Cross-wind telemetry method, device, equipment and storage medium

Abstract

The application discloses a cross-wind measurement and control method, device and equipment and a storage medium, relates to the technical field of automobile electronic control and vehicle dynamics, and comprises the following steps: collecting real-time sensing information of a vehicle output by a sensor; calculating cross-wind force based on the real-time sensing information of the vehicle; determining a non-cross-wind target interference working condition through a preset dynamic calculation strategy; and making a comprehensive cross-wind judgment and control decision in combination with the cross-wind force and the non-cross-wind target interference working condition. According to the scheme, the cross-wind force is calculated by collecting real-time sensing information of a vehicle, the non-cross-wind target interference working condition is identified according to the dynamic calculation strategy, and the comprehensive control decision is linked, so that cross-wind disturbance and other vehicle interference working conditions such as a road surface can be accurately distinguished under complex road environments and weather conditions.

Description

Technical Field

[0001] This application relates to the fields of automotive electronic control and vehicle dynamics technology, and in particular to crosswind measurement and control methods, devices, equipment, and storage media. Background Technology

[0002] Crosswinds typically refer to strong lateral winds with an angle between 30° and 150° between the wind direction and the road direction. These wind conditions are commonly found in special terrain areas such as bridges, open sections around highways, valley entrances, coastal areas, tunnel entrances and exits, and embankment roads. Crosswinds have several serious impacts on driving safety: they weaken tire lateral grip, easily causing vehicles to veer, skid, or even roll over; high-center-of-gravity vehicles are more significantly affected, experiencing low-frequency body sway; and sudden crosswinds are difficult to predict, leaving drivers insufficient reaction time and significantly increasing driving risks.

[0003] In existing technologies, crosswinds are mainly detected by two methods: one is to rely on sensors to measure the yaw rate, calculate the theoretical value by combining it with a vehicle model, and compare the difference between the two with a threshold to determine the crosswind; the other is to combine the location of special road sections such as bridges, meteorological wind force, and parameters such as vehicle speed and steering angle to construct a crosswind stability factor to determine the crosswind conditions and intensity.

[0004] However, existing technical solutions mostly rely on a single calculation of yaw rate deviation to determine whether crosswind conditions exist. The robustness of the detection results is problematic because they cannot effectively exclude completely similar vehicle yaw behavior caused by other road conditions such as hydroplaning, side slopes, or ruts.

[0005] Therefore, there is an urgent need for a crosswind detection solution that can accurately distinguish crosswind disturbances from other vehicle interference conditions such as road surface disturbances without the need for additional sensors and hardware, thereby improving the reliability of crosswind identification.

[0006] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0007] The main purpose of this application is to provide a crosswind measurement and control method, device, equipment and storage medium, which aims to solve the technical problem of how to accurately distinguish crosswind disturbance from other vehicle interference conditions such as road surface in complex road environments and weather conditions.

[0008] To achieve the above objectives, this application proposes a crosswind monitoring and control method, which includes: Collect real-time vehicle sensor information; Based on the real-time sensor information of the vehicle, calculate the crosswind force; The interference conditions of non-crosswind targets are determined by a preset dynamic calculation strategy; A comprehensive judgment and control decision on crosswind is made by combining the crosswind force and the non-crosswind target interference conditions.

[0009] In one embodiment, the step of calculating the crosswind force based on the vehicle's real-time sensor information includes: Based on the real-time sensor information of the vehicle, the inertial torque is calculated using a pre-trained vehicle yaw inertia model. The tire torque is calculated using a pre-defined two-degree-of-freedom differential equation of vehicle motion. Obtain braking control parameters and power torque regulation parameters, and convert them into equivalent yaw moment; Combining the inertial torque, tire torque, and equivalent yaw torque, the crosswind interference torque is calculated using a preset torque balance equation. Using a pre-calibrated wind dynamic model and the distance between the wind pressure center and the vehicle's center of gravity, the crosswind force is calculated based on the crosswind disturbance moment.

[0010] In one embodiment, the step of determining the non-crosswind target interference condition through a preset dynamic calculation strategy includes: The adaptive slip ratio offset corresponding to the real-time vehicle sensing information is processed by adaptive offset filtering. Transient slip anomaly detection is performed on multiple wheel combinations and hydroplaning indicators of various dimensions are generated. When all hydroplaning indicators are set at the same time, it is determined that there is a hydroplaning condition of the vehicle and a hydroplaning detection indicator is output. Based on the vehicle motion parameters corresponding to the filtered real-time vehicle sensing information and the pre-trained yaw rate model, the first yaw rate and the second yaw rate are calculated. If the difference between the first yaw rate and the second yaw rate is greater than the preset total tolerance of yaw rate, the existence of vehicle side slope condition is determined and a side slope detection mark is output. The yaw rate and lateral acceleration corresponding to the real-time vehicle sensing information are subjected to feature conversion and filtering to obtain the relative value of the yaw rate and the corresponding vehicle status signal. When the vehicle status signal meets the preset trigger flag combination conditions, the existence of the vehicle groove condition is determined and the groove detection flag is output. The vehicle's real-time sensor information is used to filter wheel speed data and calculate speed difference and acceleration difference. Speed ​​difference counters and acceleration difference counters are obtained by statistical analysis. When the speed difference counters and / or acceleration difference counters exceed preset thresholds, a vehicle roll condition is determined and a roll detection flag is output.

[0011] In one embodiment, the step of processing the adaptive slip ratio offset corresponding to the real-time vehicle sensing information through adaptive offset filtering, performing transient slip anomaly detection on multiple wheel combinations and generating hydroplaning indicators in various dimensions, and determining the existence of vehicle hydroplaning condition and outputting a hydroplaning detection indicator when all hydroplaning indicators are simultaneously set includes: The wheel speed difference and the original slip ratio are calculated from the real-time sensor information of the vehicle to obtain the relative slip ratio calculation value; Calculate the adaptive slip ratio offset based on the relative slip ratio calculation value and the preset forgetting factor; The net slip ratio is calculated based on the adaptive slip ratio offset and the relative slip ratio calculation value. Based on the net slip ratio and the preset slip ratio threshold, transient slip anomaly detection is performed on multiple types of wheel combinations to obtain the anomaly judgment results for each type of wheel combination. If the preset vehicle speed and steering wheel angle thresholds are met and the abnormal judgment result of all wheel combinations is transient slip abnormality, the vehicle is determined to be in hydroplaning condition and a hydroplaning detection mark is output.

[0012] In one embodiment, the step of calculating a first yaw rate and a second yaw rate based on the vehicle motion parameters corresponding to the filtered real-time vehicle sensing information and the pre-trained yaw rate model, and determining the existence of a vehicle side slope condition and outputting a side slope detection flag when the difference between the first yaw rate and the second yaw rate is greater than the preset total tolerance of yaw rate, includes: Based on the pre-trained yaw rate model, combined with the vehicle motion parameters corresponding to the preset weight factors and the real-time vehicle sensing information after filtering, the first yaw rate is calculated. The second yaw rate is obtained based on the sensor information; Calculate the absolute difference between the first yaw rate and the second yaw rate. When the absolute difference is greater than the preset total tolerance of yaw rate, determine that the vehicle is in a side slope condition and output a side slope detection sign.

[0013] In one embodiment, the step of performing feature conversion and filtering on the yaw rate and lateral acceleration corresponding to the real-time vehicle sensing information to obtain the relative value of the yaw rate and the corresponding vehicle state signal, and determining the existence of a vehicle groove condition and outputting a groove detection flag when the vehicle state signal meets the preset trigger flag combination conditions, includes: The theoretical yaw rate is obtained by converting the lateral acceleration corresponding to the real-time vehicle sensing information to a dimension, and the basic characteristic difference is obtained by combining the measured yaw rate. The basic feature difference is subjected to dynamic averaging filtering to obtain the filtered feature difference; A steady-state offset benchmark is constructed using a pre-defined nonlinear state observer, and the relative value of the yaw rate is calculated by combining the filtered feature difference. The theoretical yaw rate and the measured yaw rate are subjected to a first-order low-pass filter to obtain a multi-dimensional vehicle state signal. Based on the comparison between the vehicle status signals of each dimension and the corresponding preset status thresholds, multiple groove recognition trigger signs are generated. When the multiple trigger flags meet the preset trigger flag combination conditions, the groove detection timer is started. If the groove detection timer duration exceeds the preset time threshold, it is determined that there is a vehicle groove condition and a groove detection flag is output.

[0014] In one embodiment, the step of filtering the wheel speed data corresponding to the real-time vehicle sensing information and calculating the speed difference and acceleration difference, respectively obtaining a speed difference counter and an acceleration difference counter, and determining the existence of a vehicle roll condition and outputting a roll detection flag when the speed difference counter and / or acceleration difference counter exceed a preset threshold includes: The wheel speed data corresponding to the real-time vehicle sensor information is filtered to calculate the speed difference and acceleration difference. The speed difference is compared with a preset fixed threshold. When the speed difference exceeds the preset fixed threshold, the speed difference counter is incremented. A dynamic threshold is calculated based on the proportion of front axle drive torque, and the acceleration difference is compared with the dynamic threshold. When the acceleration difference exceeds the dynamic threshold, the acceleration difference counter is incremented. The wheel speed difference and acceleration difference of the front axle, rear axle, and front-rear axle combination wheels are detected, and corresponding roll markers are generated. When the speed difference counter and / or acceleration difference counter exceed a preset threshold and the roll flag is valid, a vehicle roll condition is determined and a roll detection flag is output.

[0015] In one embodiment, the step of making a comprehensive judgment and control decision on crosswinds by combining the crosswind force and the non-crosswind target interference conditions includes: Determine whether the steering wheel angle range, vehicle speed range, yaw rate range, and lateral acceleration range corresponding to the real-time vehicle sensing information conform to the preset operating area, and obtain the operating area mark; Based on the crosswind force, the level is determined by combining the preset wind force classification threshold to obtain the corresponding crosswind force level. Inspect the condition of water skid detection marks, groove detection marks, side tilt detection marks, and side slope detection marks; If any flag is set, a crosswind prohibition sign will be output. Based on the crosswind force level, crosswind no-intervention sign, and operation area sign, the corresponding crosswind control mode is output, which includes waiting mode, intervention mode, and exit mode.

[0016] Furthermore, to achieve the above objectives, this application also proposes a crosswind monitoring and control device, which includes: The sensor acquisition module is used to collect real-time sensor information of the vehicle; The crosswind calculation module is used to calculate the crosswind force based on the real-time sensor information of the vehicle. The interference condition identification module is used to determine the interference conditions of non-crosswind targets through a preset dynamic calculation strategy. The crosswind arbitration control module is used to make comprehensive judgments and control decisions on crosswinds by combining the crosswind force and the non-crosswind target interference conditions.

[0017] In addition, to achieve the above objectives, this application also proposes a crosswind monitoring and control 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 monitoring and control method described above.

[0018] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the crosswind measurement and control method described above.

[0019] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the crosswind measurement and control method described above.

[0020] This application proposes a crosswind measurement and control method, device, equipment, and storage medium. The method includes: collecting real-time vehicle sensor information; calculating crosswind force based on the real-time vehicle sensor information; determining non-crosswind target interference conditions through a preset dynamic calculation strategy; and making comprehensive crosswind judgment and control decisions by combining the crosswind force and the non-crosswind target interference conditions. This solution, by collecting real-time vehicle sensor information to calculate crosswind force, identifying non-crosswind target interference conditions according to a dynamic calculation strategy, and linking them with comprehensive control decisions, can accurately distinguish crosswind disturbances from other vehicle interference conditions such as road surface disturbances under complex road environments and weather conditions. Attached Figure Description

[0021] 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.

[0022] 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.

[0023] Figure 1 This is a flowchart illustrating an embodiment of the crosswind monitoring and control method of this application. Figure 2 A schematic diagram of the crosswind force calculation provided in Embodiment 1 of this application; Figure 3 This is a schematic diagram of the process for hydroplaning condition testing provided in Embodiment 1 of this application; Figure 4 This is a schematic diagram of the slope condition detection process provided in Embodiment 1 of this application; Figure 5 This is a schematic diagram of the process for detecting groove working conditions provided in Embodiment 1 of this application; Figure 6 This is a schematic diagram of the roll condition detection process provided in Embodiment 1 of this application; Figure 7 This is a detailed schematic diagram of crosswind comprehensive judgment and control provided in Embodiment 1 of this application; Figure 8 This is a flowchart illustrating Embodiment 2 of the crosswind monitoring and control method of this application. Figure 9 This is a flowchart illustrating Embodiment 3 of the crosswind measurement and control method of this application; Figure 10 This is a flowchart illustrating Embodiment 4 of the crosswind measurement and control method of this application; Figure 11 This is a flowchart illustrating Embodiment 5 of the crosswind measurement and control method of this application. Figure 12 This is a schematic diagram of the overall architecture of the crosswind detection system provided in Embodiment 1 of this application; Figure 13 This is a schematic diagram of the module structure of the crosswind monitoring and control device according to an embodiment of this application; Figure 14 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the crosswind measurement and control method in this application embodiment.

[0024] 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

[0025] 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.

[0026] 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.

[0027] The main solution of this application embodiment is: to collect real-time vehicle sensing information; to calculate crosswind force based on the real-time vehicle sensing information; to determine non-crosswind target interference conditions through a preset dynamic calculation strategy; and to make a comprehensive judgment and control decision on crosswind by combining the crosswind force and the non-crosswind target interference conditions.

[0028] In this embodiment, for ease of description, the vehicle crosswind main control unit will be used as the execution subject in the following description.

[0029] The existing technology has several obvious drawbacks: First, determining the presence of crosswind conditions solely through calculation of yaw rate deviation has significant robustness issues, as it cannot effectively exclude completely similar vehicle yaw behavior caused by other road conditions such as hydroplaning, side slopes, or ruts. Second, relying on information such as specific geographical location and macroscopic wind speed to detect crosswinds may not meet the stringent real-time requirements of chassis stability control systems and the functional safety requirements of modern automobiles. Furthermore, this detection method has significant limitations in terms of operating conditions, failing to cover all possible crosswind scenarios. Additionally, some solutions may require the addition of extra environmental sensors or positioning equipment, increasing system cost and integration complexity.

[0030] This application provides a solution that fully utilizes existing standard sensor signals, combined with a precise vehicle dynamics model and multi-condition exclusion strategy, to achieve highly robust and accurate identification of crosswind conditions without increasing hardware costs. This significantly improves the vehicle's stability control capability under crosswind conditions and fully meets the functional safety and real-time requirements of modern chassis stability control.

[0031] 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 tablet computer, personal computer, or mobile phone, or an in-vehicle controller or in-vehicle terminal device capable of performing the above functions. The following description uses an in-vehicle controller as an example to illustrate this embodiment and the subsequent embodiments.

[0032] Based on this, the embodiments of this application provide a crosswind measurement and control method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the crosswind measurement and control method of this application.

[0033] In this embodiment, the crosswind monitoring and control method includes steps S10 to S40: Step S10: Collect real-time vehicle sensing information output by the sensors; In this embodiment, the real-time vehicle sensing information is collected in real time by a standard sensor group mounted on the vehicle and transmitted to the crosswind main control unit via hardwire or CAN bus. The sensor group includes four wheel speed sensors, a steering wheel angle sensor, and an inertial measurement unit (IMU). The four wheel speed sensors correspond to the left front, right front, left rear, and right rear wheels, respectively, and are used to collect real-time wheel speed signals for each wheel. The steering wheel angle sensor is used to collect the real-time steering wheel angle signal. The IMU integrates a gyroscope and an accelerometer, and can simultaneously collect the vehicle's yaw rate, longitudinal acceleration, and lateral acceleration signals. The real-time vehicle sensing information includes, but is not limited to, the four-wheel speed sensor signals, such as the real-time wheel speed signals of the left front wheel (FL), right front wheel (FR), left rear wheel (RL), and right rear wheel (RR); the steering wheel angle sensor signals, such as the real-time steering wheel angle acquisition signal; and the IMU integrating sensor signals, such as yaw rate, longitudinal acceleration, and lateral acceleration signals.

[0034] Through the above steps, reliable and complete raw data support is provided for subsequent crosswind force calculation, identification of various non-crosswind interference conditions, and crosswind comprehensive control decisions by using only onboard standard vehicle real-time sensor information such as IMU, wheel speed sensors, and steering angle sensors. There is no need to add additional high-precision sensing equipment, which effectively reduces the system hardware cost and the complexity of the vehicle configuration. At the same time, relying on the high timeliness and high reliability of standard sensors, the real-time performance and accuracy of subsequent crosswind detection and control logic are guaranteed.

[0035] Step S20: Calculate the crosswind force based on the real-time sensor information of the vehicle; It should be noted that the crosswind force refers to the external lateral wind interference force experienced by the vehicle during its operation. In this embodiment, the crosswind interference torque is calculated by using the torque residual identification method based on the principle of yaw moment balance of the vehicle around the Z-axis, and then the magnitude of the crosswind force is calculated.

[0036] like Figure 2As shown, the core detection logic in this embodiment is based on the torque balance equation around the z-axis during vehicle movement, and detects external disturbances by calculating the residual of the torque balance. The inertial torque that generates yaw acceleration during vehicle movement consists of multiple parts, mainly including the tire torque generated by vehicle steering motion and tire elastic deformation, as well as external disturbance torques caused by external environments such as crosswinds. Under conditions without external disturbances, the vehicle's tire torque and inertial torque are in balance, and the external disturbance torque approaches zero. When a torque imbalance occurs that cannot be explained by known motion factors such as vehicle steering and tire deformation, the torque residual is the external disturbance torque caused by crosswinds. At the same time, the intervention torque generated by the vehicle system's active control needs to be eliminated during the torque balance calculation to avoid interference from the vehicle's own control torque on the calculation of crosswind disturbance torque.

[0037] Based on the above principles, the torque balance equation used in this embodiment is: Disturbance torque = Inertial torque Tire torque The active intervention torque is used to accurately calculate the real-time crosswind interference torque of the vehicle through this equation, and the corresponding crosswind force is obtained by further calculation based on the interference torque.

[0038] Optionally, step S20 may include steps S21 to S25: Step S21: Calculate the inertial torque based on the real-time vehicle sensing information using a pre-trained vehicle yaw inertial model. It should be noted that the vehicle yaw inertia model is Mz,Jz=Jz dYawRate, where Jz is the vehicle's yaw moment of inertia, which is an inherent structural parameter of the vehicle; dYawRate is the rate of change of yaw acceleration, which is obtained by measuring the difference in yaw angular velocity using an IMU. This inertial torque value is used to characterize the basic torque required to maintain the vehicle's current yaw rotational motion.

[0039] In this embodiment, based on the real-time yaw rate signal collected by the IMU sensor, the yaw rate of change is obtained by solving the differential algorithm. Combined with the yaw moment of inertia calibrated by the vehicle, it is substituted into the vehicle yaw inertia model to calculate the yaw inertia torque during the vehicle's driving process in real time, providing core basic parameters for subsequent torque balance residual calculation.

[0040] Step S22: Calculate the tire torque using a preset two-degree-of-freedom differential equation of vehicle motion; In this embodiment, based on the two-degree-of-freedom vehicle motion differential equations, and assuming the vehicle is traveling at a constant speed and in a steady-state response, the equations are solved simultaneously by combining the vehicle's real-time motion parameters and inherent structural parameters. The two-degree-of-freedom vehicle steady-state motion differential equations used are as follows:

[0041]

[0042] in, For front axle lateral stiffness; Rear axle lateral stiffness; For lateral velocity; Longitudinal velocity; The front wheel steering angle is m; the vehicle mass is m. b is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle. ω represents the yaw rate.

[0043] By combining the above equations, we can derive the steady-state yaw gain formula:

[0044] Where L is the wheelbase.

[0045] Further introduction of vehicle stability factors Complete the simplified derivation of the formula and solve for the theoretical front wheel speed of the vehicle under steady-state conditions. Where R is the steady-state turning radius. This is lateral acceleration.

[0046] By combining the calculated theoretical front wheel steering angle with the actual wheel steering angle δact collected by the sensor, the equivalent front wheel steering angle δd caused by crosswind interference can be obtained. In the absence of external interference, the equivalent front wheel steering angle approaches zero. Finally, based on the equivalent front wheel steering angle, the yaw moment Mz,Angle generated around the vehicle's center of gravity is calculated, thus obtaining the real-time tire torque, which characterizes the basic yaw moment generated by vehicle steering and tire deformation.

[0047] Step S23: Obtain braking control parameters and power torque regulation parameters, and convert them into equivalent yaw moment; In this embodiment, the vehicle's active intervention torque mainly originates from the active braking torque of the vehicle stability control system and the torque regulation torque of the power torque vector system, including braking yaw torque Mz,Brk and power regulation yaw torque Mz,PT. The system reads the vehicle chassis braking pressure parameters and engine torque intervention regulation parameters in real time via the CAN bus, matches them with a preset torque conversion calibration algorithm, and converts the wheel-end braking pressure difference and the power asymmetric torque distribution into the vehicle's equivalent active intervention yaw torque around its center of gravity. The system fully collects the torque output generated by the vehicle's own active control, which is used to subsequently eliminate the interference of its own control torque on the calculation of crosswind interference torque, ensuring the accuracy of crosswind force calculation.

[0048] Step S24: Combine the inertial torque, tire torque and the equivalent yaw torque, and calculate the crosswind interference torque using a preset torque balance equation. In this embodiment, the crosswind disturbance moment is obtained by solving the moment balance equation, combined with the inertial moment, tire moment, and equivalent yaw moment. The specific calculation formula is as follows: Mz,Disturb= Mz,Jz-Mz,Angle-Mz,Brk-Mz,PT Where Mz,J is the vehicle yaw moment; Mz,Angle is the tire moment corresponding to tire deformation and steering motion; Mz,Brk is the equivalent yaw moment of the braking system; and Mz,PT is the equivalent yaw moment of the power torque regulation system.

[0049] Step S25: Using a pre-calibrated wind dynamic model and combining the distance parameters between the wind pressure center and the vehicle's center of gravity, the crosswind force is calculated based on the crosswind interference moment.

[0050] It should be noted that the wind dynamic model is obtained by fitting the actual vehicle wind tunnel test and the whole vehicle calibration test. It is calibrated offline based on the inherent vehicle parameters such as vehicle shape, wind-receiving area, and wind pressure action position, and can accurately characterize the correspondence between crosswind interference torque and lateral wind force.

[0051] In this embodiment, the crosswind disturbance torque includes the yaw torque component generated by the crosswind. The crosswind force is calculated based on the pre-calibrated wind dynamic model F_wind = Mz_disturb / d, where d is the horizontal distance from the wind pressure center to the vehicle's center of mass.

[0052] Through the above steps, based on the vehicle's two-degree-of-freedom dynamics model and the torque balance principle, internal disturbance torques such as vehicle inertial motion, tire deformation, and active control are eliminated layer by layer. By calibrating the aeolian model, the accurate conversion of crosswind disturbance torques to crosswind force is achieved, completing the entire solution process from sensor raw signal acquisition and dynamic torque calculation to crosswind force quantification. Compared to traditional lookup table methods, this dynamics calculation method has stronger environmental adaptability and higher identification accuracy. It can effectively adapt to different vehicle speeds and steering states, accurately quantifying real-time crosswind force levels, and providing high-precision input for subsequent multi-condition interference elimination, crosswind control mode arbitration, and torque distribution control.

[0053] Step S30: Determine the non-crosswind target interference conditions through a preset dynamic calculation strategy; It should be noted that the non-crosswind target interference conditions include, but are not limited to, vehicle hydroplaning conditions, vehicle side slope conditions, vehicle groove conditions, and vehicle tilting conditions.

[0054] Understandably, complex road conditions and attitude disturbances such as hydroplaning, side slopes, ruts, and rolls can cause imbalances in vehicle yaw moment, abrupt changes in yaw rate and lateral acceleration, and produce vehicle dynamic responses highly similar to crosswind disturbances. These can easily be confused with crosswind disturbances, leading to errors in crosswind moment identification. Therefore, step S30 is executed to analyze the vehicle dynamic performance characteristics of various disturbance conditions and design targeted dynamic algorithm strategies to identify and exclude conditions such as hydroplaning, side slopes, rolls, and ruts. This can overcome the robustness problem of single crosswind dynamic identification and avoid misidentifying other disturbance conditions as crosswind conditions, thus preventing misidentification and misintervention.

[0055] Optionally, step S30 may include steps S31 to S34: Step S31: Adaptive offset filtering technology is used to perform adaptive slip ratio offset calculation and multi-wheel slip detection, and combined with the multi-hydroplaning sign linkage condition to determine the vehicle hydroplaning condition and output hydroplaning detection sign. It should be noted that the adaptive slip ratio offset refers to the baseline slip ratio calculated based on the vehicle's real-time longitudinal speed and four-wheel wheel speed sensor signals, combined with the vehicle's steady-state driving characteristics and typical road surface slip deviation features, and dynamically corrected using an adaptive offset filtering algorithm. This offset can adaptively adjust in real time according to vehicle speed and driving conditions, effectively offsetting the conventional slip errors caused by smooth straight driving, gentle steering, and minor road surface bumps, and accurately highlighting the transient slip anomaly characteristics caused by low-adhesion water accumulation on the road surface.

[0056] The hydroplaning indicator is a condition identification indicator generated by a transient slip anomaly threshold discrimination mechanism for the multi-dimensional wheel combination state of a vehicle. It covers multi-dimensional slip detection indicators such as single wheel, front and rear axle wheels, left and right wheels, and diagonal wheels, and is used to verify whether the vehicle has tire slippage or hydroplaning tendency from different wheel force slip dimensions.

[0057] like Figure 3 As shown, the core logic of hydroplaning condition recognition in this embodiment is a multi-dimensional wheel combination slip joint verification. It relies on real-time wheel speed information of the four wheels collected by the vehicle's wheel speed sensors, and calculates the wheel slip ratio by combining it with the longitudinal vehicle speed. An adaptive offset filter is used to eliminate steady-state offset errors in the slip ratio under normal driving conditions, and transient and abrupt wheel slip anomalies caused by hydroplaning are extracted. By performing slip anomaly detection on multiple wheel combinations one by one, corresponding hydroplaning markers are generated. A multi-marker joint positioning judgment logic is adopted to avoid misjudgments caused by single wheel slip errors or minor road bumps. Ultimately, it accurately identifies hydroplaning conditions during high-speed vehicle travel, effectively distinguishing conditions without crosswind interference.

[0058] Step S32: Use the filtered vehicle motion parameters and the pre-trained yaw rate model to obtain the yaw rate. By comparing the difference in yaw rate with the preset total tolerance of yaw rate, determine whether the vehicle is in a side slope condition and output a side slope detection flag. It should be noted that the yaw rate includes a first yaw rate and a second yaw rate. The first yaw rate is the theoretical yaw rate calculated based on the vehicle dynamics model, and the second yaw rate is the actual yaw rate measured by the sensor.

[0059] The side slope detection mark is a condition indicator used to characterize the presence of lateral slope interference on the road surface where the vehicle is traveling. When the vehicle is traveling on a road surface with a single-sided inclination, the lateral component of gravity will continuously induce the vehicle to produce steady-state yaw deviation. This abnormal deviation cannot be explained by conventional steering or road curvature interference. The mark is generated by judging the excess of the residual between the theoretical value and the measured value of the yaw rate, and is used to distinguish side slope interference from crosswind and instantaneous impact interference from the road surface.

[0060] like Figure 4 As shown, the vehicle side slope condition recognition in this embodiment is mainly based on the residual comparison logic between the theoretical yaw rate and the actual yaw rate. First, the raw sensor information such as vehicle speed, steering wheel angle, and lateral acceleration is preprocessed with a low-pass filter to remove high-frequency noise and instantaneous bump interference, obtaining stable and effective vehicle motion parameters, namely smooth lateral acceleration and its rate of change. The filtered vehicle motion parameters are then input into a pre-trained vehicle yaw rate model to fit and solve for the first yaw rate under stable driving conditions, i.e., the theoretical interference-free yaw response; simultaneously, the second yaw rate measured by the IMU (Inertial Measurement Unit) is read. By comparing the deviation amplitude between the theoretical and measured yaw rates in real time, when the difference continuously exceeds the preset total tolerance of the yaw rate, it is determined that the vehicle is currently on a road surface with a continuous lateral slope, indicating a side slope interference condition. A side slope detection flag is effectively output, achieving accurate identification of side slope-type steady-state road surface interference.

[0061] Step S33: Perform feature conversion and filtering on the yaw rate and lateral acceleration corresponding to the real-time vehicle sensing information to obtain the relative value of the yaw rate and the corresponding vehicle status signal. If the vehicle status signal meets the preset trigger flag combination conditions, determine the existence of the vehicle groove condition and output the groove detection flag. It should be noted that the trigger flag combination is specifically designed for the transient, abrupt, and short-term recovery dynamic characteristics of road surface groove impact conditions. It is a preset multi-dimensional and multi-condition joint triggering logic, which is formed by combining multiple vehicle status signal threshold exceeding limits and status consistency verification.

[0062] The groove detection mark is a special condition mark used to accurately identify the instantaneous impact interference of road grooves and potholes encountered by vehicles during driving. It is set only when multiple preset trigger marks are simultaneously satisfied and the timing characteristics match the groove impact characteristics. It is used to eliminate the road groove interference source during crosswind identification and avoid crosswind misidentification and misintervention.

[0063] like Figure 5 As shown, this embodiment addresses the dynamic identification logic for road surface groove conditions. First, it collects real-time vehicle sensor information, including yaw rate and lateral acceleration during vehicle movement. The raw sensor signals undergo unified feature conversion and adaptive filtering to remove high-frequency noise from sensors, minor road surface bumps, and steady-state road deviation interference. This process yields a stable relative yaw rate and multi-dimensional vehicle state signals. Based on this, and according to preset trigger flag combinations, the processed vehicle state signals undergo multi-dimensional threshold verification and joint judgment. When the vehicle state signal perfectly matches the preset trigger conditions, it is determined that the vehicle is currently experiencing road surface groove interference, and a groove detection flag is stably output. This achieves accurate identification and effective elimination of non-crosswind instantaneous impact interference conditions such as road surface grooves.

[0064] Step S34: Filter the wheel speed data corresponding to the real-time vehicle sensor information and calculate the speed difference and acceleration difference. Obtain the speed difference counter and acceleration difference counter respectively. When the speed difference counter and / or acceleration difference counter exceed the preset threshold, determine that there is a vehicle roll condition and output a roll detection flag.

[0065] It should be noted that the roll detection mark is a special condition mark used to identify vehicle roll posture disturbances caused by road conditions such as road undulations, longitudinal ruts, and differences in road height between the left and right sides. Vehicle roll conditions will cause lateral displacement and slight yaw disturbances of the vehicle body. The dynamic response is highly similar to the characteristics of crosswind disturbances. This mark is used to distinguish between road roll posture disturbances and actual crosswind disturbances, avoiding false triggering of crosswind algorithms and unnecessary chassis intervention under roll conditions.

[0066] like Figure 6As shown, this embodiment constructs a roll recognition program to accurately distinguish and identify vehicle roll conditions. During high-speed vehicle travel, road surface undulations and uneven longitudinal ruts can easily cause inconsistent heights between the left and right sides of the vehicle, triggering instantaneous roll and generating lateral impacts and weak yaw responses. These responses highly overlap with the dynamic characteristics of crosswind disturbances, easily interfering with crosswind identification. This embodiment uses four-wheel wheel speed sensor data as the core input. First, the raw wheel speed data is filtered and preprocessed to remove high-frequency noise and minor road bumps, obtaining stable and reliable effective wheel speed signals. Based on the filtered wheel speed data, the wheel speed difference and wheel speed acceleration difference between the left and right wheels are calculated in real time. A counter mechanism is used to continuously count the duration of speed difference exceeding limits and acceleration difference exceeding limits, constructing a dual detection mechanism for speed difference and acceleration difference. When either or both of the speed difference counter and acceleration difference counter exceed their respective preset thresholds, it is determined that the vehicle is currently experiencing a body roll caused by road undulations or ruts. The vehicle then outputs a stable roll detection flag, effectively identifying and eliminating non-crosswind interference conditions that cause vehicle roll, further enhancing the anti-interference capability and adaptability of the crosswind recognition algorithm.

[0067] Through the methods described above, this embodiment addresses four typical road surface and vehicle posture interference conditions: hydroplaning, side slope, groove, and side tilt. It matches differentiated dynamic identification strategies to each condition. By employing multi-dimensional feature verification, multi-marker joint judgment, and dual-parameter redundant detection mechanisms, it accurately distinguishes between various non-crosswind interferences and real crosswind disturbances. It eliminates homogeneous dynamic interferences at the algorithm level, completely resolving the technical pain point of crosswind detection misidentification under complex road conditions. This provides a reliable basis for subsequent comprehensive crosswind judgment and precise control decisions.

[0068] Step S40: Combine the crosswind force and the non-crosswind target interference conditions to make a comprehensive judgment and control decision on the crosswind.

[0069] Understandably, relying solely on crosswind force is prone to problems such as misintervention, over-intervention, or ineffective intervention under complex and combined operating conditions. Furthermore, vehicles are only adapted to crosswind correction control within specific driving state ranges, and intervention in other operating conditions can actually worsen driving stability. Therefore, step S40 integrates driving operation area verification, shielding of multiple types of non-crosswind interference conditions, quantitative determination of crosswind force level, and multi-mode state machine switching logic. This avoids misintervention, ineffective intervention, and control failure caused by operating condition mismatch due to non-crosswind interference under complex road conditions, thereby achieving closed-loop crosswind stability control with accurate crosswind condition identification, precise control of intervention timing, and a smooth and controllable control process.

[0070] like Figure 7As shown, this embodiment first determines whether the vehicle is in an operable area by verifying multi-dimensional vehicle motion parameters. Then, it combines four types of non-crosswind interference condition indicators to complete the interference shielding determination, while quantifying the crosswind force level. Based on multi-dimensional condition linkage arbitration, it achieves precise switching between waiting mode, intervention mode, and exit mode. In effective intervention mode, it completes the stepped distribution of yaw correction torque according to the available status and performance limit of the vehicle actuators, and performs closed-loop feedback correction in combination with the real-time motion status of the vehicle, ultimately achieving smooth correction of the vehicle's trajectory and attitude stability control under crosswind disturbance.

[0071] Optionally, step S40 may include steps S41 to S45: Step S41: Determine whether the steering wheel angle range, vehicle speed range, yaw rate range and lateral acceleration range corresponding to the real-time vehicle sensing information conform to the preset operation area, and obtain the operation area mark. It should be noted that the operating area refers to the preset stable driving range of the vehicle adapted to crosswind correction control. By setting upper and lower limit thresholds for four core motion parameters, namely vehicle speed, steering wheel angle, yaw rate, and lateral acceleration, the effective working conditions in which the vehicle can carry out crosswind intervention are defined, so as to avoid working conditions that are not suitable for crosswind intervention, such as extreme steering, low-speed driving, and large attitude fluctuations.

[0072] In this embodiment, based on real-time vehicle sensor information, the real-time vehicle speed, steering wheel angle, yaw rate, and lateral acceleration parameters are extracted, and each parameter is compared and verified against its corresponding preset threshold range. The vehicle is determined to be in a controllable operating area and the operating area flag is set only if all four motion parameters fall within their respective preset reasonable ranges; otherwise, the intervention access conditions are not met, and the operating area flag is not set.

[0073] Step S42: Based on the crosswind force, the level is determined by combining the preset wind force classification threshold to obtain the corresponding crosswind force level. In this embodiment, multiple gradient wind force classification thresholds are preset, corresponding to different wind force levels such as no crosswind, weak crosswind, moderate crosswind, and strong crosswind. By comparing the real-time crosswind force with each threshold level, the crosswind force level is quantitatively classified, providing a classification basis for subsequent intervention mode triggering and intervention torque amplitude matching.

[0074] Step S43: Check the status of the water skid detection mark, groove detection mark, tilt detection mark, and side slope detection mark; In this embodiment, by reading the status of four types of non-crosswind interference condition signs in real time, the positioning and resetting status of the hydroplaning sign, groove sign, tilt sign, and side slope sign are monitored throughout the process. The system provides real-time feedback on whether the vehicle is experiencing non-crosswind interference conditions such as road slippage, road impact, vehicle body tilt, or road side slope, providing direct condition basis for crosswind intervention authority shielding.

[0075] Step S44: If any flag status is set, output a crosswind prohibition sign. In this embodiment, if any one of the following markers—hydroplaning detection, groove detection, roll detection, or side slope detection—is in the active position, it is determined that the current vehicle dynamics disturbance is dominated by non-crosswind road surface or attitude conditions, and the crosswind force identification result has interference bias. At this time, the system immediately sets the crosswind prohibition intervention sign and locks the crosswind active intervention function to avoid erroneous triggering of crosswind correction control under non-crosswind interference conditions, thus preventing vehicle attitude instability caused by erroneous intervention.

[0076] Step S45: Based on the crosswind force level, crosswind no-intervention sign, and operation area sign, output the corresponding crosswind control mode.

[0077] It should be noted that the crosswind control modes include, but are not limited to, waiting mode, intervention mode, and exit mode.

[0078] In this embodiment, the three-mode switching is completed based on multi-condition linkage arbitration logic: when the crosswind force level exceeds the preset level threshold, the operation area indicator is set to allow operation, and when the crosswind prohibition intervention is not set, the system determines that all intervention access conditions are met and enters the crosswind intervention mode; if the crosswind intervention prohibition indicator is set or the operation area does not meet the requirements, the system enters the waiting mode; if the crosswind force level decreases, causing the requested intervention torque to gradually decrease or the intervention exceeds a certain time, the system enters the exit mode from the intervention mode and selects an appropriate intervention torque exit slope so that the system control can exit smoothly.

[0079] When the system is in intervention mode, the required yaw correction torque needs to be calculated. First, based on the available actuators and their capabilities, the yaw correction torque is distributed sequentially to the available actuators, influencing the vehicle's motion. During this process, the vehicle's yaw rate and lateral acceleration are monitored simultaneously, and feedback control is applied to the deviation between the target state and the actual state, enabling the vehicle to accurately return to its original trajectory.

[0080] Through the above steps, this embodiment realizes comprehensive decision-making control of the entire process, including crosswind force analysis, interference condition shielding, operating area access, multi-mode state switching, torque collaborative distribution, and closed-loop feedback correction. It effectively distinguishes the boundaries of operating conditions that can be intervened, cannot be intervened, and exit intervention, and completely solves the problems of traditional crosswind control such as easy false triggering, abrupt intervention, sudden exit, and poor adaptability to complex operating conditions. It significantly improves the driving stability, smoothness, and trajectory following accuracy of vehicles under high-speed crosswind conditions.

[0081] The above-described embodiments involve collecting real-time vehicle sensor information; calculating crosswind force based on this information; identifying non-crosswind target interference conditions using a preset dynamic calculation strategy; and combining the crosswind force and the non-crosswind target interference conditions for comprehensive crosswind judgment and control decisions. This solution, by collecting real-time vehicle sensor information to calculate crosswind force, identifying non-crosswind target interference conditions based on a dynamic calculation strategy, and linking these with comprehensive control decisions, can accurately distinguish between crosswind disturbances and other vehicle-related interference conditions, such as road surface disturbances, under complex road environments and weather conditions.

[0082] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 8 Further elaborating on step S31, the crosswind monitoring and control method also includes steps S311 to S316: Step S311: Extract four-wheel wheel speed data based on the real-time vehicle sensing information, and extract the left and right front wheel speeds from the four-wheel wheel speed data for adaptive offset filtering. In this embodiment, four-wheel wheel speed data is first extracted from the collected real-time vehicle sensor information, and then the front left and right wheel speed signals are extracted from the four-wheel wheel speed data. Adaptive offset filtering is then applied to the front left and right wheel speed signals. This scheme uses a second-order Butterworth filter to optimize the noise reduction of the front left and right wheel speed signals. The filter is configured with two sets of characteristic parameters, A1 and A2, to control the smoothness of the filtering and the response delay characteristics. Larger values ​​for parameters A1 and A2 result in better smoothing of the front left and right wheel speed signals, effectively filtering out high-frequency noise and instantaneous jitter interference, but slightly increasing the signal response delay. Smaller parameter values ​​result in higher sensitivity of the front left and right wheel speed signals to real-time changes, but with limited noise reduction capability. Therefore, this embodiment combines simulation calibration and multi-scenario real-vehicle testing to adapt and calibrate the filter coefficients. While ensuring no significant distortion of the wheel speed signal and rapid response to real wheel speed changes, it maximizes the filtering of high-frequency noise, providing a clean and stable original wheel speed signal for subsequent accurate calculation of wheel speed difference and slip ratio.

[0083] Step S312: Calculate the wheel speed difference based on the wheel speeds of the left and right front wheels after adaptive offset filtering, and combine it with the original slip ratio to obtain the calculated value of the relative slip ratio; After filtering the front wheel speed signals, the wheel speed difference and the original slip ratio are further standardized and calculated. Taking the front axle wheels as an example, the left front wheel speed vFL and the right front wheel speed vFR are collected, and the front wheel speed difference is calculated using the formula v_diff=vFL-vFR. To avoid calculation anomalies caused by the denominator approaching zero at low speeds, a maximum value limiting logic is introduced in the slip ratio calculation process, using the formula sl_FA=v_diff. 2 / max(vFL+vFR,1) is used to solve for the original relative slip ratio of the front wheels. The calculation logic for the wheel speed difference and relative slip ratio of the left and right wheels of the rear axle, the front and rear axle combinations, and the diagonal wheel combinations is consistent with that of the front wheels. Through multi-dimensional wheel combination traversal calculation, the final output is the relative slip ratio calculation value λ corresponding to multiple sets of wheels.

[0084] Step S313: Calculate the adaptive slip ratio offset based on the calculated relative slip ratio and the preset forgetting factor; In this embodiment, an iterative update algorithm with a forgetting factor is used to achieve dynamic adaptive learning of the adaptive slip ratio offset. The specific iterative calculation formula is: λOffset(k) = α λOffset(k-1) + (1-α) λ(k), where α is a preset forgetting factor used to control the update speed and tracking sensitivity of the offset; λOffset(k) is the adaptive slip rate offset of the previous time step, and λ(k) is the real-time relative slip rate calculated at the current time step.

[0085] It should be noted that the parameter characteristics of the forgetting factor determine that this algorithm has the learning characteristics of slow update and strong resistance to disturbances. Since the value of α is close to 1, the weight of historical offset is extremely high during the iteration process, while the correction weight of the current real-time slip ratio is extremely small. This means that the adaptive slip ratio offset can only slowly follow the steady-state slip characteristics of the road surface for update and learning. When the vehicle is driving smoothly on a normal high-adhesion road surface, the adaptive offset can gradually converge and stabilize near the inherent small slip ratio of the road surface, accurately fitting the steady-state slip deviation under normal driving conditions. When the road surface state changes abruptly, such as when the vehicle instantly enters a water film road surface from a high-adhesion road surface, the wheel speed signal will change drastically, and the slip ratio will jump sharply. However, the adaptive offset is constrained by the forgetting factor and cannot respond quickly to the instantaneous change. It still maintains the steady-state offset value before the change and will not jump synchronously with the transient slip anomaly, thus achieving effective separation of the steady-state baseline and transient abnormal features.

[0086] Step S314: Calculate the net slip ratio based on the adaptive slip ratio offset and the relative slip ratio calculation value; In this embodiment, the difference between the calculated relative slip ratio and the adaptive slip ratio offset is calculated to remove low-frequency interference components such as road surface steady-state slip deviation and normal driving slip offset, and the net slip ratio that can truly reflect the degree of instantaneous abnormal slip is obtained. The specific calculation method is: λ_net = λ - λOffset.

[0087] Step S315: Based on the net slip ratio and the preset slip ratio threshold, perform transient slip anomaly detection on the preset multiple wheel combinations respectively, and obtain the anomaly judgment results of each wheel combination. In this embodiment, the preset slip ratio threshold is λ_threshold. When the net slip ratio λ_net > λ_threshold and the duration exceeds a certain time, the hydroplaning flag B_AquaPlanningFA is triggered. This step can effectively distinguish between continuous low-adhesion road surfaces (such as ice surfaces) and transient hydroplaning conditions.

[0088] Furthermore, the same transient anomaly detection is performed on various wheel combinations, including the left and right rear axle wheels, front and rear axles, and diagonal wheels, outputting various hydroplaning indicators. The transient anomaly detection method is basically the same as that used for the left and right front wheels, the difference being that the wheel speed difference and relative slip ratio between wheels are calculated using the wheel speed inputs between each pair of wheels or axles. For example, the transient detection of the left and right rear axle wheels uses the wheel speed inputs of the left and right rear wheels, the transient detection of the front and rear axles uses the average wheel speed inputs of the front and rear axles, and the transient detection of the diagonal wheels uses the left front wheel speed and right rear wheel speed or the right front wheel speed and left rear wheel speed inputs. The threshold for detecting transient differences in each wheel will need to be determined through actual vehicle calibration tests based on whether the wheels are driven and the axle load distribution. Finally, B_AquaPlanningRA, B_AquaPlanningFARA, and B_AquaPlanningDiag are calculated and output.

[0089] Step S316: If the preset vehicle speed and steering wheel angle thresholds are met and the abnormal judgment result of all wheel combinations is transient slip abnormality, it is determined that there is a vehicle hydroplaning condition and a hydroplaning detection mark is output.

[0090] In this embodiment, the final hydroplaning determination is made based on the combination of the final hydroplaning indicators, and the final hydroplaning release indicator is output. Provided that the vehicle speed and steering wheel angle thresholds are met, if the indicators B_AquaPlanningFA, B_AquaPlanningRA, B_AquaPlanningFARA, and B_AquaPlanningDiag are all set simultaneously, the module will finally output the hydroplaning release detection indicator.

[0091] By introducing an adaptive offset iteration mechanism with a forgetting factor through the above-described embodiments, and utilizing the characteristics of slow steady-state baseline updates and immediate highlighting of abrupt change features, the mechanism can accurately distinguish between steady-state slip deviation on conventional road surfaces and transient slip anomalies on waterlogged road surfaces. At the same time, second-order Butterworth filtering is used to ensure the purity of wheel speed signals. Combined with multi-wheel combination full-domain slip detection, time constraint judgment, and multiple verification logic for vehicle speed and steering condition access, the mechanism effectively avoids misjudgment problems caused by sensor noise, minor road bumps, and slippage on steady-state low-adhesion roads.

[0092] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 9 Further elaborating on step S32, the crosswind monitoring and control method also includes steps S321 to S323: Step S321: Based on the pre-trained yaw rate model, combined with the vehicle motion parameters corresponding to the preset weight factor and the real-time vehicle sensing information after filtering, the first yaw rate is calculated. It should be noted that the yaw rate model comprises two parts: a yaw rate based on kinematic relationships and a yaw rate based on the dynamic yaw moment equations, considering both the geometric kinematic relationships of the problem's turning and the dynamic characteristics of transient turning. Combining the kinematic and dynamic parts, and adding weighting factors, yields the theoretical yaw rate model:

[0093] In the formula, : Kinematic weighting factor; Lateral acceleration; Longitudinal speed; is the dynamic weighting factor; m is the vehicle mass; L: Distance from center of gravity to front axle; Wheelbase; Rear axle lateral stiffness; : Rate of change of lateral acceleration.

[0094] In this embodiment, the actual lateral acceleration and longitudinal vehicle speed collected by the sensors are first filtered and smoothed using a first-order filtering module to remove high-frequency noise and instantaneous jitter interference from the original signal, outputting stable and effective vehicle motion parameters such as lateral acceleration, rate of change of lateral acceleration, and longitudinal vehicle speed. The pre-processed vehicle motion parameters, inherent vehicle structural parameters, and preset kinematic and dynamic weighting factors are then substituted into the yaw rate fusion model to solve for the kinematic and dynamic yaw rate components. After weighted fusion correction using the weighting factors, a theoretical yaw rate that balances steady-state and transient characteristics is output, which is the first yaw rate. This provides accurate theoretical benchmark parameters for subsequent residual determination of slope conditions.

[0095] Step S322: Obtain the second yaw rate based on the real-time vehicle sensor information; In this embodiment, the second yaw rate is the actual yaw response parameter under the actual driving state of the vehicle. The system directly reads the original yaw rate signal collected in real time by the IMU inertial measurement unit, and after preset low-pass filtering preprocessing to remove instantaneous impact noise and high-frequency noise interference, a stable and real actual yaw rate of the vehicle is obtained. This is used as the measured feedback parameter for working condition discrimination and is used to compare the difference with the theoretical yaw rate to characterize the actual yaw motion state of the vehicle.

[0096] Step S323: Calculate the absolute difference between the first yaw rate and the second yaw rate. When the absolute difference is greater than the preset total tolerance of yaw rate, determine that the vehicle is in a side slope condition and output a side slope detection mark.

[0097] In this embodiment, a dynamic yaw rate total tolerance Tolerance_dPsi is pre-constructed as a judgment threshold. This total tolerance is composed of two coupled parts: the basic tolerance and the wind interference tolerance. It can be dynamically adjusted adaptively with vehicle speed and wind conditions to avoid the problems of low judgment accuracy and poor adaptability of fixed thresholds.

[0098] The basic tolerance includes a lateral acceleration tolerance conversion term and a yaw rate tolerance term. The lateral acceleration tolerance term needs to be correlated with vehicle speed, and the tolerance band tightens as vehicle speed increases. The wind interference tolerance is positively correlated with real-time vehicle speed and wind resistance gain. The wind resistance gain is a wind force-yaw rate conversion calibration coefficient, which fully considers the structural characteristics of the wind force application point and the vehicle's center of gravity position. It can accurately convert lateral wind interference into the corresponding yaw rate deviation range, thus offsetting the interference of crosswind conditions on slope identification.

[0099] Further side slope condition assessment is performed. The system calculates the absolute residual between the theoretical first yaw rate and the measured second yaw rate in real time. This residual eliminates normal yaw deviation caused by active steering and conventional road curvature. When the absolute difference between the measured and theoretical yaw rates exceeds the total tolerance of yaw rates, it indicates that the current yaw deviation of the vehicle cannot be reasonably explained by steering motion, road curvature, or crosswind interference. The system determines that the vehicle is experiencing continuous steady-state yaw interference caused by the lateral slope of the road surface, indicating a side slope condition. The system then stabilizes and outputs a side slope detection flag.

[0100] Through the methods described in the above embodiments, this embodiment obtains a theoretical yaw rate benchmark that closely matches real driving characteristics by using a high-precision yaw rate model that integrates kinematics and dynamics. Simultaneously, it constructs a dynamic total tolerance discrimination threshold that is vehicle speed adaptive and wind-coupled, replacing the traditional fixed threshold discrimination method. This effectively distinguishes between transient crosswind interference, instantaneous road impact interference, and steady-state lateral slope interference, accurately identifying persistent yaw deviation anomalies caused by road side slopes. It solves the problems of confusion and high misjudgment rate in traditional side slope identification, and can stably eliminate non-crosswind interference from side slopes, further improving the accuracy and robustness of crosswind identification and crosswind control decisions.

[0101] Based on the first embodiment of this application, in the fourth embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 10 Further elaborating on step S33, the crosswind monitoring and control method also includes steps S331 to S336: Step S331: Dimensionally convert the lateral acceleration corresponding to the real-time vehicle sensing information to obtain the theoretical yaw rate, and calculate the basic characteristic difference by combining the measured yaw rate. In this embodiment, the real-time lateral acceleration of the vehicle and the yaw rate measured by the IMU are first acquired. To eliminate the discrimination error caused by the inconsistency of signal dimensions, the lateral acceleration is converted into an equivalent theoretical yaw rate dPsiFromAy, thus unifying the dimensions of the lateral motion parameters and the yaw motion parameters. Furthermore, the difference between the converted theoretical yaw rate dPsiFromAy and the actual yaw rate dPsi measured by the sensor is calculated to obtain the fundamental characteristic difference dPsiDiff, which characterizes the phase and amplitude deviations of the two types of yaw signals.

[0102] Step S332: Perform dynamic averaging filtering on the basic feature difference to obtain the filtered feature difference; In this embodiment, a first-order dynamic averaging filter algorithm is used to smooth and denoise the basic feature difference dPsiDiff, filtering out high-frequency noise from the sensor and invalid signal disturbances caused by minor road bumps and vibrations, while retaining the effective amplitude abrupt changes caused by road impacts. Through the dynamic weighted averaging characteristics of the first-order filter, the signal smoothness is optimized while ensuring no loss of transient abrupt signals, outputting a stable and pure filtered feature difference dPsiDiffF, providing a high-quality input signal for subsequent steady-state baseline stripping and relative feature solving.

[0103] Step S333: Construct a steady-state offset reference value using a preset nonlinear state observer, and calculate the relative value of the yaw rate by combining the filtered feature difference. In this embodiment, the core feature stripping step for identifying groove conditions involves constructing a dynamic steady-state offset reference value dPsiDiffAvg using a nonlinear state observer that integrates variable step-size adaptive filtering. This observer incorporates an adaptive high-pass filter with an asymmetric dynamic cutoff frequency, which can selectively filter out high-frequency interference components in the filtered feature difference dPsiDiffF. Simultaneously, it slowly tracks low-frequency steady-state offset components caused by road surface curvature, continuous crosswinds, and other conditions, updating the slowly changing steady-state offset reference value dPsiDiffAvg in real time.

[0104] Furthermore, the steady-state offset component is removed through interpolation, and the relative yaw rate is calculated as follows: dPsiDiffRel = dPsiDiffF - dPsiDiffAvg. Since the steady-state offset reference value dPsiDiffAvg has the characteristics of slow tracking and delayed updates, when the vehicle encounters a road groove impact and dPsiDiffF undergoes a sudden and violent change, the steady-state reference value cannot quickly respond to the transient change. This causes the relative yaw rate value dPsiDiffRel to rise significantly instantaneously, accurately highlighting the short-term, violent, and abrupt dynamic characteristics specific to the groove condition, and completely separating the steady-state road and crosswind interference baselines.

[0105] Step S334: Perform a first-order low-pass filter on the theoretical yaw rate and the measured yaw rate, and combine the relative value of the yaw rate to obtain a multi-dimensional vehicle state signal. In this embodiment, the core yaw motion signal undergoes global first-order low-pass filtering smoothing. The measured yaw rate dPsi and the theoretical yaw rate dPsiFromAy (converted from lateral acceleration) are filtered and denoised to obtain smoothed steady-state signals dPsiF and dPsiFromAyF. Based on the filtered clean signals, the relative value of the filtered yaw rate is calculated: dPsiDiffRel_F = dPsiF - dPsiFromAyF - PsiDiffAvg, for multi-marker consistency verification. Furthermore, the actual yaw rates dPsi and dPsiFromAy, along with the filtered yaw rate, are used to calculate the yaw rate change rates ddPsiF and ddPsiFromAy, accurately characterizing the instantaneous yaw impact intensity of the vehicle. Finally, a multi-dimensional, high signal-to-noise ratio vehicle state observation signal system is obtained.

[0106] Step S335: Based on the vehicle status signals of each dimension, compare them with the corresponding preset status thresholds to generate multiple groove recognition trigger flags; In this embodiment, based on multi-dimensional vehicle state signals, six differentiated groove recognition trigger flags are generated through an independent threshold discrimination mechanism to comprehensively verify the transient impact characteristics of the grooves, specifically including: Indicator 1: The difference between the measured yaw rate dPsi and the Ackermann theoretical yaw rate dPsiAck exceeds the limit, indicating that the actual yaw motion deviates from the theoretical steering characteristics. Indicator 2: The difference between the lateral acceleration converted yaw rate dPsiFromAy and the Ackerman yaw rate exceeds the limit, indicating abnormal lateral motion response; Indicator 3: The rate of change of yaw rate after filtering, ddPsiF, exceeds the limit, indicating that the instantaneous change in the measured yaw is drastic; Indicator 4: The rate of change of yaw rate corresponding to lateral acceleration, ddPsiFromAy, exceeds the limit, indicating that the instantaneous impact of lateral dynamics is significant; Indicator 5: The core yaw rate relative characteristic value dPsiDiffRel exceeds the limit, indicating that the transient offset change characteristic of the signal meets the standard; Marker 6: The original relative eigenvalues ​​and the filtered relative eigenvalues ​​dPsiDiffRel and dPsiDiffRel_F are in the same state, avoiding single-point false triggering caused by random noise.

[0107] Step S336: When the multiple trigger flags meet the preset trigger flag combination conditions, start the groove detection timer. If the groove detection timer duration exceeds the preset time threshold, determine that there is a vehicle groove condition and output the groove detection flag.

[0108] In this embodiment, a composite discrimination logic combining multiple markers is adopted to avoid misjudgment due to a single feature and improve the robustness of working condition identification. The specific preset trigger combination condition is: markers 1, 3, 4, and 5 are simultaneously set, and either marker 2 or marker 6 is set, which satisfies the complete groove impact feature trigger condition. For example, it satisfies marker 1 & marker 3 & (marker 2 || marker 6) & marker 4 & marker 5.

[0109] When all the combined conditions are met instantaneously, the system immediately starts the groove condition timing mechanism, filtering instantaneous noise and brief signal disturbances through duration constraints; when the continuous timing duration exceeds the preset time threshold, it determines that the current vehicle dynamics change characteristics are stably matched with the instantaneous impact characteristics of road grooves and potholes, and finally stabilizes and outputs the groove detection mark, completing the identification of groove interference conditions.

[0110] Through the methods described in the above embodiments, this embodiment employs a nonlinear state observer with an asymmetric dynamic cutoff frequency to accurately separate steady-state crosswinds, low-frequency deviations in road curvature, and instantaneous high-frequency abrupt impact features of grooves. Simultaneously, by employing multi-dimensional feature threshold verification, state consistency discrimination, and timing anti-jitter mechanisms, it significantly improves the accuracy and anti-interference capability of groove condition identification. This effectively solves the problem that traditional algorithms cannot distinguish between steady-state crosswind interference and instantaneous groove impact interference on the road surface, stably eliminating groove-related non-crosswind interference and ensuring the accuracy of crosswind force identification and the effectiveness of crosswind control strategies.

[0111] Based on the first embodiment of this application, in the fifth embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 11 Further elaborating on step S34, the crosswind monitoring and control method also includes steps S341 to S345: Step S341: Filter the wheel speed data corresponding to the real-time vehicle sensing information to calculate the speed difference and acceleration difference; In this embodiment, for the wheel speed data in the real-time vehicle sensing information, digital filtering preprocessing is first performed on the original wheel speed signals of the left and right front wheels to filter out high-frequency noise from wheel speed acquisition and signal noise caused by minor road bumps and vibrations, while retaining the effective wheel speed deviation characteristics caused by road undulations and vehicle body roll, thus obtaining stable and accurate real-time wheel speed signals of the left and right front wheels. Based on the filtered wheel speeds of the left and right front wheels, a difference calculation is performed to obtain the real-time original speed difference. Further differential calculation is performed on the continuous periodic speed difference signals, and the acceleration difference between the left and right front wheels is calculated in real time by the change in speed difference within a unit sampling period.

[0112] Step S342: Compare the speed difference with a preset fixed threshold. When the speed difference exceeds the preset fixed threshold, increment the speed difference counter. In this embodiment, the preset fixed threshold is calibrated based on the wheel speed fluctuation range of normal driving on a smooth paved road surface, which can effectively cover the normal wheel speed deviation under normal driving conditions.

[0113] The real-time filtered speed difference is compared frame by frame with a preset fixed threshold. When the real-time speed difference amplitude exceeds the fixed threshold, it is determined that there is an abnormal wheel speed deviation between the left and right wheels of the current front axle, and the speed difference counter is incremented. If the real-time speed difference does not exceed the limit, the counter is kept clear or maintained in the initial state. The counting accumulation mechanism filters out the abnormal wheel speed deviation that exists continuously, avoiding false triggering caused by instantaneous random noise.

[0114] Step S343: Calculate the dynamic threshold based on the front axle drive torque ratio, and compare the acceleration difference with the dynamic threshold. When the acceleration difference exceeds the dynamic threshold, increment the acceleration difference counter. In this embodiment, the vehicle's drive torque signal is read, the proportion of the front axle drive torque to the total vehicle drive torque is calculated, and a preset torque proportion-threshold mapping calibration curve is used to dynamically and in real-time match the acceleration difference judgment threshold corresponding to the current operating condition. Since different vehicle driving states result in variations in the inherent characteristics of wheel slippage and sudden wheel speed changes, the threshold is adaptively adjusted by the torque proportion to adapt to different driving conditions such as front-wheel drive, four-wheel drive, and dynamic torque distribution, solving the problem that a fixed threshold cannot adapt to all driving conditions.

[0115] The real-time wheel speed acceleration difference is further compared with the dynamically matched acceleration threshold. When the real-time acceleration difference exceeds the dynamic threshold corresponding to the current working condition, it is determined that the instantaneous wheel speed of the left and right front axles changes drastically and there is a sudden change in the vehicle body posture, triggering the acceleration difference counter to accumulate. If the acceleration difference does not exceed the limit, the acceleration counter does not accumulate, realizing high-precision wheel speed sudden change anomaly detection based on adaptive driving conditions.

[0116] Step S344: Detect wheel speed difference and acceleration difference for the front axle, rear axle, and front-rear axle combination wheels, and generate corresponding roll marker positions; In this embodiment, the algorithm logic is completely consistent with that used for front axle wheel speed detection, performing full-domain detection of wheel speed difference and acceleration difference for both the left and right rear axle wheels and the combined front and rear axle wheels. For the left and right rear axle wheels, the real-time speed difference and acceleration difference are calculated based on the filtered wheel speeds of the two rear axle wheels. Anomaly detection of rear axle wheel speed deviation is performed using a fixed speed threshold, a dynamic acceleration threshold based on torque ratio, and a counter accumulation mechanism. When the detection conditions are met, the rear axle roll flag B_RollingRA is set. For the combined front and rear axle wheels, the average wheel speed of the front axle and the average wheel speed of the rear axle are used as input parameters. Similarly, the speed difference and acceleration difference are calculated, and threshold exceedance is determined. When the conditions are met, the front and rear axle roll flags B_RollingFARA are set. Through multi-wheel combination cross-detection, full-coverage identification of multi-dimensional wheel speed anomaly features of the entire vehicle is achieved, avoiding the limitations of single front axle detection and improving the comprehensiveness of vehicle roll condition identification.

[0117] Step S345: When the speed difference counter and / or acceleration difference counter exceed a preset threshold and the roll flag is valid, determine that there is a vehicle roll condition and output a roll detection flag.

[0118] In this embodiment, for each axle and each wheel combination detection channel, when the cumulative value of the speed difference counter exceeds a preset counting threshold, or the cumulative value of the acceleration difference counter exceeds a preset counting threshold, either condition is met to determine that the corresponding wheel combination has a continuous wheel speed asymmetry deviation and instantaneous change characteristics, and the corresponding roll flag is effectively triggered. Based on this, a logical OR operation is performed on the front axle roll flag B_RollingFA, the rear axle roll flag B_RollingRA, and the front and rear axle roll flags B_RollingFARA. As long as any set of roll flags is effectively set, it can be determined that the vehicle is currently affected by road conditions such as road surface undulations, longitudinal ruts, and uneven road surface height, and there is a roll condition with the vehicle body tilting to the left and right. Finally, the vehicle roll detection flag B_RollingDetect is stably set and output.

[0119] By employing the methods described above, and through multi-axle and multi-wheel combination cross-detection and logical fusion judgment, non-crosswind interference such as vehicle body roll caused by road ruts and undulations can be accurately identified. This effectively distinguishes between roll dynamic disturbances and real crosswind disturbances, further improving the multi-scenario interference elimination system and greatly enhancing the robustness and recognition accuracy of the crosswind identification algorithm under complex road conditions.

[0120] For example, to help understand the implementation process of the crosswind monitoring and control method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 12 , Figure 12 A schematic diagram of the overall architecture of a crosswind detection system for crosswind measurement and control is provided, specifically: like Figure 12As shown, Figure 12 This diagram illustrates the overall hardware architecture and signal flow of the crosswind detection and control system of this invention. At the center of the figure is a box labeled "Crosswind Main Control Unit," representing the vehicle's core processor (such as a vehicle controller or area controller). To its left is a standard sensor array, including: four wheel speed sensors (labeled FL, FR, RL, RR respectively), a steering wheel angle sensor, and an inertial measurement unit (IMU) integrating a yaw rate gyroscope, longitudinal accelerometer, and lateral accelerometer. These sensors input real-time signals to the main control unit via hardwired connections or a bus (such as CAN). To the right of the main control unit is a vehicle actuator array, including: the hydraulic modulator of the Electronic Stability Program (ESP) (used for independent braking control of each wheel to generate yaw torque), the Vector Torque Control Unit (TVC) (used to adjust engine torque distribution to generate asymmetric yaw torque), and the Rear Wheel Steering System (RWS) (used to provide rear wheel steering yaw torque output). The bottom of the diagram illustrates the system's core processing flow: sensor signals first enter the "Signal Preprocessing and Vehicle State Estimation" module, then the "Crosswind Interference Torque Calculation" module, and subsequently the "Multi-Condition Interference Elimination Logic" module (comprising four sub-modules: hydroplaning, rutting, side slope, and roll). Finally, all information converges into the "Crosswind Comprehensive Judgment and Control Decision" module, whose output commands are sent to each actuator. The entire diagram clearly illustrates the complete closed-loop control chain from signal perception to information processing and decision execution.

[0121] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the crosswind measurement and control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0122] This application also provides a crosswind monitoring and control device; please refer to [reference needed]. Figure 13 The crosswind monitoring and control device includes: The sensor acquisition module 10 is used to collect real-time sensor information of the vehicle; Crosswind calculation module 20 is used to calculate crosswind force based on the real-time sensor information of the vehicle; The interference condition identification module 30 is used to determine the interference conditions of non-crosswind targets through a preset dynamic calculation strategy. The crosswind arbitration control module 40 is used to make comprehensive judgments and control decisions on crosswinds by combining the crosswind force and the non-crosswind target interference conditions.

[0123] The crosswind monitoring and control device provided in this application, employing the crosswind monitoring and control method described in the above embodiments, can solve the technical problem of accurately distinguishing crosswind disturbances from other vehicle interference conditions such as road surface disturbances in complex road environments and weather conditions. Compared with the prior art, the beneficial effects of the crosswind monitoring and control device provided in this application are the same as those of the crosswind monitoring and control method provided in the above embodiments, and other technical features of the crosswind monitoring and control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0124] This application provides a crosswind monitoring and control 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute the crosswind monitoring and control method in the first embodiment described above.

[0125] The following is for reference. Figure 14 The diagram illustrates a structural schematic suitable for implementing crosswind monitoring and control equipment in the embodiments of this application. The crosswind monitoring and control equipment in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 14 The crosswind monitoring and control equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0126] like Figure 14As shown, the crosswind monitoring and control equipment 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 the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the crosswind monitoring and control equipment. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the crosswind monitoring and control equipment to communicate wirelessly or wiredly with other devices to exchange data. Although crosswind monitoring and control equipment with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0127] 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 ROM 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.

[0128] The crosswind monitoring and control device provided in this application, employing the crosswind monitoring and control method described in the above embodiments, can solve the technical problem of accurately distinguishing crosswind disturbances from other vehicle interference conditions such as road surface disturbances in complex road environments and weather conditions. Compared with the prior art, the beneficial effects of the crosswind monitoring and control device provided in this application are the same as those of the crosswind monitoring and control method provided in the above embodiments, and other technical features of this crosswind monitoring and control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0129] 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.

[0130] 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.

[0131] 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 measurement and control method in the above embodiments.

[0132] 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 having 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 fiber, 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 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.

[0133] The aforementioned computer-readable storage medium may be included in the crosswind monitoring and control equipment; or it may exist independently and not be assembled into the crosswind monitoring and control equipment.

[0134] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the crosswind monitoring and control equipment, the crosswind monitoring and control equipment: collects real-time vehicle sensor information; calculates crosswind force based on the real-time vehicle sensor information; determines non-crosswind target interference conditions through a preset dynamic calculation strategy; and makes a comprehensive crosswind judgment and control decision by combining the crosswind force and the non-crosswind target interference conditions.

[0135] 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).

[0136] 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, can 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.

[0137] 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.

[0138] 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 aforementioned crosswind measurement and control method. This solves the technical problem of accurately distinguishing crosswind disturbances from other vehicle interference conditions, such as road surface disturbances, under complex road environments and weather conditions. 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 measurement and control method provided in the above embodiments, and will not be repeated here.

[0139] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the crosswind measurement and control method described above.

[0140] The computer program product provided in this application can solve the technical problem of accurately distinguishing crosswind disturbance from other vehicle interference conditions such as road surface disturbance in complex road environments and weather conditions. 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 measurement and control method provided in the above embodiments, and will not be repeated here.

[0141] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for crosswind measurement and control, characterized in that, The method includes: Collect real-time vehicle sensing information output by sensors; Based on the real-time sensor information of the vehicle, the crosswind force is calculated. The interference conditions of non-crosswind targets are determined by a preset dynamic calculation strategy; A comprehensive judgment and control decision on crosswind is made by combining the crosswind force and the non-crosswind target interference conditions.

2. The crosswind monitoring and control method as described in claim 1, characterized in that, The step of calculating the crosswind force based on the vehicle's real-time sensor information includes: Based on the real-time sensor information of the vehicle, the inertial torque is calculated using a pre-trained vehicle yaw inertia model. The tire torque is calculated using a pre-defined two-degree-of-freedom differential equation of vehicle motion. Obtain braking control parameters and power torque regulation parameters, and convert them into equivalent yaw moment; Combining the inertial torque, tire torque, and equivalent yaw torque, the crosswind interference torque is calculated using a preset torque balance equation. Using a pre-calibrated wind dynamic model and the distance between the wind pressure center and the vehicle's center of gravity, the crosswind force is calculated based on the crosswind disturbance moment.

3. The crosswind monitoring and control method as described in claim 1, characterized in that, The step of determining the non-crosswind target interference condition through a preset dynamic calculation strategy includes: An adaptive offset filtering technique is used to perform adaptive slip ratio offset calculation and multi-wheel slip detection. Combined with the linkage conditions of multiple hydroplaning signs, the hydroplaning condition of the vehicle is determined and the hydroplaning detection sign is output. The yaw rate is obtained by combining the filtered vehicle motion parameters with the pre-trained yaw rate model. By comparing the difference in yaw rate with the preset total tolerance of yaw rate, it is determined whether the vehicle is in a side slope condition and a side slope detection flag is output. The yaw rate and lateral acceleration corresponding to the real-time vehicle sensing information are subjected to feature conversion and filtering to obtain the relative value of the yaw rate and the corresponding vehicle status signal. When the vehicle status signal meets the preset trigger flag combination conditions, the existence of the vehicle groove condition is determined and the groove detection flag is output. The wheel speed data corresponding to the real-time vehicle sensing information is filtered and the speed difference and acceleration difference are calculated. The speed difference counter and acceleration difference counter are obtained respectively. When the speed difference counter and / or acceleration difference counter exceed the preset threshold, the vehicle roll condition is determined and a roll detection flag is output.

4. The crosswind monitoring and control method as described in claim 3, characterized in that, The steps of using adaptive offset filtering technology to solve for adaptive slip ratio offset and detect multi-wheel slip, and combining multiple hydroplaning indicator linkage conditions to determine hydroplaning conditions and output vehicle hydroplaning detection indicators include: Based on the real-time vehicle sensor information, four-wheel speed data is extracted, and the left and right front wheel speeds are extracted from the four-wheel speed data and subjected to adaptive offset filtering. The wheel speed difference is calculated based on the wheel speeds of the left and right front wheels after adaptive offset filtering, and combined with the original slip ratio, the relative slip ratio is calculated. Calculate the adaptive slip ratio offset based on the relative slip ratio calculation value and the preset forgetting factor; The net slip ratio is calculated based on the adaptive slip ratio offset and the relative slip ratio calculation value. Based on the net slip ratio and the preset slip ratio threshold, transient slip anomaly detection is performed on multiple preset wheel combinations to obtain the anomaly judgment results for each wheel combination. If the preset vehicle speed and steering wheel angle thresholds are met and the abnormal judgment result of all wheel combinations is transient slip abnormality, the vehicle is determined to be in hydroplaning condition and a hydroplaning detection mark is output.

5. The crosswind monitoring and control method as described in claim 3, characterized in that, The yaw rate includes a first yaw rate and a second yaw rate; The steps of obtaining the yaw rate by combining the filtered vehicle motion parameters with a pre-trained yaw rate model, determining whether the vehicle is in a side slope condition by comparing the difference in yaw rate with the preset total yaw rate tolerance, and outputting a side slope detection flag include: The first yaw rate is calculated based on the pre-trained yaw rate model, combined with the preset weighting factor and the filtered vehicle motion parameters. The second yaw rate is obtained based on the real-time sensor information of the vehicle. Calculate the absolute difference between the first yaw rate and the second yaw rate. When the absolute difference is greater than the preset total tolerance of yaw rate, determine that the vehicle is in a side slope condition and output a side slope detection sign.

6. The crosswind monitoring and control method as described in claim 3, characterized in that, The steps of performing feature conversion and filtering on the yaw rate and lateral acceleration corresponding to the real-time vehicle sensing information to obtain the relative value of the yaw rate and the corresponding vehicle state signal, and determining the existence of a vehicle groove condition and outputting a groove detection flag when the vehicle state signal meets the preset trigger flag combination conditions, include: The theoretical yaw rate is obtained by converting the lateral acceleration corresponding to the real-time vehicle sensing information to a dimension, and the basic characteristic difference is calculated by combining the measured yaw rate. The basic feature difference is subjected to dynamic averaging filtering to obtain the filtered feature difference; A steady-state offset benchmark is constructed using a pre-defined nonlinear state observer, and the relative value of the yaw rate is calculated by combining the filtered feature difference. The theoretical yaw rate and the measured yaw rate are subjected to first-order low-pass filtering, and a multi-dimensional vehicle state signal is obtained by combining the relative value of the yaw rate. Based on the comparison between the vehicle status signals of each dimension and the corresponding preset status thresholds, multiple groove recognition trigger signs are generated. When the multiple trigger flags meet the preset trigger flag combination conditions, the groove detection timer is started. If the groove detection timer duration exceeds the preset time threshold, it is determined that there is a vehicle groove condition and a groove detection flag is output.

7. The crosswind monitoring and control method as described in claim 3, characterized in that, The step of filtering the wheel speed data corresponding to the real-time vehicle sensor information and calculating the speed difference and acceleration difference, respectively obtaining speed difference counters and acceleration difference counters, and determining the existence of vehicle roll condition and outputting a roll detection flag when the speed difference counters and / or acceleration difference counters exceed a preset threshold includes: The wheel speed data corresponding to the real-time vehicle sensor information is filtered to calculate the speed difference and acceleration difference. The speed difference is compared with a preset fixed threshold. When the speed difference exceeds the preset fixed threshold, the speed difference counter is incremented. A dynamic threshold is calculated based on the proportion of front axle drive torque, and the acceleration difference is compared with the dynamic threshold. When the acceleration difference exceeds the dynamic threshold, the acceleration difference counter is incremented. The wheel speed difference and acceleration difference of the front axle, rear axle, and front-rear axle combination wheels are detected, and corresponding roll markers are generated. When the speed difference counter and / or acceleration difference counter exceed a preset threshold and the roll flag is valid, a vehicle roll condition is determined and a roll detection flag is output.

8. The crosswind monitoring and control method as described in claim 3, characterized in that, The steps for making a comprehensive judgment and control decision on crosswinds by combining the crosswind force and the non-crosswind target interference conditions include: Determine whether the steering wheel angle range, vehicle speed range, yaw rate range, and lateral acceleration range corresponding to the real-time vehicle sensing information conform to the preset operating area, and obtain the operating area mark; Based on the crosswind force, the level is determined by combining the preset wind force classification threshold to obtain the corresponding crosswind force level. Inspect the condition of water skid detection marks, groove detection marks, side tilt detection marks, and side slope detection marks; If any flag is set, a crosswind prohibition sign will be output. Based on the crosswind force level, crosswind no-intervention sign, and operation area sign, the corresponding crosswind control mode is output, which includes waiting mode, intervention mode, and exit mode.

9. A crosswind monitoring and control device, characterized in that, The crosswind monitoring and control device includes: The sensor acquisition module is used to collect real-time vehicle sensing information output by the sensors; The crosswind calculation module is used to calculate the crosswind force based on the real-time sensor information of the vehicle. The interference condition identification module is used to determine the interference conditions of non-crosswind targets through a preset dynamic calculation strategy. The crosswind arbitration control module is used to make comprehensive judgments and control decisions on crosswinds by combining the crosswind force and the non-crosswind target interference conditions.

10. A crosswind monitoring and control 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 monitoring and control method as described in any one of claims 1 to 8.

11. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the crosswind monitoring and control method as described in any one of claims 1 to 8.

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