A vehicle driving road surface characteristic parameter identification system and method

Abstract

The application discloses a vehicle driving road surface characteristic parameter identification system and method, and the method comprises the following steps: determining a dominant parameter representing road surface mechanical characteristics, measuring wheel force / torque and slip ratio, and estimating an estimated value of the road surface dominant parameter according to the aforementioned measurement values; then, the estimated value of the road surface dominant parameter is compared with data in a road surface parameter database to obtain a current road surface type. The application can quickly track road surface parameter changes caused by wheel motion state changes or other factors, and does not need to consider the influence of environmental factors such as light and weather, has fast calculation speed, high robustness, and is suitable for online analysis.

Description

Technical Field

[0001] This invention relates to the field of road surface recognition technology for engineering vehicles, and specifically to a system and method for identifying road surface characteristic parameters of vehicles. Background Technology

[0002] Engineering vehicles operate in complex environments with varied road conditions, frequently encountering soft, muddy surfaces in the wild. Due to the unstable structure and poor load-bearing capacity of soft surfaces, vehicle wheels sink significantly, increasing rolling resistance. Furthermore, the low shear strength of these surfaces makes them prone to shear deformation, leading to severe wheel slippage and even chassis bottoming out, causing vehicles to become trapped. To ensure vehicle handling stability and passability, it is essential to effectively identify road surface types and accurately determine their characteristic parameters. Methods for road surface parameter identification include Newton's iteration method, coupled solution method, and neural network method. However, Newton's iteration method requires at least three sets of experimental data as input to obtain a convergent solution, coupled solution method requires solving highly coupled mechanical equations, and neural network method requires a large amount of experimental data. All of these methods have limitations, necessitating a method for identifying road surface parameters that is unaffected by environmental conditions, computationally fast, and robust. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the deficiencies mentioned in the background art by providing a system and method for identifying vehicle road surface characteristic parameters, in order to improve the accuracy of the identification results and thus provide a basis for higher-level motion planning.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0005] A method for identifying road surface characteristic parameters of a vehicle includes the following steps:

[0006] Step 1: Determine the dominant parameters characterizing the current pavement mechanical properties;

[0007] Step 2: Measure the wheel force, wheel torque, and slip ratio of the vehicle traveling on the current road surface;

[0008] Step 3: Estimate the dominant road surface parameters based on the measurements in Step 2; then compare the estimated values ​​of the dominant road surface parameters with the data in the road surface parameter database to determine the current road surface type.

[0009] In a further proposed approach, the dominant parameters in step 1 refer to the basic mechanical properties of the road surface that have the greatest impact on the interaction force between the road surface and the wheels. These basic mechanical properties include two sets of parameters: bearing capacity parameters and shear capacity parameters.

[0010] In a further embodiment, the pressure-bearing characteristic parameters include cohesive modulus, internal friction angular modulus, and settlement index, specifically expressed as: pressure-bearing characteristic parameters

[0011] The shear characteristic parameters include cohesion, internal friction angle, and shear modulus, specifically expressed as: shear characteristic parameters

[0012] Where, k c It is the cohesive modulus. Let be the internal friction angular modulus, n be the sink index, and c be the cohesion. Let θ be the internal friction angle and K be the shear modulus.

[0013] In a further embodiment, the dominant parameter is determined by the following method:

[0014] Step 41: Depress the accelerator pedal to a certain position and keep it there, then calculate the current wheel slip ratio.

[0015] Step 42: Calculate the longitudinal force F of the wheel under the current wheel slip ratio according to the following formula. x ,

[0016]

[0017] Step 43: Obtain the relative sensitivity RS of wheel force to sinking index according to the following formula:

[0018]

[0019] Where r is the wheel radius; b is the wheel width; θ1 and θ2 are the wheel approach angle and departure angle, respectively, and τ x σ is the component of the shear stress on the wheel in the x direction; σ is the normal stress on the wheel; i is the characteristic parameter of each road surface; and y is the wheel force.

[0020] Step 44: Calculate and store the RS values ​​of each basic mechanical property parameter of the road surface in sequence, and sort them into groups. Select group G. Ⅰ and group G Ⅱ The parameter with the largest RS value is taken as the dominant parameter.

[0021] In a further step, the wheel forces and torques that need to be measured in step 2 include the wheel vertical force, wheel longitudinal force, and wheel driving torque.

[0022] The measuring devices include: gyroscope, torque sensor, speed sensor, and GPS.

[0023] Preferably, the specific methods for measuring wheel force / torque and slip ratio are as follows:

[0024] Step 61: Detect the vertical force on the wheel:

[0025] The longitudinal and lateral accelerations of the vehicle during movement are measured using a gyroscope, and then the vertical force on the wheels is calculated using the following formula:

[0026]

[0027] Among them, F zfl F zfr F zrl and F zrr ρ represents the vertical forces on the left front wheel, left rear wheel, right front wheel, and right rear wheel, respectively; m is the total vehicle mass; L is the wheelbase; a and b are the distances from the center of gravity to the front and rear axles, respectively; h is the height of the center of gravity; B f and B r These are the track widths of the front and rear axles, respectively; a x and a y These are longitudinal acceleration and lateral acceleration, respectively.

[0028] Step 62: Detect the longitudinal force of the wheel.

[0029] wheel longitudinal force F x It is calculated using the following formula:

[0030]

[0031] In the formula, r is the radius of the wheel;

[0032] Step 63: Detect wheel drive torque:

[0033] A torque sensor is installed on the wheel axle to measure the wheel driving torque T.

[0034] Step 64: Detect slip ratio:

[0035] The formula for calculating the wheel slip ratio s is as follows:

[0036]

[0037] In the formula, w w Let w be the wheel rotation speed, V be the vehicle speed, and w be the wheel rotation speed. w V can be measured by a speed sensor, and V can be measured by GPS.

[0038] A further approach is to estimate the dominant road surface parameters in step 3 as follows:

[0039] First, we approximate the expression for wheel stress by defining the following fitting function:

[0040]

[0041] Where p represents the stress on the wheel, including normal stress and shear stress; θ m The maximum stress angle;

[0042] Define the center approach angle θ of the wheel 1m and the departure angle θ 2m for:

[0043]

[0044] The coefficients of the above fitting function can then be determined by the stress values ​​at three points. i θ im and θ m They are respectively:

[0045]

[0046] Where, λ pi =p m / 2p im i = 1, 2;

[0047] Will and Substituting into the formula yields a unified expression:

[0048]

[0049] Define F = [F z ,F x ] T And X = [σ, τ] T ,but:

[0050]

[0051] in,

[0052] The final simplified analytical model of wheel-land interaction is as follows:

[0053] F = rb(F1 - F2)X m

[0054] Where X m =[σ m ,τ m ] T ;

[0055] Meanwhile, the driving torque T can be simplified to:

[0056] T = r 2 b(T D1 -T D2 )τ m

[0057] in,

[0058] Then, the dominant parameters are estimated using Kalman filtering according to the following equation:

[0059]

[0060] Where f(·) is the system's state function; h(·) is the measurement function; W is the process noise; and V is the observation noise.

[0061] Another objective of this invention is to provide a vehicle road surface characteristic parameter identification system, which includes the following modules:

[0062] Dominant parameter identification module: used to calculate and analyze to determine the dominant parameters that can characterize the road surface characteristics, and send the results to the estimator module;

[0063] Sensor module: Used to measure wheel force, wheel torque, and slip ratio, and sends the measured values ​​to the estimator module;

[0064] Estimator module: Used to receive values ​​from the dominant parameter identification module and sensor module, and identify the magnitude of the dominant characteristic parameters of the unknown road surface;

[0065] Processor: Used to execute computational code;

[0066] Memory: Used to store calculation code and parameter identification results.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] This invention employs a strong tracking unscented Kalman filter to estimate road surface characteristic parameters of vehicle travel, avoiding the direct solution of highly coupled wheel-ground interaction equations and simplifying complex stress integration calculations, particularly for road surface parameters with time-varying characteristics.

[0069] This invention can quickly track changes in road surface parameters caused by changes in wheel motion or other factors, without considering the influence of lighting or weather conditions. It has a fast calculation speed, strong robustness, and is suitable for online analysis. Attached image description:

[0070] Figure 1 This is a block diagram of the road surface characteristic parameter identification system of the present invention;

[0071] Figure 2 This is a steady-state steering diagram for engineering vehicles.

[0072] Figure 3 This is an estimate of the subsidence index;

[0073] Figure 4 This is an estimated value for the internal friction angle. Detailed Implementation

[0074] The technical solutions in 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] Example 1:

[0076] A method for identifying road surface characteristic parameters of vehicles follows these steps:

[0077] Step 1: Determine the dominant parameters characterizing the mechanical properties of the road surface.

[0078] According to the theory of wheel-ground interaction, the mechanical properties of road surface can be expressed as bearing characteristics and shear characteristics;

[0079] The pressure-bearing characteristics can be expressed by the following formula (1):

[0080]

[0081] In equation (1), σ is the normal stress; k c It is the cohesive modulus; θ is the internal friction angular modulus; n is the sinking index; r is the wheel radius; b is the wheel width; θ1 and θ2 are the wheel approach and departure angles, respectively; θ m The maximum stress angle is denoted as .

[0082] The shear property can be represented by the following formula (2):

[0083]

[0084] In equation (2), τ is the shear stress; c is the cohesive force; θ is the internal friction angle; K is the shear modulus.

[0085] Equations (1) and (2) contain the basic characteristic parameters and bearing capacity parameters of the road surface. and shear property parameters Table 1 below summarizes the range of values ​​for common road surface basic characteristic parameters. The fixed values ​​in Table 1 refer to the values ​​of the parameter when performing sensitivity analysis calculations for other road surface parameters.

[0086]

[0087] Taking the sinking index n as an example, let n vary within [0.11, 1.1], and take the fixed values ​​in Table 1 for other parameters. The wheel radius r = 0.562m and the wheel width b = 0.315m. Substituting these values ​​into equation (3) will allow us to calculate the value of the longitudinal force of the wheel under the current slip ratio.

[0088]

[0089] By taking n values ​​of 0.5, 0.7, 0.9, and 1.1 respectively, and controlling the wheel slip ratio s = 0.2, the magnitude of the longitudinal force of the wheel under these four sinkage indices can be calculated. Substituting the results into equation (4) yields the relative sensitivity RS of the wheel force to the sinkage index:

[0090]

[0091] The calculation process is implemented by a processor, and the relevant code is stored in memory. The processor groups and sorts the parameters RS according to their magnitudes, and then selects G from each group. Ⅰ and G Ⅱ The maximum value of RS corresponds to the pavement parameter P to be estimated. bear and P shear .

[0092] Step 2: Measure wheel force / torque and slip ratio.

[0093] The wheel forces and torques that need to be measured include: wheel vertical force, wheel longitudinal force, and wheel driving torque. The measuring equipment includes: gyroscope, torque sensor, speed sensor, and GPS.

[0094] Substituting the longitudinal acceleration and lateral acceleration values ​​of the vehicle body collected by the gyroscope into equation (5) yields the vertical force of the wheel:

[0095]

[0096] The wheel drive torque can be measured using a torque sensor; based on the collected drive torque, the longitudinal force of the wheel can be calculated using equation (6).

[0097]

[0098] Substituting the wheel speed measured by the speed sensor and the vehicle speed measured by GPS into equation (7), the wheel slip ratio can be calculated:

[0099]

[0100] Step 3: Estimate the dominant road surface parameters based on the measurements in Step 2; then compare the estimated values ​​of the dominant road surface parameters with the data in the road surface parameter database to determine the current road surface type.

[0101] This embodiment uses a strong tracking unscented Kalman filter to estimate the dominant parameters. The measured values ​​in step 2 are used as prior estimates of the filter. The specific estimation method is as follows:

[0102] First, the wheel stress is approximated using the trigonometric method, and the following fitting function is defined:

[0103]

[0104] Where p represents the stress on the wheel.

[0105] Define the center approach angle θ of the wheel 1m and the departure angle θ 2m for:

[0106]

[0107] The coefficients in equation (8) can be determined by the stress values ​​at three points: θ i θ im and θ m They are respectively:

[0108]

[0109] Where, λ pi =p m / 2p im i = 1, 2.

[0110] Will and Substituting into the formula yields a unified expression:

[0111]

[0112] Define F = [F z ,F x ] T And X = [σ, τ] T Then we have:

[0113]

[0114] in,

[0115] Substituting equation (11) into equation (12), we can obtain the final simplified analytical model of wheel-land interaction:

[0116] F = rb(F1 - F2)X m (13)

[0117] Where X m =[σ m,τ m ] T .

[0118] Meanwhile, the driving torque T can be simplified to:

[0119]

[0120] in,

[0121] Figure 2 For the steady-state steering model of the engineering vehicle, the vehicle speed v is defined as:

[0122] v = v f (15)

[0123] Coordinates of the midpoint of the front axle O f for:

[0124]

[0125] Where, θ f v is the heading angle of the front axle. f This represents the longitudinal speed of the front frame.

[0126] The front axle pose can be expressed as X b =[x f ,y f ,θ f ,δ] T ,have:

[0127]

[0128] In the formula, δ is the vehicle's bend angle; l f The distance from the midpoint of the front axle to the hinge point H; r This is the distance from the midpoint of the rear axle to the hinge point H;

[0129] Define the augmented state vector X = [x f ,y f ,θ f ,δ,P bear ,P shear ] T ,Right now:

[0130]

[0131] Then we have the following equation:

[0132]

[0133] Where f(·) is the system state function, which can be obtained by discretizing the state equation using the forward Euler method; h(·) is the measurement function. W is the process noise, and V is the observation noise. They are uncorrelated Gaussian white noise with a mean of 0, and their covariances are respectively:

[0134]

[0135] The UKF implementation steps for the state variable at different times k are as follows:

[0136] (1) Initialize the filter initial value

[0137]

[0138] In the formula, P0 is the initial predicted value of the state vector; P0 is the initial covariance matrix of the state vector.

[0139] (2) Time update. Select 2N+1 Sigma points:

[0140]

[0141] In the formula, N is the dimension of the state vector; χ is the scaling parameter. ξ i,k-1 The predicted value γ i,k|k-1 for:

[0142] γ i,k|k-1 =f(ξ i,k-1 U k-1 ) (twenty three)

[0143] Prior state estimate at time k-1 for:

[0144]

[0145] In the formula, W i Weighting coefficients:

[0146]

[0147] The corresponding prior covariance matrix is:

[0148]

[0149] (3) Measurement update

[0150] The new Sigma point set is calculated based on the prior state prediction values:

[0151]

[0152] The corresponding output value κi,k|k-1 for:

[0153] κ i,k|k-1 =h(ξ′) i,k-1 U k-1 (28)

[0154] At time k-1, the observed value is obtained by weighted summation. covariance P z,k and cross covariance P xz,k They are respectively:

[0155]

[0156]

[0157]

[0158] (4) Filtering update

[0159] Calculate the filter gain matrix K k for:

[0160]

[0161] The posterior state variables and covariances are as follows:

[0162]

[0163]

[0164] Traditional unscented Kalman filters (UKFs) achieve good recognition accuracy and performance in nonlinear systems with time-invariant parameters. However, road surface characteristics in wheel-ground interactions can vary significantly due to changes in terrain type and wheel motion state; that is, road surface parameters are time-varying. Once these parameters change, the filtering accuracy of traditional UKFs drops sharply and may even diverge. To address this issue, strong tracking theory (STT) is introduced into UKFs, and a strong tracking unscented Kalman filter (STUKF) is designed to estimate the dominant road surface parameters.

[0165] In STUKF, the prediction error covariance matrix is ​​corrected by constructing a fading factor matrix:

[0166]

[0167] The fading factor is solved using the following formula:

[0168]

[0169] In equation (36), This is an estimate of the covariance matrix of the new sequence. It can be solved using the windowing method.

[0170]

[0171] In equation (36), Φ k and H k These are the Jacobi matrices for the state function and the measurement function, respectively.

[0172]

[0173]

[0174] Because the Jacobi matrices of the two nonlinear functions in equations (38) and (39) are computationally intensive and complex, they need to be replaced by equivalent ones during the identification of road surface parameters:

[0175]

[0176] That is, Φ k It is transformed into an identity matrix of the corresponding dimension.

[0177] The prior covariance matrix is ​​denoted as P k∣k-1 The cross-covariance matrix is ​​denoted as P. xz,k Then it can be expressed as follows:

[0178]

[0179]

[0180] H k It can be given by the formula:

[0181] H k T =P k∣k-1 -1 P xz,k (42)

[0182] The estimated values ​​of the road surface's dominant parameters can be obtained by following the steps described above, and the code of the relevant formulas is stored in memory.

[0183] Example 2:

[0184] As an application embodiment of the present invention, Table 2 shows the calculation results of the relative sensitivity of various road surface parameters under the following conditions: wheel load W = 50 kg, slip ratio s = 0.25, wheel radius r = 0.562 m, and wheel width b = 0.315 m.

[0185]

[0186] As shown in Table 2, among the pressure-bearing characteristic parameters, the settlement index has the highest relative sensitivity, reaching 3.56, and plays a dominant role in pressure-bearing characteristics. Similarly, among the shear characteristic parameters, the internal friction angle has the highest relative sensitivity and plays a dominant role in shear characteristics. Therefore, these two parameters will be estimated as the dominant parameters.

[0187] Figure 3 , Figure 4 This provides the estimation results of the dominant parameters for a specific road surface. (Observation) Figure 3 and Figure 4 The estimated value of the subsidence index fluctuates around 1.01, and the estimated value of the internal friction angle is about 28.07. Compared with the results in the pavement parameter database, it is closest to the typical value of sandy soil. Therefore, it can be determined that the example pavement type is sandy soil pavement.

[0188] Example 3:

[0189] See Figure 1 A vehicle road surface characteristic parameter identification system, comprising the following modules:

[0190] Dominant parameter identification module: used to calculate and analyze to determine the dominant parameters that can characterize the road surface characteristics, and send the results to the estimator module;

[0191] Sensor module: Used to measure wheel force, wheel torque, and slip ratio, and sends the measured values ​​to the estimator module;

[0192] Estimator module: Used to receive values ​​from the dominant parameter identification module and sensor module, and identify the magnitude of the dominant characteristic parameters of the unknown road surface;

[0193] Processor: Used to execute computational code;

[0194] Memory: Used to store calculation code and parameter identification results.

[0195] Although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0196] Therefore, the above description is only a preferred embodiment of this application and is not intended to limit the scope of this application; that is, all equivalent modifications made in accordance with the scope of the claims of this application shall be within the protection scope of the claims of this application.

Claims

1. A method for identifying road surface characteristic parameters of vehicles, characterized in that: Includes the following steps: Step 1: Determine the dominant parameters characterizing the current pavement mechanical properties; Step 2: Measure the wheel force, wheel torque, and slip ratio of the vehicle traveling on the current road surface; Step 3: Estimate the estimated values ​​of the dominant parameters of the road surface based on the measured values ​​in Step 2; Then, the estimated values ​​of the dominant road surface parameters are compared with the data in the road surface parameter database to determine the current road surface type; The dominant parameters in step 1 refer to the basic mechanical properties of the road surface that have the greatest impact on the interaction force between the road surface and the wheels. These basic mechanical properties include two sets of parameters: bearing capacity parameters and bearing capacity parameters. and shear property parameters ; The dominant parameter is determined by the following method: Step 41: Depress the accelerator pedal to a certain position and keep it there, then calculate the current wheel slip ratio. Step 42: Calculate the longitudinal force of the wheel under the current wheel slip ratio using the following formula. , Step 43: Obtain the relative sensitivity of wheel force to basic road surface mechanical property parameters according to the following formula. RS : in r The radius of the wheel; b This refers to the width of the wheel; and These are the wheel approach angle and departure angle, respectively. The shear stress on the wheel x Components in direction; The normal stress on the wheel, i For the characteristic parameters of each road surface, y For wheel force; Step 44: Calculate and store the basic mechanical property parameters of each road surface in sequence. RS Values ​​are then grouped and sorted, and groups are selected. and group middle RS The parameter with the largest value is taken as the dominant parameter.

2. The method for identifying vehicle road surface characteristic parameters according to claim 1, characterized in that: The pressure-bearing characteristic parameters include cohesive modulus, internal friction angular modulus, and settlement index, specifically expressed as: pressure-bearing characteristic parameters ; The shear characteristic parameters include cohesion, internal friction angle, and shear modulus, specifically expressed as: shear characteristic parameters ; in, It is the cohesive modulus. The internal friction angle modulus, n The subsidence index, c For cohesion, φ It is the internal friction angle. K This is the shear modulus.

3. The method for identifying vehicle road surface characteristic parameters according to claim 1, characterized in that: The wheel forces and torques that need to be measured in step 2 include the vertical force, longitudinal force, and driving torque of the wheel. The measuring devices include: gyroscope, torque sensor, speed sensor, and GPS.

4. The method for identifying vehicle road surface characteristic parameters according to claim 3, characterized in that: The methods for measuring wheel force / torque and slip ratio are as follows: Step 61: Detect the vertical force on the wheel: The longitudinal and lateral accelerations of the vehicle during movement are measured using a gyroscope, and then the vertical force on the wheels is calculated using the following formula: in, , , and These are the vertical forces on the left front wheel, left rear wheel, right front wheel, and right rear wheel, respectively. m For the overall vehicle weight; L Wheelbase; a and b These are the distances from the center of mass to the front and rear axes, respectively. h The height of the center of mass; B f and B r These are the track widths of the front and rear axles, respectively. a x and a y These are longitudinal acceleration and lateral acceleration, respectively. Step 62: Detect the longitudinal force of the wheel. wheel longitudinal force F x It is calculated using the following formula: In the formula, r The radius of the wheel; Step 63: Detect wheel drive torque: A torque sensor is mounted on the wheel axle to measure the wheel driving torque. T； Step 64: Detect slip ratio: Wheel slip rate s The calculation formula is as follows: In the formula, w w For wheel speed, V For vehicle speed, where, w w It can be measured by a speed sensor. V It can be measured by GPS.

5. The method for identifying vehicle road surface characteristic parameters according to claim 1, characterized in that: The estimation method for the dominant road surface parameters in step 3 is as follows: First, the expression for wheel stress is approximated, and the following fitting function is defined: in, p This represents the stress experienced by the wheel, including normal stress and shear stress; θ m The maximum stress angle; Define the center approach angle of the wheel θ 1m and the middle departure angle θ 2m for: The coefficients of the above fitting function can then be determined by the stress values ​​at the three points. θ i , θ im as well as θ m They are respectively: in, ; Will , and Substituting into the formula yields a unified expression: definition as well as ,but: in, The final simplified analytical model of wheel-land interaction is as follows: in ; At the same time, driving torque T It can be simplified to: in, ; Then, the dominant parameters are estimated using Kalman filtering according to the following equation: in, Let this be the system's state function; For measurement functions, W It's process noise. V To observe noise.

6. A system for identifying road surface characteristic parameters of a vehicle, characterized in that: Includes the following modules: Dominant Parameter Identification Module: Used to calculate and analyze to determine the dominant parameters that can characterize the pavement characteristics, and send the results to the estimator module; Sensor module: Used to measure wheel force, wheel torque, and slip ratio, and sends the measured values ​​to the estimator module; Estimator module: Used to receive values ​​from the dominant parameter identification module and sensor module, and identify the magnitude of the dominant characteristic parameters of the unknown road surface; Processor: Used to execute computational code; Memory: Used to store calculation code and parameter identification results; The dominant parameters refer to the basic mechanical property parameters of the road surface that have the greatest impact on the interaction force between the road surface and the wheels. These basic mechanical property parameters include two sets of parameters: bearing capacity parameters and bearing capacity parameters. and shear property parameters ; The dominant parameter is determined by the following method: Step 41: Depress the accelerator pedal to a certain position and keep it there, then calculate the current wheel slip ratio. Step 42: Calculate the longitudinal force of the wheel under the current wheel slip ratio using the following formula. , Step 43: Obtain the relative sensitivity of wheel force to basic road surface mechanical property parameters according to the following formula. RS : in r The radius of the wheel; b This refers to the width of the wheel; and These are the wheel approach angle and departure angle, respectively. The shear stress on the wheel x Components in direction; The normal stress on the wheel, i For the characteristic parameters of each road surface, y For wheel force; Step 44: Calculate and store the basic mechanical property parameters of each road surface in sequence. RS Values ​​are then grouped and sorted, and groups are selected. and group middle RS The parameter with the largest value is taken as the dominant parameter.

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