Step S102: Based on the vehicle speed, the front wheel angle, and the configuration parameters, determine the ideal yaw rate of the vehicle in a windless environment.

[0040] In this embodiment, the ideal yaw rate is the vehicle's yaw rate under ideal operating conditions without external crosswind interference, determined based on state information. The ideal yaw rate can serve as a reference for determining whether the vehicle is affected by crosswind interference.

[0041] Specifically, the ideal yaw rate can be calculated using a two-degree-of-freedom linear single-vehicle model based on the current vehicle speed, front wheel steering angle, and configuration parameters. The ideal yaw rate characterizes the yaw response the vehicle should produce under the current steering input. If there is a significant deviation between the actual yaw rate and the ideal yaw rate, it indicates that the vehicle is affected by external disturbance torques such as crosswinds.

[0042] Understandably, the process of determining the ideal yaw rate can be calculated in real time when the crosswind detection function is activated, or it can be achieved using a pre-calibrated mapping table. When using real-time calculation, a complete calculation is performed each time a new vehicle speed signal and front wheel steering angle signal are acquired, ensuring that the ideal yaw rate can keep up with changes in the vehicle's driving state. When using the mapping table method, the corresponding ideal yaw rate can be pre-calculated in the offline calibration stage based on different combinations of vehicle speed and front wheel steering angle and stored as a two-dimensional lookup table. During online operation, the ideal yaw rate value under the current operating condition can be obtained directly through table lookup and interpolation, thereby effectively reducing real-time calculation overhead and improving the system's response speed.

[0043] In some feasible implementations, to distinguish between the normal dynamic response of a vehicle and abnormal crosswind disturbances under high-speed, large-angle driving conditions, and to avoid misinterpreting the driver's active lane changes or aggressive driving behavior as crosswinds, step S102 may include: Based on the vehicle mass, wheelbase, distance from the center of gravity to the front axle, distance from the center of gravity to the rear axle, front axle lateral stiffness, and rear axle lateral stiffness, the vehicle's stability coefficient is determined; based on the vehicle speed, front wheel steering angle, wheelbase, and stability coefficient, the vehicle's ideal yaw rate in a windless environment is determined.

[0044] For example, before calculating the ideal yaw rate, the vehicle's stability coefficient is determined based on the vehicle's mass, wheelbase, distance from the center of gravity to the front axle, distance from the center of gravity to the rear axle, front axle lateral stiffness, and rear axle lateral stiffness, as follows:

[0045] in, This is the distance from the center of gravity to the front axle. This is the distance from the center of mass to the rear axle. Wheelbase For front axle lateral stiffness, For rear axle lateral stiffness, The stability coefficient reflects the influence of the vehicle's mass distribution and tire lateral slip characteristics on the yaw response.

[0046] After determining the vehicle's stability coefficient, by combining the stability coefficient... After applying a nonlinear correction to the theoretical gain, the ideal yaw rate of the vehicle in a windless environment is obtained as follows:

[0047] in, For vehicle speed, For the front wheel steering angle, This is the stability coefficient. The effects of tire lateral slip characteristics and vehicle speed are introduced to reduce the kinematic response at higher vehicle speeds, thereby more accurately simulating the steady-state dynamics of real vehicles. The kinematic relationships based on Ackermann steering geometry are described, i.e., the vehicle's yaw response assuming no tire slippage. The above-obtained... This refers to the ideal yaw rate that a vehicle should produce when it is on a straight road, without crosswind interference, and only controlled by the driver's steering input.

[0048] Step S103: Based on the vehicle speed and the ideal yaw rate, determine the ideal lateral acceleration of the vehicle in a windless environment.

[0049] For example, based on common sense about vehicle dynamics, under the steady-state / quasi-steady-state driving assumptions of a level road surface, no roll, no crosswinds, and the tires in the linear sideslip zone, the lateral motion of the vehicle is entirely provided by the centripetal force of the yaw motion. Therefore, the ideal lateral acceleration is... It can be calculated using the following kinematic formula:

[0050] in, For vehicle speed, The ideal yaw rate.

[0051] Understandably, besides directly calculating using multiplication relationships, ideal lateral acceleration can also be obtained by consulting a pre-calibrated two-dimensional mapping table based on vehicle speed and ideal yaw rate. This mapping table can be pre-calibrated through offline simulation or real-vehicle testing to compensate for deviations in ideal lateral acceleration from the kinematic theoretical value caused by tire nonlinear characteristics, load transfer, and other factors. When the vehicle is traveling on a low-friction surface or under conditions of high lateral acceleration, the tire's operating state enters the nonlinear region. In this case, the ideal lateral acceleration value obtained by using the calibrated mapping table is closer to the vehicle's actual response characteristics than the purely kinematic calculation value, which can further improve the accuracy of crosswind identification benchmarks.

[0052] Step S20: Based on the state information, the ideal state parameters, and the actual state parameters of the vehicle, determine the crosswind parameters experienced by the vehicle in the current environment.

[0053] In this embodiment, the calculated crosswind parameters can be used to characterize the impact of the current environmental crosswind on the vehicle. Crosswind parameters may include crosswind speed and crosswind force. State information may include vehicle speed and front wheel steering angle; ideal state parameters may include ideal yaw rate and ideal lateral acceleration; actual state parameters include actual yaw rate and actual lateral acceleration collected in real time by sensors such as inertial measurement units.

[0054] Specifically, based on the above input parameters, this embodiment can determine the crosswind parameters from two dimensions: kinematic response and mechanical response. For example, by comparing the deviation between the ideal state parameters and the actual state parameters, the crosswind speed can be calculated. This path focuses on the degree of deviation of the vehicle's motion state caused by the crosswind. Based on the state information and the actual yaw rate, the crosswind force can be inferred from the vehicle dynamics model. This path focuses on the magnitude of the actual force exerted by the crosswind on the vehicle.

[0055] In specific scenarios, crosswind speed and crosswind force are independent of each other and complement each other, avoiding false triggering or missed identification caused by occasional fluctuations of a single signal.

[0056] Step S30: Determine whether the vehicle is in a crosswind environment based on the crosswind parameters.

[0057] In this embodiment, the system determines whether a vehicle is currently in a crosswind environment based on crosswind speed, crosswind force, and their respective threshold values. For example, when the crosswind speed reaches or exceeds a preset wind speed threshold, and the crosswind force simultaneously reaches or exceeds a preset wind force threshold, the system determines that the vehicle is currently in a crosswind environment; otherwise, it determines that the vehicle is not in a crosswind environment.

[0058] For example, when judging based on crosswind speed, the crosswind speed is compared with a preset wind speed threshold. If the crosswind speed is greater than or equal to the preset wind speed threshold, the wind speed condition is deemed met, and the vehicle can be placed in a crosswind preparedness environment. The preset wind speed threshold value can be calibrated based on the vehicle's crosswind resistance, vehicle platform characteristics, and the intervention requirements of the crosswind control function. Different vehicle models can have different threshold settings. For example, for vehicles with a high center of gravity and a large frontal area, which are more sensitive to crosswinds, the wind speed threshold can be set to a relatively low value; for sedan models with a low center of gravity and good aerodynamic characteristics, the wind speed threshold can be set to a relatively high value.

[0059] For example, when judging based on crosswind force, the crosswind force value is compared with a preset wind force threshold. If the crosswind force value is greater than or equal to the preset wind force threshold, the wind condition is determined to be met, and the vehicle can be in a crosswind environment. The specific value of the preset wind force threshold can be calibrated based on inherent parameters such as the vehicle's mass, frontal area, and wind pressure center location, or it can be dynamically adjusted according to the needs of the crosswind control function.

[0060] Wind speed and wind force conditions are judged independently, and the order of their judgment can be interchanged. It is possible to judge wind speed conditions first and then wind force conditions, or vice versa. This embodiment does not limit this.

[0061] After determining that the vehicle is in a crosswind environment, a crosswind identification mark (such as a crosswind marker) can be generated. This mark can be used to trigger the vehicle's crosswind control function to intervene, such as by means of active steering compensation, differential braking, suspension damping adjustment, etc., to counteract the adverse effects of crosswind on vehicle stability.

[0062] In this embodiment, the ideal state parameters of the vehicle in a crosswind-free environment are first determined as a benchmark, and then the crosswind parameters are determined based on the deviation between the ideal state parameters and the actual state parameters of the vehicle. Finally, the crosswind environment is determined based on the crosswind parameters. The entire crosswind recognition process does not require additional hardware investment and can complete the crosswind recognition using only the vehicle's existing state information, which reduces the requirements for vehicle hardware configuration and improves the reliability of crosswind recognition.

[0063] In one feasible embodiment of this application, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the process of determining crosswind parameters in one feasible embodiment of the crosswind identification method of this application.

[0064] In this embodiment, the ideal state parameters include the ideal yaw rate and the ideal lateral acceleration, the actual state parameters include the actual yaw rate and the actual lateral acceleration, and the crosswind parameters include the crosswind speed and the crosswind force.

[0065] Step S20 may include steps S201 to S202: Step S201: Determine the crosswind speed experienced by the vehicle based on the ideal yaw rate, the ideal lateral acceleration, the actual yaw rate, and the actual lateral acceleration.

[0066] In this embodiment, the crosswind speed can be determined based on the degree of deviation between the ideal state parameters and the actual state parameters. The specifically determined wind speed value is not the directly measured atmospheric wind speed, but an estimated wind speed that reflects the equivalent intensity of the crosswind's impact on the vehicle.

[0067] Specifically, ideal yaw rate and ideal lateral acceleration characterize the expected motion of a vehicle in a windless environment, while actual yaw rate and actual lateral acceleration characterize the vehicle's actual motion in the current environment. When a vehicle is affected by crosswinds, the actual state deviates from the ideal state, and the degree of deviation is positively correlated with the crosswind intensity: the stronger the crosswind, the more significant the unexpected yaw motion and lateral displacement of the vehicle, and the greater the deviation of the actual values ​​from the ideal values.

[0068] In some feasible implementations, a crosswind speed estimation mechanism based on state residuals can be used to directly reflect the actual degree of interference of crosswinds on vehicle handling stability, thus avoiding interference from adverse environments. In this case, step S201 may include steps S2011-S2012: Step S2011: Based on the ideal yaw rate, the actual yaw rate, the ideal lateral acceleration, and the actual lateral acceleration, determine the residual value of the vehicle in the current environment. The residual value is used to quantify the degree of influence of crosswind on the vehicle.

[0069] In this embodiment, the residual value is used to reflect the degree of interference of crosswinds on the vehicle's yaw and lateral movements. Specifically, a preset fusion algorithm can be used to integrate the deviation information of the two dimensions into a comprehensive index, namely the residual value. The magnitude of this residual value directly reflects the intensity of the interference of crosswind on the vehicle's current motion state: the larger the residual value, the more serious the deviation of the vehicle's actual motion state from the ideal motion state in a crosswind-free environment, that is, the stronger the crosswind influence on the vehicle; conversely, the smaller the residual value, the weaker the crosswind influence on the vehicle.

[0070] In some feasible implementations, the sensitivity of the residual value to different deviation components can be adaptively adjusted according to the vehicle dynamics characteristics to ensure that the residual value can accurately reflect the actual severity of crosswind interference under different vehicle models and speed conditions, thereby improving the robustness of the solution. In this case, step S2011 may include: Based on the ideal yaw rate and the actual yaw rate, the yaw rate deviation of the vehicle is determined; based on the ideal lateral acceleration and the actual lateral acceleration, the lateral acceleration deviation of the vehicle is determined; based on the yaw rate deviation, the lateral acceleration deviation, and a preset proportionality coefficient, the residual value of the vehicle is determined.

[0071] For example, firstly based on the ideal yaw rate (The vehicle's rotational speed in the absence of crosswinds), and the actual yaw rate collected in real time by the IMU gyroscope. The difference between the two is calculated as the yaw rate deviation. ,as follows:

[0072] in, This indicates that the vehicle did not actually rotate enough, for example, a crosswind blew the car away from the intended turning arc; if This indicates that the vehicle actually rotated excessively, for example, crosswinds exacerbated the turning tendency. The deviation directly reflects the direction and intensity of the crosswind's interference with the vehicle's attitude.

[0073] Then, based on ideal lateral acceleration (The vehicle's rotational speed in the absence of crosswinds), and the actual lateral acceleration collected in real time by the IMU gyroscope. The difference between the two is calculated as the lateral acceleration deviation. ,as follows:

[0074] Lateral acceleration deviation This reflects the degree of deviation between the vehicle's actual driving trajectory and the driver's intended steering trajectory. Crosswinds can cause unexpected lateral displacement of the vehicle, which is reflected in this deviation item.

[0075] In obtaining the yaw rate deviation and lateral acceleration deviation The residual values ​​can then be obtained through weighted fusion, as follows:

[0076] in, This is the yaw rate proportionality coefficient. This is the lateral acceleration proportionality coefficient, which is pre-calibrated based on the vehicle's dynamic performance in crosswinds.

[0077] because and Since their dimensions are different, they cannot be directly added together. Therefore, we can introduce... ,right Process it so that its rate of change characteristics are similar to The dimensions of the data should be kept consistent. It should be noted that this processing is to eliminate dimensional differences and make the two data points additive so that weighted fusion can be performed.

[0078] According to the formula for calculating the residual value, its dynamic performance is related to the vehicle's yaw rate and lateral acceleration, which is consistent with the vehicle's performance in crosswinds. Therefore, the wind speed can be mapped through the residual value.

[0079] Step S2012: Based on the residual value and the preset calibration curve, determine the crosswind speed experienced by the vehicle.

[0080] In this embodiment, the preset calibration curve is established in advance through experiments or simulations and reflects the residual value. Equivalent crosswind speed The curve representing the mapping relationship between the two. The preset calibration curve is stored in the non-volatile memory of the vehicle control unit. During actual operation, the residual value is used as input, and the corresponding crosswind speed value can be obtained by querying the calibration curve.

[0081] In some feasible implementations, the x-axis of the preset calibration curve is the residual value R, and the y-axis is the equivalent crosswind speed. The specific form of the preset calibration curve (such as linear, nonlinear, piecewise function, etc.) can be determined by a combination of factors such as the vehicle's aerodynamic characteristics, suspension characteristics, and tire characteristics, and can be calibrated through real vehicle crosswind tests or computational fluid dynamics simulations.

[0082] Since the residual value comprehensively reflects the degree of interference of crosswinds on the yaw and lateral motion of the vehicle, and there is a definite correspondence between the degree of interference and the crosswind speed—that is, the greater the crosswind speed, the stronger the lateral force and yaw moment on the vehicle, the more serious the deviation of the actual state from the ideal state, and the larger the residual value—a preset calibration curve is used to map the residual value to the crosswind speed.

[0083] In some feasible implementations, the preset calibration curve can also be stored in the vehicle control unit in the form of a lookup table. For example, the range of residual values ​​can be divided into several continuous intervals, each interval corresponding to a wind speed output value or a set of interpolation nodes. During actual operation, it is determined which interval the current residual value falls into, and then the corresponding accurate crosswind speed value is calculated through linear interpolation or spline interpolation.

[0084] Step S202: Determine the crosswind force experienced by the vehicle based on the status information and the actual yaw rate.

[0085] In this embodiment, the crosswind force is the actual lateral force generated by the crosswind acting on the side of the vehicle, which is derived from the moment balance equation through the vehicle dynamics model.

[0086] For example, refer to Figure 4 , Figure 4 This is a schematic diagram of the vehicle force model in the crosswind identification method of this application. The diagram shows the force on the vehicle wheels under the action of crosswind. This represents the equivalent concentrated lateral force exerted by a crosswind on the side of the vehicle. Among these forces... and These represent the longitudinal force and lateral force acting on the left front wheel, respectively. and These represent the longitudinal force and lateral force acting on the right front wheel, respectively. and These represent the longitudinal force and lateral force acting on the left rear wheel, respectively. and These represent the longitudinal force and lateral force acting on the right rear wheel, respectively. This indicates the action acting on the vehicle about its vertical axis (i.e.) The yaw moment of the axle is obtained by combining the moments generated by the longitudinal and lateral components of the tire forces relative to the vehicle's center of gravity. The above forces and moments satisfy the vehicle dynamics equilibrium relationship.

[0087] In some feasible implementations, the actual force exerted by the crosswind on the vehicle can be reconstructed from a mechanical equilibrium perspective, complementing the calculation of the crosswind speed to compensate for the shortcomings of the residual method in steady-state crosswind identification sensitivity. In this case, step S202 may include: Based on the state information and the actual yaw rate, the yaw moment of the vehicle is determined; the rate of change of yaw rate is determined based on the actual yaw rate; the crosswind moment is determined based on the yaw moment, the rate of change of yaw rate, and the vehicle's preset moment of inertia; and the crosswind force experienced by the vehicle is determined based on the crosswind moment and the distance from the vehicle's wind pressure center to its center of mass.

[0088] For example, based on the current vehicle state information (vehicle speed and front wheel steering angle) and the actual yaw rate. Calculate the yaw moment generated by the vehicle itself under normal steering conditions. ,as follows:

[0089] in, The current speed of the vehicle. This is the actual yaw rate. This is the front wheel steering angle. The remaining parameters are fixed parameters for the vehicle. The stability coefficient, The average steering angle of the front wheels. This is the distance from the center of gravity to the front axle. This is the distance from the center of mass to the rear axle. Wheelbase For front axle lateral stiffness, This refers to the rear axle lateral stiffness.

[0090] The above steps quantify the internal torques generated by "driver's active operation" and "normal vehicle dynamic response." In subsequent torque balance calculations, these known torques must be subtracted to accurately separate the unknown disturbances caused by external crosswinds.

[0091] Next, the actual yaw rate is obtained. The rate of change over time, i.e., the rate of change of yaw angular velocity. In discrete control systems, the rate of change of yaw rate can be obtained by performing a first-order backward difference on the actual yaw rate of two consecutive frames. This embodiment will not describe this in detail.

[0092] According to the torque balance equation for rigid body rotation about a fixed axis, the yaw motion of a vehicle is caused by both internal and external torques. By establishing the following equilibrium relationship, the yaw torque generated by the crosswind can be obtained:

[0093] in, Let be the moment of inertia of the vehicle about the z-axis. The torque generated by longitudinal forces (including driving and braking forces) on a vehicle. This refers to the torque generated by the lateral forces (tire lateral force) on the vehicle. During the vehicle development phase, the vehicle's rotational inertia around the z-axis can be measured using the three-line torsion pendulum method or an inertial parameter measurement test bench, and stored as a fixed parameter in the vehicle control unit. and Both can be calculated based on the longitudinal and lateral forces of the vehicle's four wheels combined with the tire contact patch position, or they can be extrapolated based on the vehicle's longitudinal acceleration, lateral acceleration, and overall vehicle mass.

[0094] When the vehicle is traveling at a constant speed in a straight line or at approximately a constant speed in a straight line... and The values ​​of the two items are relatively small and can be simplified or ignored. At this point, the crosswind force can be obtained by analyzing the vehicle's center of gravity and the center of wind pressure affected by the crosswind. size:

[0095] in, WindPointDist is the distance from the vehicle's center of pressure to its center of mass. The center of pressure is the point of equivalent pressure when crosswinds act on the side of the vehicle, and its distance to the center of mass, WindPointDist, is an aerodynamic parameter of the vehicle. Both the center of pressure and WindPointDist can be pre-determined during the vehicle development phase through wind tunnel testing or computational fluid dynamics simulation and stored as fixed parameters in the non-volatile memory of the onboard control unit.

[0096] Since the effect of crosswinds on a vehicle can be equivalent to a concentrated force acting at the center of wind pressure, and the torque generated by this concentrated force relative to the vehicle's center of mass is the crosswind torque. Therefore, dividing the crosswind moment by the distance from the center of pressure to the center of mass yields the equivalent crosswind force. This is obtained through the above calculations. This refers to the quantified crosswind force, which represents the intensity of the objective physical interference of crosswinds on vehicle stability.

[0097] In this embodiment, crosswind speed is quantified by the deviation between ideal and actual states, and crosswind force is quantified by state information and actual yaw rate. This allows for the separate characterization of the crosswind's impact on the vehicle from both kinematic and dynamic dimensions, avoiding false triggering or missed identification caused by occasional fluctuations in a single signal.

[0098] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the crosswind identification method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0099] This application also provides a crosswind identification device, please refer to... Figure 5 The crosswind detection device includes: The parameter acquisition module 10 is used to determine the ideal state parameters of the vehicle in a crosswind-free environment based on the vehicle's state information, provided that the vehicle meets the crosswind identification conditions. The ideal state parameters include the ideal yaw rate and the ideal lateral acceleration. Parameter determination module 20 is used to determine the crosswind parameters experienced by the vehicle in the current environment based on the state information, the ideal state parameters, and the actual state parameters of the vehicle; and The environment judgment module 30 is used to determine whether the vehicle is in a crosswind environment based on the crosswind parameters.

[0100] The crosswind identification device provided in this application, employing the crosswind identification method in the above embodiments, can solve the technical problem that existing crosswind identification technologies rely heavily on non-essential sensors, resulting in high hardware configuration requirements. Compared with the prior art, the beneficial effects of the crosswind identification device provided in this application are the same as those of the crosswind identification method provided in the above embodiments, and other technical features in the crosswind identification device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0101] This application provides a crosswind identification device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the crosswind identification method in Embodiment 1 above.

[0102] The following is for reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing the crosswind detection device in the embodiments of this application. The crosswind detection device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The crosswind identification device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0103] like Figure 6As shown, the crosswind detection device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the crosswind detection device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the crosswind identification device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows crosswind identification devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0104] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0105] The crosswind identification device provided in this application, employing the crosswind identification method in the above embodiments, can solve the technical problem that existing crosswind identification technologies rely heavily on non-essential sensors, resulting in high hardware configuration requirements. Compared with the prior art, the beneficial effects of the crosswind identification device provided in this application are the same as those of the crosswind identification method provided in the above embodiments, and other technical features of this crosswind identification device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0106] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0108] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the crosswind identification method in the above embodiments.

[0109] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0110] The aforementioned computer-readable storage medium may be included in the crosswind identification device; or it may exist independently and not assembled into the crosswind identification device.

[0111] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the crosswind identification device, cause the crosswind identification device to perform the crosswind identification method described above.

[0112] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0114] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0115] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described crosswind identification method. This solves the technical problem in the prior art where crosswind identification technology is highly dependent on non-essential sensors, leading to high hardware configuration requirements. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the crosswind identification method provided in the above embodiments, and will not be repeated here.

[0116] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the crosswind identification method described above.

[0117] The computer program product provided in this application can solve the technical problem that the existing crosswind recognition technology relies heavily on non-essential sensors, resulting in high hardware configuration requirements. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the crosswind recognition method provided in the above embodiments, and will not be repeated here.

[0118] The above description is only a part of the embodiments of this application and does not limit the scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of this application.

[0119] It should be noted that the data collection, tag management, rule setting, and push decision-making processes involved in this application are designed to work with other technical features to solve technical problems. They do not involve or support any illegal activities. Any data processing that may violate laws and regulations (such as unauthorized collection of privacy data, generation of discriminatory tags, setting unfair rules, or pushing illegal information) is not within the scope of protection of this application's technical solution. Of course, the user data in this application will be encrypted, anonymized, or de-identified before storage to ensure user data security.

Claims

1. A method for identifying crosswinds, characterized in that, The crosswind identification method includes: If the vehicle meets the crosswind recognition conditions, based on the vehicle's state information, the ideal state parameters of the vehicle in a crosswind-free environment are determined, wherein the ideal state parameters include the ideal yaw rate and the ideal lateral acceleration. Based on the state information, the ideal state parameters, and the actual state parameters of the vehicle, determine the crosswind parameters experienced by the vehicle in the current environment; and The vehicle is determined to be in a crosswind environment based on the crosswind parameters.

2. The crosswind identification method as described in claim 1, characterized in that, The actual state parameters include the actual yaw rate and the actual lateral acceleration, and the crosswind parameters include the crosswind speed and the crosswind force. The step of determining the crosswind parameters experienced by the vehicle in the current environment based on the state information, the ideal state parameters, and the actual state parameters of the vehicle includes: Based on the ideal yaw rate, the ideal lateral acceleration, the actual yaw rate, and the actual lateral acceleration, the crosswind speed experienced by the vehicle is determined; and Based on the status information and the actual yaw rate, the crosswind force experienced by the vehicle is determined.

3. The crosswind identification method as described in claim 2, characterized in that, The step of determining the crosswind speed experienced by the vehicle based on the ideal yaw rate, the ideal lateral acceleration, the actual yaw rate, and the actual lateral acceleration includes: Based on the ideal yaw rate, actual yaw rate, ideal lateral acceleration, and actual lateral acceleration, a residual value for the vehicle under the current environment is determined. This residual value is used to quantify the impact of crosswinds on the vehicle. Based on the residual value and the preset calibration curve, the crosswind speed experienced by the vehicle is determined.

4. The crosswind identification method as described in claim 3, characterized in that, The step of determining the residual value of the vehicle based on the ideal yaw rate, the actual yaw rate, the ideal lateral acceleration, and the actual lateral acceleration includes: Based on the ideal yaw rate and the actual yaw rate, the yaw rate deviation of the vehicle is determined; Based on the ideal lateral acceleration and the actual lateral acceleration, determine the lateral acceleration deviation of the vehicle; and The residual value of the vehicle is determined based on the yaw rate deviation, the lateral acceleration deviation, and a preset proportional coefficient.

5. The crosswind identification method as described in claim 2, characterized in that, The step of determining the crosswind force experienced by the vehicle based on the state information and the actual yaw rate includes: Based on the state information and the actual yaw rate, the yaw moment of the vehicle is determined; The rate of change of yaw rate is determined based on the actual yaw rate. Based on the yaw moment, the rate of change of the yaw angular velocity, and the vehicle's preset moment of inertia, the crosswind moment is determined; and The crosswind force experienced by the vehicle is determined based on the crosswind moment and the distance from the vehicle's wind pressure center to its center of mass.

6. The crosswind identification method as described in any one of claims 1 to 5, characterized in that, The crosswind detection conditions include at least one of the following: the vehicle's steering wheel angle is less than a preset angle threshold, the vehicle's brake pedal opening is less than a preset opening threshold, the vehicle's speed is greater than a preset speed threshold, the vehicle is in drive, and the vehicle's status flag is inactive.

7. The crosswind identification method as described in any one of claims 1 to 5, characterized in that, The state information includes vehicle speed and front wheel steering angle. The step of determining the ideal state parameters of the vehicle in a windless environment based on the vehicle's state information includes: Obtain the configuration parameters of the vehicle, including vehicle weight, wheelbase, distance from center of gravity to front axle, distance from center of gravity to rear axle, front axle lateral stiffness, and rear axle lateral stiffness. Based on the vehicle speed, the front wheel steering angle, and the configuration parameters, determine the ideal yaw rate of the vehicle in a windless environment; and Based on the vehicle speed and the ideal yaw rate, the ideal lateral acceleration of the vehicle in a windless environment is determined.

8. The crosswind identification method as described in claim 7, characterized in that, The step of determining the ideal yaw rate of the vehicle in a windless environment based on the vehicle speed, the front wheel steering angle, and the configuration parameters includes: Based on the vehicle mass, wheelbase, distance from the center of gravity to the front axle, distance from the center of gravity to the rear axle, front axle lateral stiffness, and rear axle lateral stiffness, the stability coefficient of the vehicle is determined; and Based on the vehicle speed, the front wheel steering angle, the wheelbase, and the stability coefficient, the ideal yaw rate of the vehicle in a windless environment is determined.

9. A crosswind detection device, characterized in that, The device includes: The parameter acquisition module is used to determine the ideal state parameters of the vehicle in a crosswind-free environment based on the vehicle's state information, provided that the vehicle meets the crosswind recognition conditions. The ideal state parameters include the ideal yaw rate and the ideal lateral acceleration. The parameter determination module is used to determine the crosswind parameters experienced by the vehicle in the current environment based on the state information, the ideal state parameters, and the actual state parameters of the vehicle; and The environment judgment module is used to determine whether the vehicle is in a crosswind environment based on the crosswind parameters.

10. A crosswind detection device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the crosswind identification method as described in any one of claims 1 to 8.

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