A method, device and medium for early warning of vehicle instability under wind and rain conditions

Abstract

This invention relates to a method, device, and medium for intelligent assisted driving instability early warning of vehicles in windy and rainy conditions. The method involves collecting rainfall intensity, crosswind speed, and vehicle driving state parameters; querying a CFD wind-rain coupled aerodynamic load database to obtain corresponding aerodynamic six-component force data; inputting the aerodynamic six-component force data as external disturbances into the vehicle's multibody dynamics model, simulating and outputting vehicle state response parameters; calculating environmental impact parameters based on rainfall intensity and crosswind speed, and dynamically correcting the preset normal thresholds of various instability indicators based on these environmental impact parameters to obtain dynamic instability thresholds under windy and rainy conditions; assigning weights to various instability indicators according to the vehicle's current driving conditions, and calculating a comprehensive instability index based on the vehicle state response parameters and dynamic instability thresholds; determining the instability level based on the comprehensive instability index and calculating the remaining stabilization time. Compared with existing technologies, this invention has advantages such as high accuracy, strong adaptability, and good real-time performance.

Description

Technical Field

[0001] This invention relates to the field of intelligent assisted driving technology, and in particular to a method, device and medium for early warning of vehicle instability in windy and rainy conditions. Background Technology

[0002] With the intensification of global climate change and the increasing frequency of extreme weather events, the combined effects of wind and rain have become a significant factor affecting road traffic safety. Current intelligent driving systems are evolving towards Level 3 autonomous driving and above, requiring systems to possess all-weather, all-scenario environmental perception and risk assessment capabilities. However, existing intelligent driving systems suffer from significant performance degradation in windy and rainy weather, becoming a key bottleneck restricting the commercialization of advanced autonomous driving.

[0003] In vehicle instability assessment, traditional methods primarily rely on wheel speed sensors and inertial measurement units, which have the following shortcomings: First, existing technologies simplify the modeling of aerodynamic loads, typically reducing wind loads to a constant lateral force or a function linearly varying with the yaw angle, making it difficult to accurately reflect the dynamic aerodynamic characteristics under wind-rain coupling conditions. Furthermore, existing weather compensation models are mostly based on a single physical field, considering only the wind field or only the impact of rain on tire adhesion, lacking the comprehensive modeling capability for wind-rain coupling effects. Second, traditional instability warning methods often use fixed thresholds for judgment, failing to consider the dynamic impact of wind and rain intensity changes on vehicle instability boundaries. This makes the system prone to over-warning or under-warning in windy and rainy weather conditions, affecting driving experience and safety.

[0004] Therefore, how to effectively perceive and accurately warn of vehicle instability under wind and rain coupled environments is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to overcome the defects of the prior art and provide a method, device and medium for early warning of vehicle instability under windy and rainy conditions. It establishes a CFD wind-rain coupled aerodynamic load database and dynamically corrects the instability threshold by combining environmental influence parameters, thereby realizing accurate early warning of vehicle instability under windy and rainy conditions.

[0006] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a method for early warning of vehicle instability under windy and rainy conditions is provided, the specific steps of which include: S1. Collect rainfall intensity, crosswind speed and vehicle driving status parameters; based on the rainfall intensity and crosswind speed, query the pre-established CFD wind-rain coupling aerodynamic load database to obtain the corresponding aerodynamic six-component force data; S2. Input the aerodynamic six-component force data as an external disturbance into the vehicle multibody dynamics model, and simulate and output the vehicle state response parameters. S3. Calculate the environmental impact parameters based on the rainfall intensity and crosswind speed, and dynamically correct the preset normal thresholds of each instability index based on the environmental impact parameters to obtain the dynamic instability thresholds under wind and rain conditions. S4. Assign weights to various instability indicators based on the current driving conditions of the vehicle, and calculate a comprehensive instability index by combining the vehicle state response parameters and dynamic instability threshold; determine the instability level based on the comprehensive instability index and calculate the remaining stabilization time.

[0007] Furthermore, the CFD wind-rain coupling aerodynamic load database in S1 is established as follows: based on CFD numerical simulation, the Marshall-Palmer raindrop spectrum is used to describe the raindrop size distribution, a raindrop breakup model and a water film evolution model are set up, and the aerodynamic six-component force data under different rainfall intensities, different crosswind velocities and different yaw angles are calculated. The aerodynamic six-component force includes drag, lateral force, lift, yaw moment, tilt moment and pitch moment, and the calculated aerodynamic six-component force data is established as an external load data table.

[0008] Furthermore, the model parameters of the vehicle multibody dynamics model in S2 include the vehicle mass, center of gravity height, and tire adhesion coefficient corrected for water film thickness; the interface takes the aerodynamic six-component force data as external disturbance input, and the simulation output vehicle state response parameters include yaw rate, sideslip angle, and lateral displacement.

[0009] Furthermore, the instability indicators in S3 include trajectory deviation, yaw rate, sideslip angle, roll angle, and stability margin; the vehicle state response parameters include yaw rate, sideslip angle, and lateral displacement. The trajectory deviation is calculated based on the lateral displacement and the desired trajectory, and the roll angle and stability margin are calculated based on the vehicle state response parameters.

[0010] Furthermore, the environmental impact parameters in S3 are calculated as follows: the ratio of the rainfall intensity to the preset maximum rainfall intensity and the ratio of the crosswind speed to the preset maximum crosswind speed are multiplied by preset rainfall weighting coefficients and crosswind weighting coefficients respectively, and then summed to obtain the environmental impact parameters.

[0011] Furthermore, the dynamic correction method in S3 is as follows: setting the preset normal threshold values ​​for each instability index... Multiply by a correction factor to obtain the dynamic instability threshold under wind and rain conditions. The correction factor is based on environmental impact parameters. The expression is obtained by multiplying it by 0.5: , A plus sign is used when the instability index is positively correlated with the wind and rain environment, and a minus sign is used when the instability index is negatively correlated with the wind and rain environment.

[0012] Furthermore, the comprehensive instability index in S4 is calculated as follows: the ratio of the actual value of each instability indicator to the corresponding dynamic instability threshold is multiplied by the weight of the corresponding instability indicator and then summed to obtain the comprehensive instability index; the weight of the instability indicator is dynamically allocated according to the current driving conditions of the vehicle, which include straight driving conditions, turning conditions, and lane changing conditions; the instability level is divided into multiple levels according to the comprehensive instability index, including stable, low risk, medium risk, high risk, and instability. When the comprehensive instability index exceeds the preset threshold, it is determined that there is an instability risk, and a corresponding warning prompt is output in combination with the remaining stability time.

[0013] Furthermore, the remaining stabilization time is calculated as follows: calculate the remaining time required for each instability index to reach the dynamic instability threshold from the current state, and take the minimum value as the remaining stabilization time; the remaining time of a single instability index is obtained based on the difference between the corresponding dynamic instability threshold and the current actual value, as well as the rate of change of the index.

[0014] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0015] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention establishes a wind-rain coupled aerodynamic load database based on CFD numerical simulation, uses the Marshall-Palmer raindrop spectrum to describe the raindrop size distribution, and sets up a raindrop breakup model and a water film evolution model to calculate the aerodynamic six-component force data under different rainfall intensities, crosswind velocities and yaw angles. Compared with the existing technology that simplifies wind load to a constant lateral force or a linear function, this invention can more realistically reflect the dynamic fluctuation characteristics of aerodynamic load under wind-rain coupled environment. The aerodynamic six-component force data obtained from this database is used as an external disturbance input to the vehicle multibody dynamics model, so that the vehicle state response parameters output by the simulation are closer to the actual vehicle dynamic behavior under wind and rain environment, thus providing an accurate data basis for subsequent instability judgment and effectively improving the reliability of vehicle instability early warning under wind and rain environment.

[0017] (2) This invention introduces environmental impact parameters, which are obtained by weighted summation based on the ratio of rainfall intensity to the preset maximum rainfall intensity and the ratio of crosswind speed to the preset maximum crosswind speed. The preset normal thresholds for various instability indicators are then dynamically corrected, achieving continuous adaptive adjustment of the instability thresholds according to the intensity of wind and rain. This dynamic correction mechanism selects positive or negative correction based on the correlation between the instability indicators and the wind and rain environment. This allows the thresholds for positively correlated indicators such as trajectory deviation and yaw rate to be reasonably relaxed as wind and rain intensify, while simultaneously tightening the thresholds for negatively correlated indicators such as stability margin as wind and rain intensify. This overcomes the shortcomings of traditional fixed threshold methods that result in over-warning or under-warning during windy and rainy weather, improving the environmental adaptability of instability determination.

[0018] (3) This invention dynamically allocates the weights of various instability indicators according to the current driving conditions of the vehicle, calculates the comprehensive instability index in combination with the dynamic instability threshold, and further calculates the remaining stability time for graded early warning. It can make differentiated assessments of the instability sensitivity characteristics of different driving conditions. In the straight driving condition, it focuses on trajectory deviation and yaw rate monitoring; in the turning condition, it focuses on yaw rate and sideslip angle monitoring; and in the lane changing condition, it focuses on trajectory deviation and yaw rate monitoring. This makes the instability judgment result match the vehicle dynamic characteristics under different conditions. This invention can achieve fast response without neural network training, meet the real-time requirements of the vehicle system, and has important engineering application value for improving the safety and reliability of intelligent driving system in windy and rainy weather. Attached Figure Description

[0019] Figure 1 A flowchart for a vehicle intelligent assisted driving instability warning method under windy and rainy conditions; Figure 2 A flowchart for dynamic threshold determination in a vehicle intelligent assisted driving instability warning method under wind and rain conditions; Figure 3 A dynamic threshold correction curve for a vehicle intelligent assisted driving instability warning method under wind and rain conditions; Figure 4 A flowchart illustrating the instability warning process for intelligent assisted driving systems in windy and rainy conditions. Figure 5 This is an architecture diagram of a vehicle intelligent assisted driving instability warning system under windy and rainy conditions. Figure 6 The instability index response and simulated external disturbance input diagram of the virtual scenario of wind and rain driving for the intelligent assisted driving instability early warning method of vehicles in wind and rain scenarios; Figure 7 This is a diagram showing the instability warning result under the second working condition of the double-track shifting operation in this embodiment. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] In the field of intelligent assisted driving technology, vehicle instability warning under wind and rain coupled environments has always been a key focus and challenge for the industry. In windy and rainy weather, vehicles are not only affected independently by wind and rain fields, but also face complex aerodynamic effects resulting from their coupling, posing a challenge to the accurate judgment of instability. However, existing methods for vehicle instability warning under wind and rain coupled environments have two main shortcomings. First, the acquisition of aerodynamic loads lacks comprehensive modeling of the wind-rain coupling effect, making it difficult to accurately reflect the dynamic impact of factors such as raindrop phase change and water film evolution on vehicle dynamics. Second, the use of fixed thresholds for instability judgment cannot adaptively adjust with changes in wind and rain intensity, resulting in warning accuracy that fails to meet the usage needs under different wind and rain levels. To address these issues, this embodiment provides a method for intelligent assisted driving instability warning in windy and rainy scenarios. It obtains aerodynamic six-component force data by establishing a CFD wind-rain coupled aerodynamic load database, introduces environmental influence parameters to dynamically correct the instability threshold, and calculates a comprehensive instability index based on weights allocated according to driving conditions, achieving accurate assessment and graded warning of vehicle instability under windy and rainy conditions.

[0022] like Figure 1 As shown in this embodiment, a method for early warning of vehicle instability under windy and rainy conditions is provided. The specific steps include: S1. Collect rainfall intensity, crosswind speed and vehicle driving status parameters; based on rainfall intensity and crosswind speed, query the pre-established CFD wind-rain coupling aerodynamic load database to obtain the corresponding aerodynamic six-component force data; S2. Input the aerodynamic six-component force data as an external disturbance into the vehicle multibody dynamics model, and simulate the output of vehicle state response parameters. S3. Calculate environmental impact parameters based on rainfall intensity and crosswind speed, and dynamically correct the preset normal thresholds of various instability indicators based on environmental impact parameters to obtain the dynamic instability thresholds under wind and rain conditions. S4. Assign weights to various instability indicators based on the vehicle's current driving conditions, and calculate the comprehensive instability index by combining the vehicle's state response parameters and dynamic instability threshold; determine the instability level based on the comprehensive instability index and calculate the remaining stabilization time.

[0023] The CFD wind-rain coupling aerodynamic load database in S1 is established as follows: Based on CFD numerical simulation, the Marshall-Palmer raindrop spectrum is used to describe the raindrop size distribution. A raindrop breakup model and a water film evolution model are set up to calculate the aerodynamic six-component force data under different rainfall intensities, different crosswind velocities, and different yaw angles. The aerodynamic six-component forces include drag, lateral force, lift, yaw moment, tilt moment, and pitch moment. The calculated aerodynamic six-component force data is then used to establish an external load data table.

[0024] Specifically, the raindrop phase is described using the Marshall-Palmer raindrop spectrum to depict the particle size distribution, as shown in the formula: ,in The diameter of the raindrop. In this embodiment, the spectral intercept is used. , For spectral slope, R represents rainfall intensity; the fragmentation model employs secondary fragmentation via TAB activation when the Weber number exceeds the critical threshold; the water film effect is described using shallow water equations to represent the evolution of the surface water film. The calculated aerodynamic six components include drag. F x Lateral force F y Lift F z Yaw moment M z Lateral tilting moment M x and pitch moment M y Calculation of load conditions: rainfall intensity R The value ranges from 0 to 220 mm / h, the crosswind velocity V ranges from 0 to 30 m / s, and the yaw angle... β The value range is from 0° to 30°. The calculated aerodynamic six-component force data are used to create an external load data table.

[0025] The vehicle multibody dynamics model in S2 includes vehicle mass, center of gravity height, and tire adhesion coefficient corrected for water film thickness. The interface takes aerodynamic six-component force data as external disturbance input, and the simulation output vehicle state response parameters include yaw rate, sideslip angle, and lateral displacement.

[0026] The tire adhesion coefficient is corrected for water film thickness using the following formula: , in The thickness of the water film is The coefficient of friction of the tire at that time denoted as the tire adhesion coefficient on a dry road surface, and e is the natural constant. In this embodiment, the vehicle mass is 2200 kg and the center of gravity height is 0.65 m.

[0027] like Figure 2 The diagram shown is a flowchart for dynamic threshold determination. The instability indicators in S3 include trajectory deviation, yaw rate, sideslip angle, roll angle, and stability margin; the vehicle state response parameters include yaw rate, sideslip angle, and lateral displacement. The trajectory deviation is calculated based on the lateral displacement and the desired trajectory, and the roll angle and stability margin are calculated based on the vehicle state response parameters.

[0028] The environmental impact parameters in S3 are calculated as follows: the ratio of rainfall intensity to preset maximum rainfall intensity and the ratio of crosswind speed to preset maximum crosswind speed are multiplied by preset rainfall weight coefficients and crosswind weight coefficients respectively, and then summed to obtain the environmental impact parameters.

[0029] The dynamic correction method involves setting the preset normal threshold values ​​for each instability indicator. Multiply by a correction factor to obtain the dynamic instability threshold under wind and rain conditions. The correction factor is based on the environmental impact parameters. The expression is obtained by multiplying it by 0.5: , A plus sign is used when the instability index is positively correlated with the wind and rain environment, and a minus sign is used when the instability index is negatively correlated with the wind and rain environment.

[0030] Specifically, in this embodiment, the instability index is calculated as follows: Trajectory Deviation The calculation formula is: , in, This represents the actual lateral displacement of the vehicle. The lateral displacement is the desired trajectory, and the trajectory deviation is used to characterize the degree to which the vehicle deviates from the desired trajectory. yaw rate It is used directly as a monitoring indicator to characterize the rate at which a vehicle rotates around its vertical axis; Side slip angle The calculation formula is: , in, The lateral speed of the vehicle. The longitudinal velocity of the vehicle is represented by the slip angle, which is used to characterize the angle between the vehicle velocity vector and the longitudinal axis of the vehicle body. The roll angle φ is directly used as a monitoring indicator to characterize the angle at which the vehicle tilts around its longitudinal axis. The formula for calculating the stability margin SM is: , in, The coefficient of friction of the tire. It is the acceleration due to gravity. For the height of the center of mass, For vehicle speed, Lateral acceleration is used, and stability margin is used to characterize tire adhesion reserve.

[0031] The wind and rain level is determined based on the rainfall intensity R and crosswind speed V, as shown in Table 1.

[0032] Table 1 Classification of Wind and Rain Environment Levels Environmental impact parameters The expression is: , in, Rainfall intensity, To preset the maximum rainfall intensity, = 220 mm / h, where V is the crosswind speed. To preset the maximum crosswind speed, = 30 m / s, This is the rainfall weighting coefficient. = 0.6, This is the crosswind weighting coefficient. = 0.4.

[0033] In this embodiment, the preset normal thresholds for various instability indicators are set under different environmental influence parameters. The correction values ​​are shown in Table 2. Figure 3 The figure shows the dynamic threshold correction curve, which illustrates the continuous change of the dynamic instability threshold of each instability index with the environmental influence parameter γ.

[0034] Table 2 Instability Index Threshold Adjustment like Figure 4 The diagram shown is a flowchart of the instability warning process. The comprehensive instability index in S4 is calculated as follows: the ratio of the actual value of each instability indicator to its corresponding dynamic instability threshold is multiplied by the weight of the corresponding instability indicator, and then summed to obtain the comprehensive instability index. The weights of the instability indicators are dynamically allocated based on the vehicle's current driving conditions, including straight-line driving, turning, and lane-changing. The instability level is divided into multiple levels based on the comprehensive instability index, including stable, low risk, moderate risk, high risk, and instability. When the comprehensive instability index exceeds a preset threshold, it is determined that there is an instability risk, and a corresponding warning message is output based on the remaining stabilization time.

[0035] The remaining stabilization time is calculated as follows: calculate the remaining time required for each instability indicator to reach the dynamic instability threshold from the current state, and take the minimum value as the remaining stabilization time; the remaining time of a single instability indicator is obtained based on the difference between the corresponding dynamic instability threshold and the current actual value, as well as the rate of change of the indicator.

[0036] The expression for the comprehensive instability index is: , in, This is the serial number of the instability indicator. These correspond to five instability indicators. Let i be the weight of the instability index. Let i be the actual value of the instability index. Let be the dynamic instability threshold under wind and rain conditions corresponding to the i-th instability index; when the comprehensive instability index A value greater than 1 indicates a risk of instability; weight The weighting is dynamically allocated based on the vehicle's current driving conditions, specifically as follows: In straight-line driving conditions, the weight of trajectory deviation is 0.4 and the weight of yaw rate is 0.3; in turning conditions, the weight of yaw rate is 0.4 and the weight of sideslip angle is 0.3; in lane-changing conditions, the weight of trajectory deviation is 0.35 and the weight of yaw rate is 0.35.

[0037] The expression for the remaining steady time (TTC) is: , in, For the first i The current actual value of the instability indicator. For the first i The rate of change of the state of the instability index The instability time (TTC) is obtained through differential calculation; the minimum value of the calculated results of each instability index is taken as the remaining stable time.

[0038] The instability level is classified according to the comprehensive instability index as follows: a comprehensive instability index less than 0.6 indicates stability; a comprehensive instability index greater than or equal to 0.6 and less than 0.8 indicates mild risk; a comprehensive instability index greater than or equal to 0.8 and less than 1.0 indicates moderate risk; a comprehensive instability index greater than or equal to 1.0 and less than 1.2 indicates high risk; and a comprehensive instability index greater than 1.2 indicates instability. The instability warning level classification and corresponding prompt control strategy in this embodiment are shown in Table 3.

[0039] Table 3. Classification of Instability Early Warning Levels and Corresponding Control Strategies According to the method of this embodiment, a vehicle intelligent assisted driving instability warning system in windy and rainy conditions is also provided, including an environmental perception module, a CFD database module, a vehicle dynamics simulation module, a dynamic threshold determination module, and an instability warning module, such as... Figure 5 As shown.

[0040] The system comprises the following modules: an environmental perception module for collecting rainfall intensity, crosswind speed, and vehicle driving status parameters; a CFD database module for storing a pre-established CFD wind-rain coupling aerodynamic load database, which retrieves corresponding aerodynamic six-component force data based on rainfall intensity and crosswind speed; a vehicle dynamics simulation module for inputting the aerodynamic six-component force data as external disturbances into the vehicle multibody dynamics model and simulating and outputting vehicle state response parameters; a dynamic threshold determination module for calculating environmental impact parameters based on rainfall intensity and crosswind speed, dynamically correcting the preset normal thresholds of various instability indicators based on these parameters to obtain dynamic instability thresholds under wind and rain conditions; allocating weights to various instability indicators based on the vehicle's current driving conditions, and calculating a comprehensive instability index based on the vehicle state response parameters and dynamic instability thresholds; determining the instability level and calculating the remaining stabilization time based on the comprehensive instability index; and an instability warning module for outputting the instability level and remaining stabilization time.

[0041] To verify the effectiveness of the driving instability warning method and system provided in this embodiment, a simulation was conducted. The first operating condition was set as follows: initial speed 100 km / h, straight driving, step disturbance: t At time 0s, the rainfall intensity is R=140mm / h and the crosswind speed is V=18m / s.

[0042] The wind and rain field represents the coupled effect of wind and rain fields on vehicles within the same spatiotemporal domain. This includes the interaction between aerodynamic loads and road surface adhesion, the disturbance effect of raindrop phase change on airflow, and the changes in tire characteristics caused by water film evolution, rather than the independent effects of a single wind or rain field. The environmental attenuation factor, γ, is a comprehensive quantitative index characterizing the degree of influence of wind and rain intensity on vehicle stability boundaries. Its value ranges from 0 to 1, where γ=0 indicates no wind or rain influence, and γ=1 indicates extreme wind and rain conditions. In practical applications, the rainfall weighting coefficient k1 and crosswind weighting coefficient k2 can be adjusted according to regional climate characteristics to adapt to the wind and rain coupling characteristics of different regions. The remaining stability time (TTC) is used to predict the time required for a vehicle to develop from its current state to a critical instability state, thus quantifying the urgency of the warning.

[0043] First, environmental perception was performed, obtaining rainfall intensity R=140mm / h, crosswind speed V=18m / s, and vehicle speed V=100km / h. The aerodynamic six components were obtained from the CFD aerodynamic load data table and imported into a multibody dynamics model for simulation. Figure 6 As shown, the real-time instability index value is obtained.

[0044] like Figure 2 The dynamic thresholds were adjusted as shown, and the environmental impact parameter γ was calculated to be 0.62, with the wind and rain level ranging from strong to extreme wind and rain. Based on γ, the dynamic instability thresholds for various instability indicators were calculated and compared with the simulation results. Table 4 shows the thresholds for various instability indicators in the simulation system under the first working condition.

[0045] Table 4 Thresholds of various instability indicators in the simulation system under the first operating condition. like Figure 3 The calculation of the comprehensive instability index is shown, based on the weight allocation under straight-line driving conditions and trajectory deviation. The weight is set to 0.4, and the yaw rate is... The weight is set to 0.3, and the sideslip angle is... The weight of is set to 0.15, and the weight of stability margin SM is set to 0.05. The comprehensive instability index is then calculated. =1.21, which is greater than 1.2, so it is judged as an instability level, and the remaining stabilization time TTC is calculated to be 0.25s.

[0046] The warning result indicated that the vehicle would become unstable within 0.25 seconds, and immediate stabilization measures were recommended. The verification result showed that the vehicle actually fishtailed after 0.3 seconds, proving the warning was accurate.

[0047] The second operating condition is set as follows: vehicle speed 80km / h, using the ISO 3888-2 double lane change test standard, with a rainy / windy environment of rainfall intensity R=80mm / h and crosswind speed V=12m / s, assuming an instability warning condition for double lane change. Table 5 shows the parameter values ​​for each parameter under the second operating condition.

[0048] Table 5. Parameter values ​​under the second operating condition. The scene weights are adjusted as follows: During the lane-changing phase from t=3.0s to 4.0s, the trajectory deviation weight is 0.35, and the yaw rate weight is 0.35. During the homing phase from t=5.0s to 6.0s, the weight of the yaw rate is 0.40 and the weight of the sideslip angle is 0.30.

[0049] The warning result is as follows: At t=3.5s, the risk level is assessed as moderate, and caution is advised when driving. At t=5.0s, the risk level is determined to be high, and it is recommended to decelerate immediately.

[0050] The comparative verification results are as follows: When a fixed threshold is used, namely a yaw rate threshold of 15° / s, instability is misjudged at t=3.5s (the actual value is 18, which is greater than 15); using the method of this invention, t=3.5s is accurately identified as moderate risk and t=5.0s as high risk. Figure 7 The diagram shows the instability warning results under the second working condition of double lane change. The yaw rate time series curve includes the fixed threshold judgment result, the dynamic threshold judgment result, and the actual vehicle response. The instability index change trajectory marks the instability level and the remaining stabilization time warning value at each moment.

[0051] The third operating condition is set as follows: a uniform operating condition with a vehicle speed of 80 km / h, single lane change operation, and comparisons are made under different wind and rain levels. Table 6 shows the parameter values ​​for each item under the third operating condition.

[0052] Table 6. Parameter values ​​under the third working condition. As shown in Table 6, under the same driving operation, different wind and rain environments correspond to different warning levels. The method of the present invention can adaptively adjust the judgment result according to the wind and rain intensity.

[0053] In summary, this embodiment establishes a CFD wind-rain coupled aerodynamic load database, uses the Marshall-Palmer raindrop spectrum to describe raindrop size distribution, and sets up raindrop breakup and water film evolution models. It comprehensively considers the dynamic fluctuation characteristics of raindrop phase change, water film evolution, and aerodynamic load. Compared with the existing technology that simplifies wind load to a constant lateral force or a linear function, it can more realistically reflect the aerodynamic load characteristics under wind-rain coupled environment, providing an accurate data basis for subsequent instability determination. Furthermore, by introducing the environmental impact parameter γ, which is obtained by weighted summation based on the ratio of rainfall intensity to the preset maximum rainfall intensity and the ratio of crosswind speed to the preset maximum crosswind speed, and dynamically correcting the preset normal thresholds of various instability indicators, continuous adaptive adjustment of the instability thresholds with wind and rain intensity is achieved. Simultaneously, a strategy of dynamically allocating weights according to the vehicle's current driving conditions is adopted. In straight-line driving conditions, the focus is on monitoring trajectory deviation and yaw rate; in turning conditions, the focus is on monitoring yaw rate and sideslip angle; and in lane-changing conditions, the focus is on monitoring trajectory deviation and yaw rate. This accurately matches the instability assessment needs of different driving conditions. This embodiment also employs a computational architecture combining CFD aerodynamic load database query with vehicle multibody dynamics model simulation. The instability parameter calculation cycle is short, and rapid response can be achieved without neural network training, meeting the real-time requirements of the vehicle system. By calculating the comprehensive instability index and determining the instability level, and calculating the remaining stabilization time for graded early warning, the accuracy of instability early warning in windy and rainy environments is significantly improved compared to traditional fixed threshold methods, effectively avoiding over-warning or under-warning problems in extreme weather.

[0054] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0055] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0056] Multiple components in the device are connected to an I / O interface, including: input units such as a keyboard, mouse, etc.; output units such as various types of displays, speakers, etc.; storage units such as disks, optical disks, etc.; and communication units such as network interface cards, modems, wireless transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the method of the present invention by any other suitable means (e.g., by means of firmware).

[0057] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0058] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0059] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0060] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A vehicle intelligent auxiliary driving instability early warning method in a wind and rain scene, characterized in that, The specific steps comprise: S1, collecting rainfall intensity, crosswind speed and vehicle driving state parameters; according to the rainfall intensity and crosswind speed, querying a pre-established CFD wind and rain coupled aerodynamic load database to obtain corresponding aerodynamic six-component force data; S2, inputting the aerodynamic six-component force data as external disturbance into a vehicle multi-body dynamics model to simulate and output vehicle state response parameters; S3, calculating environmental influence parameters according to the rainfall intensity and crosswind speed, and dynamically correcting the pre-set normal threshold of each instability index according to the environmental influence parameters to obtain dynamic instability threshold values in the wind and rain environment; S4, according to the current driving condition of the vehicle, distributing the weight of each instability index, combining the vehicle state response parameters and the dynamic instability threshold values to calculate a comprehensive instability index; determining the instability level according to the comprehensive instability index and calculating the remaining stability time. 2.The method according to claim 1, wherein, The establishment method of the CFD wind and rain coupled aerodynamic load database in S1 is: based on CFD numerical simulation, using Marshall-Palmer raindrop spectrum to describe the raindrop particle size distribution, setting raindrop breaking model and water film evolution model, calculating the aerodynamic six-component force data under different rainfall intensity, different crosswind speed and different yaw angle conditions, the aerodynamic six-component force includes drag, lateral force, lift, yaw moment, roll moment and pitch moment, and the calculated aerodynamic six-component force data is established as an external load data table. 3.The method of claim 1, wherein, The model parameters of the vehicle multi-body dynamics model in S2 include the mass of the whole vehicle, the height of the center of mass and the tire adhesion coefficient corrected with the water film thickness; the aerodynamic six-component force data is input as external disturbance through the interface, and the simulated output vehicle state response parameters include yaw rate, side slip angle and lateral displacement. 4.The method of claim 1, wherein, The instability indexes in S3 include trajectory deviation, yaw rate, side slip angle, roll angle and stability margin; the vehicle state response parameters include yaw rate, side slip angle and lateral displacement, the trajectory deviation is calculated according to the lateral displacement and the expected trajectory, and the roll angle and the stability margin are calculated according to the vehicle state response parameters.

5. The vehicle intelligent auxiliary driving instability early warning method in wind and rain scene according to claim 1, characterized in that, The calculation method of the environmental influence parameters in S3 is: according to the ratio of the rainfall intensity to the pre-set maximum rainfall intensity and the ratio of the crosswind speed to the pre-set maximum crosswind speed, multiplying the pre-set rainfall weight coefficient and crosswind weight coefficient respectively and then summing up to obtain the environmental influence parameters.

6. The vehicle intelligent auxiliary driving instability early warning method in wind and rain scene according to claim 1, characterized in that, The dynamic correction in S3 is as follows: the preset normal threshold of each instability index is multiplied by a correction coefficient to obtain the dynamic instability threshold in wind and rain environment The dynamic instability threshold in wind and rain environment is obtained by multiplying the preset normal threshold of each instability index by a correction coefficient The correction coefficient is obtained according to 0.5 times of the environmental influence parameter The expression is as follows: ， When the instability index is positively correlated with the wind and rain environment, the plus sign is used, and when the instability index is negatively correlated with the wind and rain environment, the minus sign is used.

7. The method according to claim 1, wherein, The comprehensive instability index in S4 is calculated as follows: the ratio of the actual value of each instability indicator to the corresponding dynamic instability threshold is multiplied by the weight of the corresponding instability indicator and then summed to obtain the comprehensive instability index. The weight of the instability indicator is dynamically allocated according to the current driving conditions of the vehicle, which include straight driving, turning, and lane changing. The instability level is divided into multiple levels according to the comprehensive instability index, including stable, low risk, medium risk, high risk, and instability. When the comprehensive instability index exceeds the preset threshold, it is determined that there is an instability risk, and a corresponding warning prompt is output in combination with the remaining stability time. 8.The method of claim 7, wherein, The remaining stabilization time is calculated as follows: calculate the remaining time required for each instability index to reach the dynamic instability threshold from the current state, and take the minimum value as the remaining stabilization time; the remaining time of a single instability index is obtained based on the difference between the corresponding dynamic instability threshold and the current actual value, as well as the rate of change of the index.

9. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

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