Method and device for quantifying risk of vehicle instability on low adhesion road surface and vehicle

Abstract

The application relates to a low-attachment road vehicle instability risk quantification method and device and a vehicle, wherein the method comprises the following steps: based on a preset Fiala tire model and a double-track two-degree-of-freedom vehicle model, calculating the longitudinal force and the lateral force of the vehicle to determine a front-rear side slip angle phase plane of the vehicle; based on the front-rear side slip angle phase plane, determining the stability region and the stability boundary of the front-rear wheel side slip angle state of the vehicle under different vehicle states and different attachment coefficients to determine the low-attachment road vehicle reference stability region and the reference stability boundary; based on the influence of the reference stability boundary and the road attachment coefficient on the stability boundary of the vehicle and the influence of the vehicle speed and the front wheel steering angle on the stability boundary, scaling the distance between the vehicle state and the origin to obtain a scaled vehicle state, and comparing the scaled vehicle state with the reference stability boundary to generate a low-attachment road vehicle stability margin index. Therefore, the problems in the prior art that the linear two-degree-of-freedom model has limitations, the state of the vehicle on the low-attachment road cannot be accurately described, the ice and snow road attachment coefficient changes, the vehicle model parameters have time variability, the instability risk size is affected, and the instability risk quantification method is complex and difficult to generalize are solved.

Description

Technical Field

[0001] This application relates to the field of autonomous driving decision control technology, and in particular to a method, device and vehicle for quantifying the risk of vehicle instability on low-adhesion road surfaces. Background Technology

[0002] Currently, the adhesion coefficient of mixed ice and snow pavements exhibits significant nonlinear time-varying characteristics, typically ranging from 0.1 to 0.3, which is 65%-85% lower than that of conventional dry asphalt pavements (adhesion coefficient 0.7-0.9). It is noteworthy that the spatial distribution of the friction coefficient on ice and snow pavements shows significant heterogeneity—affected by factors such as uneven vehicle load distribution, varying solar radiation intensity gradients, and the discreteness of de-icing agent spraying, a single road segment may simultaneously contain abrupt frictional characteristic abrupt regions composed of multiple phases, including dry snow zones, compacted snow zones, and black ice zones. This multi-physics coupled pavement environment easily induces time-varying jumps in vehicle dynamic parameters, often causing traditional control algorithms to fail.

[0003] In related technologies, the autonomous driving decision control architecture is based on a quasi-static vehicle dynamics model, and uses a linear two-degree-of-freedom model or a simplified magic formula tire model for motion planning and control. The instability risk of the vehicle is assessed using a stability index based on the saddle point.

[0004] However, in related technologies, the linear two-degree-of-freedom model does not consider the strong nonlinear characteristics of the tire force-slip curve. When the longitudinal slip ratio exceeds the critical value, the tire force will enter the saturation region, which makes it impossible to effectively capture the dynamic process. It has significant limitations under low adhesion conditions. The stability index based on saddle point is used to quantify the instability risk, which requires the identification of a large number of parameters, which increases the complexity of practical applications and limits its generalization ability under different vehicle models and road conditions. It urgently needs to be improved. Summary of the Invention

[0005] This application provides a method, device, and vehicle for quantifying vehicle instability risk on low-adhesion roads, in order to solve the problems in related technologies, such as the limitations of linear two-degree-of-freedom models, which make it difficult to accurately describe the state of vehicles on low-adhesion roads, the changes in adhesion coefficients on icy and snowy roads, the time-varying nature of vehicle model parameters affecting the magnitude of instability risk, and the complexity and difficulty in generalizing instability risk quantification methods.

[0006] The first aspect of this application provides a method for quantifying the risk of vehicle instability on low-adhesion roads, comprising the following steps: Based on a preset Fiala tire model and a dual-track two-degree-of-freedom vehicle model, calculating the longitudinal and lateral forces of the vehicle, and determining the front and rear slip angle phase plane diagrams of the vehicle based on the longitudinal and lateral forces; based on the front and rear slip angle phase plane diagrams, determining the stable regions and stable boundaries of the front and rear wheel slip angle states of the vehicle under different vehicle states and different adhesion coefficients, and determining the low-adhesion road surface reference stable region of the vehicle based on the stable regions and the stable boundaries, so as to obtain the reference stable boundary of the vehicle according to the low-adhesion road surface reference stable region; based on the influence of the reference stable boundary and the road adhesion coefficient on the vehicle's stable boundary, and the influence of vehicle speed and front wheel steering angle on the stable boundary, scaling the distance between the vehicle state and the origin to obtain the scaled vehicle state, and comparing the scaled vehicle state with the reference stable boundary to generate a vehicle stability margin index for low-adhesion roads.

[0007] This application embodiment can acquire the longitudinal force and lateral force of the vehicle to construct a phase plane diagram of the front and rear sideslip angles, thereby determining the stable region and boundary under different states and adhesion coefficients. After obtaining the benchmark stable boundary, the vehicle state distance is scaled by comprehensively considering the influence of road adhesion coefficient, vehicle speed, and front wheel steering angle and compared with the benchmark boundary to generate a stability margin index. This quantifies the instability risk of the vehicle on low-adhesion road surfaces and can provide a meaningful parameter for the perception, decision-making, and control of intelligent vehicles on icy and snowy roads, thereby improving the performance of intelligent vehicles when driving on low-adhesion road surfaces.

[0008] Optionally, in one embodiment of this application, determining the low-adhesion road surface reference stable region of the vehicle based on the stable region and the stable boundary, and obtaining the reference stable boundary of the vehicle based on the low-adhesion road surface reference stable region, includes: obtaining the typical vehicle speed, front wheel steering angle, and characteristic adhesion coefficient of the low-adhesion road surface under the characteristic adhesion coefficient condition through the front and rear wheel slip angle phase plane diagram; determining a circular region with the origin of the coordinates of the two saddle points as the center and the distance from the two saddle points to the origin as the radius; obtaining the low-adhesion road surface reference stable region based on the circular region; and obtaining the reference stable boundary based on the boundary of the low-adhesion road surface reference stable region.

[0009] The embodiments of this application can obtain typical working conditions of low-adhesion road surfaces, obtain the coordinates of two saddle points using the front and rear wheel side slip angle phase plane diagrams, and then determine a circular region as the reference stable region and boundary with the origin as the center and the distance from the saddle point to the origin as the radius. This simplifies the process of determining the reference stable region. By using the saddle point coordinates and the circular approximation, the core stability range of the low-adhesion road surface is effectively extracted, significantly improving the calculation efficiency and laying the foundation for the rapid calculation of the subsequent stability margin.

[0010] Optionally, in one embodiment of this application, the construction formula for the Fiala tire model is:

[0011]

[0012] Where i = fl, fr, rl, rr represent the four wheels of the car: left front, right front, left rear, and right rear, respectively, and α sli C is the critical slip angle of the tire. αi μ represents the lateral stiffness of the tire. i α is the coefficient of adhesion between the ground and each tire. i This represents the slip angle of the tire.

[0013] The embodiments of this application can accurately describe the nonlinear mechanical characteristics of tires on low-adhesion road surfaces, especially the force saturation behavior when approaching and reaching the adhesion limit. This provides key theoretical model support for the accurate calculation of vehicle longitudinal and lateral forces, thereby ensuring the accuracy of subsequent phase plane analysis and stability margin assessment, and guaranteeing the reliability of vehicle state analysis.

[0014] Optionally, in one embodiment of this application, the formula for the vehicle stability margin index of the low-adhesion road surface is:

[0015]

[0016] Where ε is the vehicle stability margin index based on the minimum adhesion coefficient on low-adhesion road surfaces, and x α The distance between the vehicle's front and rear side slip angles and the origin, μ is the adhesion coefficient corresponding to passing through the stable boundary, μ min R is the minimum adhesion coefficient for four wheels. bst0 To stabilize the boundary radius.

[0017] The embodiments of this application can combine the minimum adhesion coefficient of four wheels, the stable boundary radius, and the distance between the vehicle's front and rear side slip angles and the origin to quantify the vehicle's stability margin, effectively reflecting the relative stability of the vehicle under low adhesion conditions. This provides a direct and usable decision basis for the stability control system and improves the safety of driving on low-adhesion roads.

[0018] A second aspect of this application provides a device for quantifying the risk of vehicle instability on low-adhesion roads, comprising: a calculation module, used to calculate the longitudinal force and lateral force of the vehicle based on a preset Fiala tire model and a dual-track two-degree-of-freedom vehicle model, and to determine the front and rear slip angle phase plane diagram of the vehicle based on the longitudinal force and the lateral force; a determination module, used to determine the stable region and stable boundary of the front and rear wheel slip angle states of the vehicle under different vehicle states and different adhesion coefficients based on the front and rear slip angle phase plane diagram, and to determine the low-adhesion road surface reference stable region of the vehicle based on the stable region and the stable boundary, so as to obtain the reference stable boundary of the vehicle based on the low-adhesion road surface reference stable region; and a quantification module, used to scale the distance between the vehicle state and the origin based on the influence of the reference stable boundary and the road adhesion coefficient on the vehicle's stable boundary, and the influence of vehicle speed and front wheel steering angle on the stable boundary, to obtain a scaled vehicle state, and to compare the scaled vehicle state with the reference stable boundary to generate a vehicle stability margin index for low-adhesion roads.

[0019] This application embodiment can acquire the longitudinal force and lateral force of the vehicle to construct a phase plane diagram of the front and rear sideslip angles, thereby determining the stable region and boundary under different states and adhesion coefficients. After obtaining the benchmark stable boundary, the vehicle state distance is scaled by comprehensively considering the influence of road adhesion coefficient, vehicle speed, and front wheel steering angle and compared with the benchmark boundary to generate a stability margin index. This quantifies the instability risk of the vehicle on low-adhesion road surfaces and can provide a meaningful parameter for the perception, decision-making, and control of intelligent vehicles on icy and snowy roads, thereby improving the performance of intelligent vehicles when driving on low-adhesion road surfaces.

[0020] Optionally, in one embodiment of this application, the determining module includes: a first acquisition unit, used to acquire typical vehicle speed, front wheel steering angle, and characteristic adhesion coefficient of the wet road surface after snow for low adhesion; a second acquisition unit, used to acquire the coordinates of the two saddle points under the characteristic adhesion coefficient condition through the front and rear wheel slip angle phase plane diagram; and a determining region unit, used to determine a circular region with the origin of the coordinates of the two saddle points as the center and the distance from the two saddle points to the origin as the radius, obtain the low adhesion road surface reference stable region based on the circular region, and obtain the reference stable boundary based on the boundary of the low adhesion road surface reference stable region.

[0021] The embodiments of this application can obtain typical working conditions of low-adhesion road surfaces, obtain the coordinates of two saddle points using the front and rear wheel side slip angle phase plane diagrams, and then determine a circular region as the reference stable region and boundary with the origin as the center and the distance from the saddle point to the origin as the radius. This simplifies the process of determining the reference stable region. By using the saddle point coordinates and the circular approximation, the core stability range of the low-adhesion road surface is effectively extracted, significantly improving the calculation efficiency and laying the foundation for the rapid calculation of the subsequent stability margin.

[0022] Optionally, in one embodiment of this application, the construction formula for the Fiala tire model is:

[0023]

[0024] Where i = fl, fr, rl, rr represent the four wheels of the car: left front, right front, left rear, and right rear, respectively, and α sli C is the critical slip angle of the tire. αi μ represents the lateral stiffness of the tire. i α is the coefficient of adhesion between the ground and each tire. i This represents the slip angle of the tire.

[0025] The embodiments of this application can accurately describe the nonlinear mechanical characteristics of tires on low-adhesion road surfaces, especially the force saturation behavior when approaching and reaching the adhesion limit. This provides key theoretical model support for the accurate calculation of vehicle longitudinal and lateral forces, thereby ensuring the accuracy of subsequent phase plane analysis and stability margin assessment, and guaranteeing the reliability of vehicle state analysis.

[0026] Optionally, in one embodiment of this application, the formula for the vehicle stability margin index of the low-adhesion road surface is:

[0027]

[0028] Where ε is the vehicle stability margin index based on the minimum adhesion coefficient on low-adhesion road surfaces, and x α The distance between the vehicle's front and rear side slip angles and the origin, μ is the adhesion coefficient corresponding to passing through the stable boundary, μ min R is the minimum adhesion coefficient for four wheels. bst0 To stabilize the boundary radius.

[0029] The embodiments of this application can combine the minimum adhesion coefficient of four wheels, the stable boundary radius, and the distance between the vehicle's front and rear side slip angles and the origin to quantify the vehicle's stability margin, effectively reflecting the relative stability of the vehicle under low adhesion conditions. This provides a direct and usable decision basis for the stability control system and improves the safety of driving on low-adhesion roads.

[0030] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for quantifying the risk of vehicle instability on low-adhesion surfaces as described in the above embodiments.

[0031] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for quantifying the risk of vehicle instability on low-adhesion road surfaces.

[0032] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for quantifying the risk of vehicle instability on low-adhesion road surfaces.

[0033] This application's embodiments can acquire the vehicle's longitudinal and lateral forces to construct a phase plane diagram of the front and rear sideslip angles, thereby determining the stable regions and boundaries under different states and adhesion coefficients. After obtaining the baseline stable boundary, the vehicle state distance is scaled by comprehensively considering the influence of the road adhesion coefficient, vehicle speed, and front wheel steering angle, and compared with the baseline boundary to generate a stability margin index. This quantifies the instability risk of a vehicle on low-adhesion roads and provides a meaningful parameter for the perception, decision-making, and control of intelligent vehicles on icy and snowy roads, improving the performance of intelligent vehicles driving on low-adhesion roads. Therefore, it solves the problems of limitations in linear two-degree-of-freedom models, which make it difficult to accurately describe the vehicle's state on low-adhesion roads; variations in the adhesion coefficient on icy and snowy roads; time-varying vehicle model parameters affecting the magnitude of instability risk; and complex and difficult-to-generalize methods for quantifying instability risk.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0036] Figure 1 This is a flowchart illustrating a method for quantifying vehicle instability risk on low-adhesion road surfaces, according to an embodiment of this application.

[0037] Figure 2 This is a schematic diagram of a dual-track two-degree-of-freedom model according to an embodiment of this application;

[0038] Figure 3 This is a schematic diagram of the phase plane of the sideslip angle-yaw rate of a vehicle according to an embodiment of this application;

[0039] Figure 4 This is a schematic diagram of the phase plane of the front and rear wheel slip angles of an automobile according to an embodiment of this application;

[0040] Figure 5 This is a schematic diagram of a low-adhesion pavement reference stability region according to an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of the actual stable region and the reference stable region according to an embodiment of this application;

[0042] Figure 7 This is a schematic diagram of the actual stable region and the instability risk benchmark region considering the variation of the adhesion coefficient according to an embodiment of this application;

[0043] Figure 8 This is a schematic diagram of a device for quantifying the risk of vehicle instability on low-adhesion road surfaces, provided according to an embodiment of this application.

[0044] Figure 9 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application.

[0045] Figure label:

[0046] 10-Vehicle instability risk quantification device on low-adhesion road surfaces; 100-Calculation module, 200-Determination module, 300-Quantification module; 901-Memory, 902-Processor, 903-Communication interface. Detailed Implementation

[0047] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0048] The following description, with reference to the accompanying drawings, outlines a method, apparatus, and vehicle for quantifying vehicle instability risk on low-adhesion surfaces according to embodiments of this application. Addressing the limitations of linear two-degree-of-freedom models mentioned in the background art, which make it difficult to accurately describe the vehicle's state on low-adhesion surfaces, the changing adhesion coefficient on icy and snowy roads, and the time-varying nature of vehicle model parameters affecting the magnitude of instability risk, as well as the complexity and difficulty in generalizing instability risk quantification methods, this application provides a method for quantifying vehicle instability risk on low-adhesion surfaces. In this method, longitudinal and lateral forces of the vehicle are acquired to construct a phase plane diagram of front and rear slip angles, thereby determining the stable regions and boundaries under different states and adhesion coefficients. After obtaining the baseline stable boundary, the vehicle state distance is scaled by comprehensively considering the influence of the road adhesion coefficient, vehicle speed, and front wheel steering angle, and compared with the baseline boundary to generate a stability margin index. This quantifies the instability risk of the vehicle on low-adhesion surfaces and provides a meaningful parameter for the perception, decision-making, and control of intelligent vehicles on icy and snowy roads, improving the performance of intelligent vehicles driving on low-adhesion surfaces. This solves the problems of limitations in linear two-degree-of-freedom models, which make it difficult to accurately describe the state of vehicles on low-adhesion roads, the changing adhesion coefficient on icy and snowy roads, the time-varying nature of vehicle model parameters affecting the magnitude of instability risk, and the complexity and difficulty in generalizing instability risk quantification methods.

[0049] Specifically, Figure 1This is a flowchart illustrating a method for quantifying vehicle instability risk on low-adhesion road surfaces, as provided in an embodiment of this application.

[0050] like Figure 1 As shown, the method for quantifying vehicle instability risk on low-adhesion road surfaces includes the following steps:

[0051] In step S101, based on the preset Fiala tire model and dual-track two-degree-of-freedom vehicle model, the longitudinal force and lateral force of the vehicle are calculated, and based on the longitudinal force and lateral force, the front and rear side slip angle phase plane diagram of the vehicle is determined.

[0052] It is understood that the Fiala tire model in this application embodiment can be a dynamic model used to describe the relationship between tire lateral force and slip angle; the dual-track two-degree-of-freedom vehicle model can be a simplified vehicle dynamic model, mainly considering the lateral motion and yaw motion of the vehicle; the longitudinal force can be the force generated by the tire along its rolling direction or the opposite direction; the lateral force can be the force generated by the tire perpendicular to its rolling direction, such as centrifugal force; the slip angle can be the angle between the actual rolling direction of the tire and the center line of the tire plane; the front and rear slip angle phase plane diagram can be a two-dimensional state plane diagram composed of the front wheel slip angle and the rear wheel slip angle as the horizontal and vertical coordinate axes.

[0053] In actual implementation, the embodiments of this application can use the Fiala tire model and the dual-track two-degree-of-freedom model to model the vehicle. The dual-track two-degree-of-freedom model used is as follows: Figure 2 As shown, according to Figure 2 By listing the equilibrium equations for longitudinal force, lateral force, and rotational torque, the dual-track, three-degree-of-freedom vehicle model can be obtained, as shown below:

[0054]

[0055] Among them, F xf F xr F yf F yr It refers to the longitudinal force on the front axle, the longitudinal force on the rear axle, the lateral force on the front axle, and the lateral force on the rear axle of the vehicle. Its magnitude is the sum of the longitudinal or lateral forces of each tire on the front and rear axles of the distributed electric drive vehicle.

[0056] Combining the Fiala tire model and the dual-track three-degree-of-freedom vehicle model, if the longitudinal velocity V x Four-wheel adhesion coefficient μ i With the front wheel steering angle δ set to a constant value, we can calculate the yaw rate with respect to the center of gravity sideslip angle β and the yaw rate ω. r This is a stable phase diagram along the vertical and horizontal axes, and a schematic diagram of the phase plane representing the centroid sideslip angle versus yaw rate, i.e., β-ω. r Schematic diagram of phase plane.

[0057] like Figure 3 As shown, three equilibrium points are marked with solid and hollow black dots. The solid black dot at the origin is a stable equilibrium point, while the other two points are saddle points, which are unstable equilibrium points. When the vehicle is in a stable equilibrium point, all four wheels are in the linear region, or the front wheels are in the non-linear region while the rear wheels are in the linear region. At the saddle points, the front wheels are in the linear region while the rear wheels are in the non-linear region, meaning the vehicle is drifting. When the vehicle has no front wheel steering angle and all four wheels have equal adhesion coefficients, the two saddle points are symmetrical about the origin. From the phase diagram, the area between the two dashed lines is the stable region, and all curves in these regions converge to the stable equilibrium point. In the stable region, the lines above and below the equilibrium point are denser, indicating that the vehicle converges to the equilibrium point more quickly when in this region. Furthermore, for most points in this region, all four tires are in a linear condition, meaning none of the tires have a large slip angle. For vehicles traveling on low-traction surfaces, a certain degree of wheel slippage is permissible, effectively reducing unnecessary additional control. However, excessive wheel slippage should be avoided, as even if the vehicle is initially stable, it may easily enter the unstable region outside the dashed lines. Therefore, when discussing the vehicle's stable region, we consider the central quadrilateral area. This region lies entirely within the stable boundary and is considered the stable region. Within this region, most points, including both front and rear wheels, are in the linear region, indicating complete vehicle stability. Only a small number of points may see the front or rear wheels enter the non-linear region, which aligns well with the requirements for vehicle control on icy and snowy roads.

[0058] Side slip angle α of the front and rear wheels of the vehicle f α r Its sideslip angle β and yaw rate ω r The following relationship exists:

[0059]

[0060] Based on this equation, the phase diagram of the center of mass sideslip angle-yaw rate, i.e., β-ω, can be obtained. r The phase plane diagram is transformed into the front and rear wheel slip angle phase diagram, i.e., α. f -α r Phase diagram.

[0061] like Figure 4 As shown, the circular stable region with the two saddle points as its diameter can basically correspond to β-ω. r The quadrilateral stable region in the phase plane diagram. In contrast, a circular stable boundary is more advantageous for calculating the stable region.

[0062] This application embodiment can accurately calculate the vehicle's forces and generate front and rear side slip angle phase plane diagrams by combining the Fiala tire model and the dual-track two-degree-of-freedom vehicle model. It retains the nonlinear characteristics of tire forces, simplifies the complexity of vehicle dynamics calculations, and provides accurate and efficient basic data for subsequent analysis of the vehicle's stable region.

[0063] Optionally, in one embodiment of this application, the construction formula for the Fiala tire model is:

[0064]

[0065] Where i = fl, fr, rl, rr represent the four wheels of the car: left front, right front, left rear, and right rear, respectively, and α sli C is the critical slip angle of the tire. αi μ represents the lateral stiffness of the tire. i α is the coefficient of adhesion between the ground and each tire. i This represents the tire's slip angle.

[0066] It is understood that, in the embodiments of this application, the critical slip angle can be the critical value at which the tire lateral force changes from linear growth to saturation. After exceeding this value, the tire force no longer increases linearly with the slip angle. The lateral stiffness can be the sensitivity of the tire's lateral force to the slip angle at a small slip angle. The adhesion coefficient can characterize the frictional ability between the road surface and the tire, and directly affects the maximum lateral force that the tire can provide. The slip angle can be the angle between the tire rolling direction and the vehicle driving direction, and is the main reason for the generation of lateral force.

[0067] In actual implementation, the Fiala tire model used to calculate the tire lateral force in this embodiment is shown in the following formula:

[0068]

[0069] Where i = fl, fr, rl, rr, represent the four wheels of the car: left front, right front, left rear, and right rear. α sli The critical slip angle of the tire is calculated as follows:

[0070]

[0071] Among them, C αi μ represents the lateral stiffness of the tire. i Let α be the coefficient of adhesion between the ground and each tire. Here, we assume the coefficient of adhesion to be isotropic. i Let the slip angle of the tire be represented. The equation for solving this equation is shown below:

[0072]

[0073] F ziThe vertical force on the tire can be obtained from the vertical load on the vehicle model, as shown in the following equation:

[0074]

[0075] Where h is the ground clearance of the vehicle's center of gravity. η i The attenuation factor for this tire model is calculated using the following method:

[0076]

[0077] The embodiments of this application can accurately describe the nonlinear mechanical characteristics of tires on low-adhesion road surfaces, especially the force saturation behavior when approaching and reaching the adhesion limit. This provides key theoretical model support for the accurate calculation of vehicle longitudinal and lateral forces, thereby ensuring the accuracy of subsequent phase plane analysis and stability margin assessment, and guaranteeing the reliability of vehicle state analysis.

[0078] In step S102, based on the front and rear side slip angle phase plane diagram, the stable region and stable boundary of the front and rear wheel side slip angle of the vehicle under different vehicle states and different adhesion coefficients are determined, and based on the stable region and stable boundary, the low-adhesion road surface reference stable region of the vehicle is determined, so as to obtain the reference stable boundary of the vehicle according to the low-adhesion road surface reference stable region.

[0079] It is understood that in the embodiments of this application, the stable region can refer to the range of front and rear wheel side slip angle combinations in the front and rear side slip angle phase plane diagram in which the vehicle can maintain stable driving, and the stable boundary can be the boundary line of the stable region; the low-adhesion road surface reference stable region can be a reference stable region specifically determined for low-adhesion road surfaces (such as ice and snow, wet and slippery road surfaces), and the reference stable boundary can be the boundary of the low-adhesion road surface reference stable region.

[0080] For example, embodiments of this application can analyze different α values ​​when the vehicle speed, front wheel steering angle, and ground adhesion coefficient (assuming all four wheels have the same adhesion coefficient to the ground) are different. f -α r From the stability boundary in the phase diagram, we can conclude that when the front wheel steering angle is small and the four-wheel adhesion coefficient is constant, increasing the longitudinal vehicle speed will significantly narrow the stable region in the middle of the phase diagram, resulting in a significant reduction in the stable region. However, as the vehicle speed increases, the rate at which the stable region in the middle shrinks with increasing speed will slow down.

[0081] When the longitudinal vehicle speed and the four-wheel adhesion coefficient are constant, as the front wheel steering angle increases, the stable equilibrium point located at the origin will move towards the saddle point on one side, and the saddle point on the other side will also move in the same direction. Therefore, the position of the saddle point on one side will gradually approach the stable equilibrium point, and the distance between the two points will decrease significantly. When the front wheel steering angle increases to a certain extent, the saddle point on that side will disappear, at which point only one stable equilibrium point and one saddle point remain on the phase plane. During this process, the stable region will shrink slightly, but its size change is significantly less than the impact of the longitudinal vehicle speed.

[0082] The lower the ground adhesion coefficient, the closer the left and right saddle points will be to the central equilibrium point, resulting in a significantly smaller stable region compared to high adhesion. Therefore, the lower the adhesion coefficient, the smaller the stable region. Furthermore, the adhesion coefficient is linearly related to the distance between the two saddle points, meaning it is linearly related to the diameter or radius of the stable region. For example, when all four wheels have an adhesion coefficient of 0.8, the distance between the two saddle points is twice that when all four wheels have an adhesion coefficient of 0.4; that is, when all four wheels have an adhesion coefficient of 0.8, the diameter of the stable boundary is twice that when all four wheels have an adhesion coefficient of 0.4. This property is highly suitable for designing stability benchmarks and stability margin indices.

[0083] Low-adhesion road surfaces, such as icy and snowy roads, often have a variable coefficient of adhesion, frequently consisting of asphalt, snow-covered, and icy surfaces joined together. Therefore, further analysis of the changing trend of the vehicle phase diagram's stable region when a vehicle's tires travel on a road surface with varying coefficients of adhesion led to the following conclusion: when the coefficient of adhesion changes, the size of the stable region is smaller than that with relatively high coefficients of adhesion, but larger than that with relatively low coefficients of adhesion.

[0084] In addition to steady-state conditions, the impact of longitudinal acceleration and deceleration on vehicle stability must also be considered. This application's embodiments utilize a dual-track model to analyze the changes in the stability region of a vehicle traveling in a straight line at a certain speed with a fixed road surface adhesion coefficient and input longitudinal forces such as driving and braking forces. Analysis under different conditions reveals that longitudinal forces only significantly affect the stability region after reaching a certain level. However, as the road surface adhesion coefficient decreases, the threshold of longitudinal force that significantly reduces the stability region also decreases. If the adhesion coefficient decreases to 0.15, for a vehicle weighing approximately 1.3 tons, a single-wheel longitudinal force of only 30N is needed for a significant reduction in the stability region. Therefore, aggressive acceleration or braking actions should not be taken on icy or snowy roads.

[0085] Furthermore, this application embodiment designs a benchmark stability region, which is the stability region of a car with certain model parameters when traveling in a straight line at a constant speed under a low coefficient of adhesion. The radius of this region will be used as a benchmark for determining the risk of vehicle instability.

[0086] The embodiments of this application can analyze the stable region and boundary under different vehicle conditions and adhesion coefficients, accurately extract the benchmark stable region and boundary of low adhesion road surface, and provide a unified and targeted reference standard for subsequent quantification of instability risk, avoiding evaluation deviations caused by differences in working conditions.

[0087] Optionally, in one embodiment of this application, a reference stable region for the low-adhesion road surface of the vehicle is determined based on the stable region and the stable boundary, so as to obtain the reference stable boundary of the vehicle based on the reference stable region for the low-adhesion road surface. This includes: obtaining the typical vehicle speed, front wheel steering angle, and characteristic adhesion coefficient of the low-adhesion road surface under the characteristic adhesion coefficient condition through the front and rear wheel slip angle phase plane diagram; determining a circular region with the origin of the coordinates of the two saddle points as the center and the distance from the two saddle points to the origin as the radius; obtaining the reference stable region for the low-adhesion road surface based on the circular region; and obtaining the reference stable boundary based on the boundary of the reference stable region for the low-adhesion road surface.

[0088] It is understood that the characteristic adhesion coefficient of the wet and slippery road surface after snow in the embodiments of this application can be 0.4; the saddle point can be an unstable equilibrium point in the front and rear side deflection phase plane diagram.

[0089] For example, this application embodiment can design a baseline stability region. This region represents the stable area of ​​a car with defined model parameters traveling in a straight line at a constant speed under a low coefficient of friction. The radius of this region will be used as a benchmark for determining the risk of vehicle instability. The benchmark setting process is as follows: Due to the low coefficient of friction on icy and snowy roads, vehicle speeds generally do not exceed 40 km / h, or approximately 11 m / s. For urban roads after snowfall, the part with the highest coefficient of friction should be the wet, slippery surface after the snow has melted, with a coefficient of friction of approximately 0.4. The vehicle speed V can be obtained through a phase diagram. x =11m / s, front wheel steering angle δ=0°, road adhesion coefficient μ=0.4, vehicle α f -α r The phase plane is determined, and the coordinates of the saddle points at this point are obtained. A circular region with these two saddle point coordinates as its radius (the coordinates of the two saddle points at this point are symmetrical about the origin) and the origin as its center is defined as the benchmark region for low-adhesion pavement instability risk, also known as the benchmark stability region S. st0 Its boundary is the reference stable boundary with radius R. bst0 .like Figure 5 As shown, this benchmark stability region will serve as the standard region for determining whether a vehicle is unstable, and will become an important component of the subsequently proposed stability margin index.

[0090] The embodiments of this application can determine the reference stable region and boundary through a circular area based on the typical working conditions of low-adhesion road surfaces and the saddle point coordinates in the phase plane diagram. This simplifies the definition process of the reference stable region and boundary, which not only conforms to the actual driving characteristics of low-adhesion road surfaces, but also simplifies the shape of the reference boundary, providing a clear reference distance for the subsequent calculation of stability margin index.

[0091] In step S103, based on the influence of the reference stability boundary and the road surface adhesion coefficient on the vehicle's stability boundary, as well as the influence of vehicle speed and front wheel steering angle on the stability boundary, the distance between the vehicle's state and the origin is scaled to obtain the scaled vehicle state. The scaled vehicle state is then compared with the reference stability boundary to generate a vehicle stability margin index for low-adhesion road surfaces.

[0092] It is understood that in the embodiments of this application, the stability margin index can be a parameter that quantifies the distance between the current state of the vehicle and the instability boundary, with a value between 0 and 1, and a larger value indicates greater stability.

[0093] For example, in this application embodiment, based on the result that the lower the vehicle speed, the larger the stable area of ​​the vehicle, when the road adhesion coefficient is 0.4, ensuring that the vehicle speed is less than or equal to 11 m / s, and that the vehicle does not have an excessively large front wheel steering angle, the actual stable area S of the vehicle under this condition is determined. st Compared with the reference stability region S proposed above st0 The following relationship must be satisfied:

[0094]

[0095] Under these road surface adhesion conditions, as long as the vehicle's condition does not involve significant driving, braking, or steering, and the speed does not exceed 11 m / s, while the front and rear side slip angles are within acceptable limits... α If it remains within the baseline stability region, the following equation is satisfied:

[0096]

[0097] At this point, the vehicle's state must be within the truly stable region S. st Internally, instability will not occur, such as Figure 6 As shown.

[0098] As analyzed above, the magnitude of the adhesion coefficient is linearly related to the diameter of the stable region, and the stable region S varies depending on the adhesion coefficients of the four wheels of the vehicle on the road surface. st The size will be in the stable region S corresponding to the maximum adhesion coefficient. st,max The size of the stable region S corresponding to the minimum adhesion coefficient st,min Between the sizes, and satisfying the equation:

[0099]

[0100] Therefore, considering only the variation in the adhesion coefficient, we take the minimum adhesion coefficient μ for all four wheels. min The corresponding stable region S st,min Setting a stability margin metric ensures that the car remains within the true stability range (S). st Within this region, instability will not occur. Furthermore, utilizing the property that the adhesion coefficient is linearly related to the diameter of the stable region, μ... min By comparing the adhesion coefficient μ = 0.4 used to set the reference stable region, the conversion from the stable region under the current minimum adhesion coefficient to the reference stable region can be completed. Thus, as long as the longitudinal vehicle speed V is maintained... x For speeds ≤11 m / s and without excessively large front wheel steering angles, the relationship between the stable regions satisfies the following equation:

[0101] S st,min ∝S′ st ,

[0102]

[0103] Where S′ st This is the stable region of the vehicle at this point, converted to the stable region when μ = 0.4. It's equivalent to linearly enlarging or shrinking the diameter of the stable region by the ratio of the adhesion coefficient. The conclusion remains: as long as the vehicle's state, after scaling by the ratio of the adhesion coefficient, is within the baseline stable region, then the vehicle's state is within the true stable region. Figure 7 As shown, the closer a vehicle is to the stability boundary, the higher the risk of instability.

[0104] Furthermore, embodiments of this application can propose a vehicle stability margin index based on the minimum adhesion coefficient on low-adhesion road surfaces. Based on the influence of the reference stability boundary and the road adhesion coefficient on the vehicle's stability boundary, as well as the influence of vehicle speed and front wheel steering angle on the stability boundary, the distance between the vehicle's state and the origin is scaled to obtain a scaled vehicle state. The scaled vehicle state is then compared with the reference stability boundary to generate a vehicle stability margin index for low-adhesion road surfaces.

[0105] The embodiments of this application can take into account the influence of road surface adhesion coefficient, vehicle speed, and front wheel steering angle on the stability boundary. After scaling the vehicle state distance, it is compared with the reference boundary, which effectively compensates for the dynamic influence of the actual road surface adhesion coefficient, vehicle speed, and front wheel steering angle on the vehicle stability boundary. The generated stability margin index can accurately quantify the instability risk of the vehicle under low adhesion road surface, providing an intuitive and operable basis for real-time judgment of vehicle stability.

[0106] Optionally, in one embodiment of this application, the formula for the vehicle stability margin index on low-adhesion road surfaces is:

[0107]

[0108] Where ε is the vehicle stability margin index based on the minimum adhesion coefficient on low-adhesion road surfaces, and x α The distance between the vehicle's front and rear side slip angles and the origin, μ is the adhesion coefficient corresponding to passing through the stable boundary, μ min R is the minimum adhesion coefficient for four wheels. bst0 To stabilize the boundary radius.

[0109] In practical implementation, the embodiments of this application can propose a vehicle stability margin index ε based on the minimum adhesion coefficient on low-adhesion road surfaces, and its calculation method is as follows:

[0110]

[0111] Among them, the vehicle's front and rear side slip angle state x α The distance from the origin, the adhesion coefficient corresponding to the stable boundary (0.4), and the minimum adhesion coefficient μ of the four wheels. min The ratio after scaling (the scaled state is x′) α The distance from the stable boundary, and the radius R of the stable boundary. bsr0 To make a comparison.

[0112] The stability margin index ranges from (-∞, 1], when the scaled front and rear sideslip angles x′ are in a certain state. α When the vehicle is at the origin, ε = 1. At this point, the vehicle is least likely to become unstable. When x′ α When located at a point in the middle of the baseline stability region, but not at the origin, 0 < ε < 1. The closer to the origin, the further the vehicle is from the stability boundary, the less prone it is to instability, and the larger the value of ε. When x′ α When located on the baseline stability boundary, ε = 0. Since the baseline stability region is generally contained within the true stability region, the vehicle should not yet be unstable, but is very close to instability. When x′ α When the vehicle is outside the baseline stability region, ε < 0. At this time, the vehicle is very prone to instability and should be given an emergency warning and avoided.

[0113] The stability margin indicator can be used when the vehicle does not undergo abrupt acceleration, deceleration, or steering, and the longitudinal speed is less than 11 m / s. However, considering that vehicles generally do not accelerate, decelerate, or steer sharply on low-friction surfaces, but may not on high-friction normal asphalt surfaces, this indicator is only applicable to low-friction surfaces. Furthermore, because the baseline stability zone is set conservatively, this stability margin indicator can also be used at low speeds when there is more aggressive longitudinal and lateral control.

[0114] The embodiments of this application can combine the minimum adhesion coefficient of four wheels, the stable boundary radius, and the distance between the vehicle's front and rear side slip angles and the origin to quantify the vehicle's stability margin, effectively reflecting the relative stability of the vehicle under low adhesion conditions. This provides a direct and usable decision basis for the stability control system and improves the safety of driving on low-adhesion roads.

[0115] The method for quantifying vehicle instability risk on low-adhesion surfaces proposed in this application can acquire the vehicle's longitudinal and lateral forces to construct a phase plane diagram of the front and rear sideslip angles, thereby determining the stable regions and boundaries under different states and adhesion coefficients. After obtaining the baseline stable boundary, the vehicle state distance is scaled by comprehensively considering the influence of the road adhesion coefficient, vehicle speed, and front wheel steering angle, and compared with the baseline boundary to generate a stability margin index. This quantifies the instability risk of the vehicle on low-adhesion surfaces and provides a meaningful parameter for the perception, decision-making, and control of intelligent vehicles on icy and snowy roads, improving the performance of intelligent vehicles driving on low-adhesion surfaces. This solves the problems of limitations in linear two-degree-of-freedom models, which make it difficult to accurately describe the vehicle's state on low-adhesion surfaces; the time-varying nature of vehicle model parameters due to changes in the adhesion coefficient on icy and snowy surfaces, which affects the magnitude of instability risk; and the complexity and difficulty in generalizing instability risk quantification methods.

[0116] Next, referring to the accompanying drawings, a device for quantifying the risk of vehicle instability on low-adhesion road surfaces, according to an embodiment of this application, is described.

[0117] Figure 8 This is a schematic diagram of the structure of the vehicle instability risk quantification device for low-adhesion road surfaces according to an embodiment of this application.

[0118] like Figure 8 As shown, the low-adhesion road surface vehicle instability risk quantification device 10 includes: a calculation module 100, a determination module 200, and a quantification module 300.

[0119] The calculation module 100 is used to calculate the longitudinal and lateral forces of the vehicle based on the preset Fiala tire model and the dual-track two-degree-of-freedom vehicle model, and to determine the front and rear side slip angle phase plane diagram of the vehicle based on the longitudinal and lateral forces.

[0120] The determination module 200 is used to determine the stable region and stable boundary of the front and rear wheel sideslip angle states of the vehicle under different vehicle states and different adhesion coefficients based on the front and rear sideslip angle phase plane diagram, and to determine the low-adhesion road surface reference stable region of the vehicle based on the stable region and stable boundary, so as to obtain the reference stable boundary of the vehicle based on the low-adhesion road surface reference stable region.

[0121] The quantization module 300 is used to scale the distance between the vehicle state and the origin based on the influence of the baseline stability boundary and the road surface adhesion coefficient on the vehicle's stability boundary, as well as the influence of vehicle speed and front wheel steering angle on the stability boundary, to obtain the scaled vehicle state. The scaled vehicle state is then compared with the baseline stability boundary to generate a vehicle stability margin index for low-adhesion road surfaces.

[0122] Optionally, in one embodiment of this application, the determining module 200 includes: a first acquiring unit, a second acquiring unit, and a determining region unit.

[0123] The first acquisition unit is used to acquire typical vehicle speed, front wheel steering angle, and characteristic adhesion coefficient of wet and slippery road surface after snow for low adhesion road surface conditions.

[0124] The second acquisition unit is used to obtain the coordinates of the two saddle points under the characteristic adhesion coefficient working condition through the front and rear wheel side slip angle phase plane diagram.

[0125] Define the region unit, which is used to define a circular region with the origin of the coordinates of the two saddle points as the center and the distance from the two saddle points to the origin as the radius. Based on the circular region, obtain the low-adhesion pavement reference stability region, and based on the boundary of the low-adhesion pavement reference stability region, obtain the reference stability boundary.

[0126] Optionally, in one embodiment of this application, the construction formula for the Fiala tire model is:

[0127]

[0128] Where i = fl, fr, rl, rr represent the four wheels of the car: left front, right front, left rear, and right rear, respectively, and α sli C is the critical slip angle of the tire. αi μ represents the lateral stiffness of the tire. i α is the coefficient of adhesion between the ground and each tire. i This represents the tire's slip angle.

[0129] Optionally, in one embodiment of this application, the formula for the vehicle stability margin index on low-adhesion road surfaces is:

[0130]

[0131] Where ε is the vehicle stability margin index based on the minimum adhesion coefficient on low-adhesion road surfaces, and x α The distance between the vehicle's front and rear side slip angles and the origin, μ is the adhesion coefficient corresponding to passing through the stable boundary, μ min R is the minimum adhesion coefficient for four wheels. bst0 To stabilize the boundary radius.

[0132] It should be noted that the foregoing explanation of the embodiment of the method for quantifying the risk of vehicle instability on low-adhesion road surfaces also applies to the device for quantifying the risk of vehicle instability on low-adhesion road surfaces in this embodiment, and will not be repeated here.

[0133] The vehicle instability risk quantification device for low-adhesion road surfaces proposed in this application can acquire the vehicle's longitudinal and lateral forces to construct a phase plane diagram of the front and rear sideslip angles, thereby determining the stable regions and boundaries under different states and adhesion coefficients. After obtaining the baseline stable boundary, the vehicle state distance is scaled by comprehensively considering the influence of the road adhesion coefficient, vehicle speed, and front wheel steering angle, and compared with the baseline boundary to generate a stability margin index. This quantifies the instability risk of the vehicle on low-adhesion roads and provides a meaningful parameter for the perception, decision-making, and control of intelligent vehicles on icy and snowy roads, improving the performance of intelligent vehicles driving on low-adhesion roads. This solves the problems of limitations in linear two-degree-of-freedom models, which make it difficult to accurately describe the vehicle's state on low-adhesion roads; the time-varying nature of vehicle model parameters due to changes in the adhesion coefficient on icy and snowy roads, which affects the magnitude of instability risk; and the complexity and difficulty in generalizing instability risk quantification methods.

[0134] Figure 9 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0135] The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0136] When the processor 902 executes the program, it implements the method for quantifying the risk of vehicle instability on low-adhesion road surfaces provided in the above embodiments.

[0137] Furthermore, the vehicle also includes:

[0138] Communication interface 903 is used for communication between memory 901 and processor 902.

[0139] The memory 901 is used to store computer programs that can run on the processor 902.

[0140] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0141] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0142] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0143] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0144] This embodiment also provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for quantifying the risk of vehicle instability on low-adhesion road surfaces.

[0145] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described method for quantifying the risk of vehicle instability on low-adhesion road surfaces.

[0146] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0147] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0148] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0149] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0150] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0151] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0152] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0153] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for quantifying the risk of vehicle instability on low adhesion road surfaces, characterized in that, Includes the following steps: Based on the preset Fiala tire model and dual-track two-degree-of-freedom vehicle model, the longitudinal force, lateral force and sideslip angle of the vehicle are calculated, and the front and rear sideslip angle phase plane diagram of the vehicle is determined with the front and rear sideslip angles of the vehicle as the horizontal and vertical axes. Based on the front and rear wheel slip angle phase plane diagram, the stable region and stable boundary of the front and rear wheel slip angle state of the vehicle under different vehicle states and different adhesion coefficients are determined. Based on the stable region and the stable boundary, the low-adhesion road surface reference stable region of the vehicle is determined, so as to obtain the reference stable boundary of the vehicle according to the low-adhesion road surface reference stable region, including: obtaining the typical vehicle speed, front wheel turning angle and characteristic adhesion coefficient of the low-adhesion road surface under the working conditions of wet road surface after snow. The coordinates of the two saddle points under the characteristic adhesion coefficient condition are obtained by using the front and rear wheel side slip angle phase plane diagrams. A circular region is defined with the origin of the coordinates of the two saddle points as the center and the distance from the two saddle points to the origin as the radius. The low-adhesion pavement reference stability region is obtained based on the circular region, and the reference stability boundary is obtained based on the boundary of the low-adhesion pavement reference stability region. Based on the influence of the reference stability boundary and the road adhesion coefficient on the stability boundary of the vehicle, as well as the influence of vehicle speed and front wheel steering angle on the stability boundary, the distance between the vehicle state and the origin is scaled to obtain the scaled vehicle state. The scaled vehicle state is then compared with the reference stability boundary to generate a vehicle stability margin index for low-adhesion road surfaces.

2. The method of claim 1, wherein, The formula for constructing the Fiala tire model is as follows: in, These represent the four wheels of the car: front left, front right, rear left, and rear right. This is the critical slip angle of the tire. For the lateral stiffness of the tire, This refers to the coefficient of adhesion between the ground and each tire. This represents the slip angle of the tire. This is the attenuation factor for the tire model.

3. The method of claim 1, wherein, The formula for the vehicle stability margin index of the low-adhesion road surface is: ， in, This is a vehicle stability margin index based on the minimum adhesion coefficient on low-adhesion road surfaces. The distance between the vehicle's front and rear side slip angles and the origin. The adhesion coefficient corresponding to the stable boundary. The minimum adhesion coefficient for four wheels, To stabilize the boundary radius.

4. A device for quantifying the risk of vehicle instability on a low adhesion road surface, characterized in that it comprises: include: The calculation module is used to calculate the longitudinal force, lateral force and sideslip angle of the vehicle based on the preset Fiala tire model and the dual-track two-degree-of-freedom vehicle model, and to determine the front and rear sideslip angle phase plane diagram of the vehicle with the front wheel sideslip angle and the rear wheel sideslip angle as the horizontal and vertical coordinate axes. A determination module is used to determine, based on the front and rear wheel slip angle phase plane diagrams, the stable regions and stable boundaries of the vehicle's front and rear wheel slip angle states under different vehicle conditions and different adhesion coefficients, and to determine the low-adhesion road surface reference stable region of the vehicle based on the stable regions and the stable boundaries, so as to obtain the reference stable boundary of the vehicle according to the low-adhesion road surface reference stable region. The determination module includes: The first acquisition unit is used to acquire typical vehicle speed, front wheel steering angle and characteristic adhesion coefficient of the wet road surface after snow for low adhesion road conditions. The second acquisition unit is used to acquire the coordinates of the two saddle points under the characteristic adhesion coefficient working condition through the front and rear wheel side slip angle phase plane diagram; The defined region unit is used to define a circular region with the origin of the coordinates of the two saddle points as the center and the distance from the two saddle points to the origin as the radius. The low-adhesion pavement reference stability region is obtained based on the circular region, and the reference stability boundary is obtained based on the boundary of the low-adhesion pavement reference stability region. The quantization module is used to scale the distance between the vehicle state and the origin based on the influence of the reference stability boundary and the road adhesion coefficient on the vehicle's stability boundary, as well as the influence of vehicle speed and front wheel steering angle on the stability boundary, to obtain a scaled vehicle state, and compare the scaled vehicle state with the reference stability boundary to generate a vehicle stability margin index for low-adhesion road surfaces.

5. The apparatus according to claim 4, characterized in that, The formula for constructing the Fiala tire model is as follows: in, These represent the four wheels of the car: front left, front right, rear left, and rear right. This is the critical slip angle of the tire. For the lateral stiffness of the tire, This refers to the coefficient of adhesion between the ground and each tire. This represents the slip angle of the tire. This is the attenuation factor for the tire model.

6. A vehicle characterized by comprising: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for quantifying the risk of vehicle instability on low-adhesion surfaces as described in any one of claims 1-3.

7. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method for quantifying the risk of vehicle instability on low-adhesion surfaces as described in any one of claims 1-3.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed to implement the method for quantifying vehicle instability risk on low-adhesion road surfaces as described in any one of claims 1-3.

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