A method and system for estimating peak adhesion coefficient of a road surface under a non-structural road surface

Abstract

The present application relates to the road feature recognition technical field in vehicle engineering, disclose a kind of under non-structural road surface road surface peak adhesion coefficient estimation method and system, the method is through commonly used vehicle-mounted sensor collection vehicle pose and wheel rotation angle and other vehicle motion response signal, wheel vertical load and other motion parameters are calculated in traditional vehicle model by introducing equivalent suspension model;The above parameters are used as input, the coefficient matrix of extended Kalman filter observer is calculated by Dugoff tire model;Then considering the influence of gravity, the vehicle body acceleration is corrected in combination with vehicle pose data, and it is used as the observation signal to estimate the road surface peak adhesion coefficient.The present application improves the precision of vehicle dynamics model under non-structural road surface, and solves the problem of poor estimation effect of existing algorithm under uneven road surface and large slope conditions.

Description

Technical Field

[0001] This invention relates to the field of road surface feature recognition technology in vehicle engineering, specifically to a method for estimating the peak adhesion coefficient of unstructured road surfaces. Background Technology

[0002] Estimating the peak coefficient of adhesion (PFOA) is crucial for vehicle safety control. Current intelligent safety control algorithms often achieve active safety control by adjusting the force between the tires and the road surface. Since the maximum force that the road surface can provide is limited by the PFOA, most algorithms control vehicle motion based on the estimated PFOA. However, current estimation algorithms are primarily used for structured road surfaces. On unstructured road surfaces, unevenness and slope conditions significantly increase the estimation difficulty, leading to poor stability and low accuracy in existing methods.

[0003] Currently, the estimation of peak road adhesion coefficient (POC) is mainly divided into two categories: cause-based methods and effect-based methods. Cause-based methods utilize dedicated measurement equipment, such as optical or ultrasonic sensors, to detect road conditions and estimate the POC. While offering high accuracy, these dedicated measurement devices are expensive and significantly affected by weather conditions such as fog and snow. Effect-based methods estimate the POC by measuring and analyzing the vehicle response caused by the road surface, such as estimation methods using the adhesion coefficient-slip ratio curve and methods utilizing the relationship between the self-aligning torque and tire slip angle. These methods only require commonly used onboard sensors, such as wheel speed sensors and posture sensors, but their application is limited, and they cannot estimate the POC under coupled longitudinal and lateral forces. To address the limitation of applicable conditions, some researchers have proposed estimating the POC based on vehicle dynamics models and Kalman filtering principles. This approach can accurately estimate the POC under coupled longitudinal and lateral forces and also has the advantages of a simple solution process and fast convergence speed. Despite the advantages of estimation methods based on Kalman filtering, their application in unstructured pavements with uneven surfaces and steep gradients still faces some challenges.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] (1) Existing algorithms treat the vehicle body and wheels as a rigid connection, so the calculated tire vertical load has a large error under uneven road conditions, resulting in inaccurate estimation results.

[0006] (2) Existing algorithms often ignore the influence of gravity on vehicle acceleration and directly input vehicle acceleration as an observation into the observer, making it impossible for the algorithm to accurately estimate on roads with a certain slope. Summary of the Invention

[0007] To address the aforementioned technical problems with existing methods for estimating peak road adhesion coefficient (POC), this invention proposes a method for estimating POC on unstructured road surfaces. The method introduced in this invention incorporates an equivalent suspension model to optimize the calculation of wheel vertical loads and combines vehicle pose data to correct vehicle acceleration, thereby improving the accuracy of the vehicle dynamics model on unstructured road surfaces and resolving the problem of poor estimation performance of existing algorithms under uneven road surfaces and steep gradients.

[0008] This invention is implemented as follows: a method for estimating the peak adhesion coefficient of a road surface under unstructured pavement conditions, comprising the following steps:

[0009] Step 1: Acquire vehicle speed, front wheel steering angle, and yaw rate signals using common sensors installed on the vehicle, and calculate the wheel slip angle α for each wheel. ij .

[0010] Step 2: Calculate the velocity v along the longitudinal axis of the lower wheel center of the wheel system. wx,ij Then, the wheel speeds of each wheel are obtained through wheel speed sensors, and the slip ratio λ of each wheel is further calculated. ij .

[0011] Step 3: Calculate the vertical static load on the wheels using the vehicle's parameters.

[0012] Step 4: Acquire the vertical acceleration and vertical displacement signals of the wheels using accelerometers and angle sensors, and process them to obtain the vertical velocity signal. Then, combine the displacement and velocity signals of the vehicle's center of gravity with the vehicle's attitude to calculate the vertical dynamic load on the wheels.

[0013] Step 5: Add the calculated vertical static load and dynamic load of the wheel to obtain the current vertical load of the wheel.

[0014] Step 6: Calculate the Dugoff normalized force using the wheel slip angle, slip ratio, and vertical load.

[0015] Step 7: Calculate the acceleration component caused by tire force by combining vehicle body posture and longitudinal acceleration.

[0016] Step 8: Input the calculated normalized force, the processed acceleration signal, and the acquired wheel rotation angle into the extended Kalman filter module to estimate the peak adhesion coefficient of the road surface.

[0017] Another objective of this invention is to provide a system for estimating the peak adhesion coefficient of a road surface under unstructured pavement conditions, comprising:

[0018] The wheel slip angle module is used to acquire vehicle speed signals, front wheel steering angle signals, and yaw rate signals from common sensors installed on the vehicle, and to calculate the slip angle α of each wheel. ij ;

[0019] The wheel slip ratio module is used to calculate the velocity v in the longitudinal direction of the wheel center of the wheel system. wx,ij Then, the wheel speeds of each wheel are obtained through wheel speed sensors, and the slip ratio λ of each wheel is further calculated. ij ;

[0020] The wheel vertical static load module is used to calculate the wheel vertical static load based on the vehicle's parameters.

[0021] The wheel vertical dynamic load module is used to acquire the wheel vertical acceleration and vertical displacement signals through accelerometers and angle sensors, process them to obtain the vertical velocity signal, and then combine the displacement and velocity signals of the vehicle's center of gravity and the vehicle's attitude to calculate the wheel vertical dynamic load.

[0022] The wheel current vertical load module is used to add the calculated wheel vertical static load and dynamic load to obtain the wheel current vertical load;

[0023] The wheel normalized force module is used to calculate Dugoff normalized force based on wheel slip angle, slip ratio, and vertical load.

[0024] The wheel acceleration component module is used to calculate the acceleration component caused by tire force by combining vehicle attitude and longitudinal acceleration;

[0025] The Kalman filter module is used to input the calculated normalized force, the processed acceleration signal, and the acquired wheel rotation angle into the extended Kalman filter module to estimate the peak adhesion coefficient of the road surface.

[0026] Another object of the present invention is to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform any of the steps of the above-described improved method for estimating peak adhesion coefficient of pavement under unstructured pavement.

[0027] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform any of the steps of the above-described improved method for estimating the peak adhesion coefficient of pavement under unstructured pavement.

[0028] Another objective of this invention is to provide an information data processing terminal for implementing the aforementioned improved peak adhesion coefficient estimation system for unstructured pavements.

[0029] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0030] First, addressing the technical problems existing in the prior art and the difficulty in solving them, this paper closely analyzes, in conjunction with the technical solution to be protected by this invention and the results and data obtained during the research and development process, how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about by solving these problems. The specific description is as follows:

[0031] This invention collects vehicle motion response signals such as vehicle pose and wheel angle using commonly used onboard sensors. An equivalent suspension model is introduced into the traditional vehicle model to calculate motion parameters such as wheel vertical load. These parameters are used as input to calculate the coefficient matrix of an extended Kalman filter observer using a Dugoff tire model. Then, considering the influence of gravity, the vehicle body acceleration is corrected based on the vehicle pose data, and this correction is used as the observer's signal to estimate the peak road adhesion coefficient. The method provided by this invention, by introducing an equivalent suspension model to optimize the calculation of wheel vertical load and by correcting vehicle acceleration based on vehicle pose data, improves the accuracy of vehicle dynamics models on unstructured road surfaces and solves the problem of poor estimation performance of existing algorithms under uneven road surfaces and steep slopes. Overall, this method achieves the estimation of the peak road adhesion coefficient on unstructured road surfaces.

[0032] Second, considering the technical solution as a whole or from a product perspective, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows:

[0033] The method proposed in this invention can accurately estimate the peak adhesion coefficient of unstructured road surfaces, such as mountain and off-road surfaces. The method maintains high estimation accuracy even on uneven and steep road surfaces, helping to improve the vehicle's perception of complex environments during operation, further leveraging the advantages of intelligent vehicle control algorithms, and ensuring driving safety.

[0034] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0035] (1) The expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0036] With the advancement of technology and social development, the number of cars and the mileage of roads are constantly increasing, bringing great convenience to people's lives. However, as people's demands for the high-speed and complex-environment driving capabilities of vehicles continue to rise, safety issues are becoming increasingly prominent. Currently proposed intelligent safety control algorithms for automobiles can effectively improve driving safety, but most of them rely on the estimation of the peak road adhesion coefficient. The peak road adhesion coefficient estimation method provided in this invention is applicable to unstructured road surfaces, effectively improving the vehicle's perception capabilities in complex environments. It provides effective environmental information for intelligent vehicle safety control strategies, improves vehicle driving safety, reduces loss of life and property, and thus generates significant social and commercial benefits.

[0037] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0038] Currently, methods for estimating the peak coefficient of adhesion (PFAD) of road surfaces based on vehicle dynamics models and Kalman filtering principles are a hot research topic. This algorithm can accurately estimate PFAD under coupled longitudinal and lateral forces, and also boasts advantages such as simple solution process and fast convergence speed. Despite these advantages, the estimation method based on Kalman filtering principles suffers from poor accuracy on unstructured road surfaces with uneven surfaces and steep gradients. The method provided in this invention, by introducing an equivalent suspension model and optimizing the vehicle dynamics model, achieves high accuracy in estimating PFAD on unstructured road surfaces, filling a gap in domestic and international research on estimating PFAD for vehicles in complex environments.

[0039] (3) Whether the technical solution of the present invention solves the technical problem that people have long wanted to solve but have never been able to solve successfully:

[0040] Accurate estimation of peak road adhesion coefficient (POC) has always been a key aspect of vehicle driving safety. Estimating POC on unstructured road surfaces, which exhibit greater complexity and randomness, presents even greater challenges. The method provided in this invention introduces an equivalent suspension model to optimize the calculation of wheel vertical loads and incorporates vehicle posture data to correct vehicle acceleration. This improves the accuracy of vehicle dynamics models on unstructured road surfaces and addresses the problem of poor estimation performance of existing algorithms under uneven road surfaces and steep gradients. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method for estimating the peak adhesion coefficient of a road surface provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of a vehicle model provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of a vehicle roll motion model provided in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the vehicle pitch motion model provided in an embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of the force on a wheel provided in an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the simulation experiment results provided in the embodiments of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanatory description of the embodiments that expand upon the technical solutions of the claims.

[0049] This invention provides a method for estimating the peak adhesion coefficient of a road surface under unstructured road conditions, specifically for estimating the peak adhesion coefficient of a road surface under braking scenarios on uneven road surfaces with a certain slope.

[0050] Vehicle speed, front wheel steering angle, and yaw rate signals are acquired using sensors installed on the vehicle, and the wheel slip angle α of each wheel is calculated. ij In the following, ij = fl, fr, rl, rr represent four wheels.

[0051]

[0052] In equation (1), v x and v y These are the vehicle's longitudinal and lateral velocities, ω z Let δ be the vehicle's yaw rate, δ be the front wheel steering angle, and B be the yaw rate. f and B r For the vehicle's front wheel track and rear wheel track, L f and L r This is the distance from the center of mass to the front and rear axles.

[0053] Calculate the velocity v in the longitudinal direction of the lower wheel center of the wheel system. wx,ij :

[0054]

[0055] The wheel speeds of each wheel are then obtained using wheel speed sensors, and the slip ratio λ of each wheel is further calculated. ij :

[0056]

[0057] In equation (3), ω w,ij For the rotational speed of each wheel, R W The radius is the wheel radius.

[0058] The overall wheel vertical load is determined by calculating the vertical static and dynamic loads of the wheels during vehicle operation. First, the wheel vertical static load F is calculated using the vehicle parameters. w_ij :

[0059]

[0060]

[0061] In equation (4), m b For the sprung mass of the vehicle, m W Let g be the mass of the wheel and g be the acceleration due to gravity.

[0062] Based on the vehicle roll and pitch motion model, the suspension displacement and velocity are analyzed, such as Figure 2 , 3 As shown. The suspension displacement z of each wheel is calculated using the vehicle body roll and pitch angles, as well as the center of gravity displacement. s_ij :

[0063]

[0064] In equation (5), z b φ is the vertical displacement of the vehicle's center of gravity, φ is the roll angle of the vehicle body, and θ is the pitch angle of the vehicle body.

[0065] The suspension speed is obtained by differentiating the suspension displacement.

[0066]

[0067] Vertical acceleration and displacement signals of the wheels are acquired using accelerometers and angle sensors, and the vertical velocity signal is obtained through processing. The dynamic suspension force F is then calculated. dzs_ij :

[0068]

[0069] In equation (7), z w_ij For the vertical displacement of each wheel, Let k be the vertical velocity of each wheel. f and k r These are the equivalent stiffness coefficients of the suspension, c f and c r These are the equivalent damping coefficients of the suspension.

[0070] Analyze the forces acting on the wheel, such as Figure 4As shown, dynamic suspension force and wheel acceleration are used to determine the dynamic suspension force and wheel acceleration. The dynamic tire vertical load F can then be calculated. dw_ij :

[0071]

[0072] The current vertical load of the wheel is obtained by adding the calculated vertical static load and dynamic load of the wheel.

[0073] The Dugoff normalized force is calculated using wheel slip angle, slip ratio, and vertical load.

[0074]

[0075] Among them, C x and C y Here, λ represents the tire's longitudinal stiffness and lateral stiffness, α represents the slip ratio, α represents the tire's slip angle, and L is a nonlinear characteristic parameter used to express tire slip, which is affected by driving speed. Its expression is as follows:

[0076]

[0077]

[0078] In the formula, ε is the speed influence factor, which is used to describe the effect of slip speed on tire force.

[0079] The acceleration component caused by tire force is calculated by combining the vehicle body posture and longitudinal acceleration.

[0080]

[0081] In equation (12), a x and a y These are the longitudinal acceleration and lateral acceleration of the vehicle body, respectively. Fx Let a be the longitudinal acceleration component caused by tire force. Fy This represents the lateral acceleration component caused by tire force.

[0082] The calculated normalized force, the processed acceleration signal, and the acquired wheel rotation angle are input into the extended Kalman filter module to estimate the peak adhesion coefficient of the road surface.

[0083] The state equations required for extended Kalman filtering are written based on the road surface adhesion coefficient.

[0084]

[0085] In the formula, w(t) represents the process noise, with a standard deviation of Q and μ. ij These are the four-wheel road adhesion coefficients.

[0086] Based on the vehicle dynamics equations, list the observation equations in matrix form:

[0087]

[0088] in,

[0089]

[0090] In equation (14), v(t) represents process noise with a standard deviation of R; F xij 0 and F yij 0 These are the longitudinal normalized force and the lateral normalized force of the tire, respectively.

[0091] The detailed iterative process of the extended Kalman filter is as follows:

[0092] set up P is the state estimate at time k-1. k-1 The covariance at time k-1 is expressed by a Taylor expansion. Linearizing the state equations, we have:

[0093]

[0094] In equation (16), and make

[0095] Similarly, linearize the measurement equation:

[0096]

[0097] In equation (17),

[0098] Therefore, the predicted values ​​of the prior state variables are:

[0099]

[0100] Since w is process noise that follows a zero-mean Gaussian distribution with a mean of 0, and the expected value of the state estimate is the true value, we have:

[0101]

[0102] The prior covariance is:

[0103]

[0104] Similarly, the predicted values ​​and covariance matrix of the measurements are:

[0105]

[0106] The cross-covariance matrix between state and measurement is:

[0107]

[0108] The state gain matrix is:

[0109]

[0110] The state estimate at time k is:

[0111]

[0112] The covariance matrix of the state estimate is:

[0113]

[0114] This allows us to obtain the state estimate and covariance matrix at time k, and the cycle continues until time k+1.

[0115] To demonstrate the inventiveness and technical value of the technical solution of this invention, this section provides specific product or related technology application examples of the technical solution claimed.

[0116] This invention applies the peak adhesion coefficient estimation method for unstructured pavements to off-road pavements. Off-road pavements are typical unstructured pavements, exhibiting greater randomness and complexity compared to structured pavements, with more pronounced unevenness and steep gradients. Therefore, studying off-road pavements is of great significance for researching peak adhesion coefficient estimation for unstructured pavements.

[0117] For a typical off-road surface, when a vehicle passes over the surface, common sensors installed on the vehicle acquire vehicle speed signals, front wheel steering angle signals, and yaw rate signals. Accelerometers and angle sensors acquire wheel vertical acceleration and vertical displacement signals, and wheel speed sensors acquire the wheel speeds of each wheel.

[0118] The wheel slip angle and slip ratio are calculated from the signals collected by the sensors. Next, the vertical load on the wheel under off-road conditions is calculated. First, the static vertical load is calculated using the vehicle's parameters; then, the dynamic vertical load is calculated by combining the vehicle's center of gravity displacement, velocity signals, and vehicle attitude. Finally, the static and dynamic vertical loads are added together to obtain the current vertical load on the wheel. Based on the Dugoff tire model, the normalized force is calculated from the wheel slip angle, slip ratio, and vertical load. The acceleration component caused by the tire force is calculated by combining the vehicle attitude and longitudinal acceleration. Finally, the calculated normalized force, the processed acceleration signal, and the collected wheel rotation angle are input into the extended Kalman filter module to estimate the peak adhesion coefficient of the off-road surface.

[0119] By driving on off-road surfaces for a period of time, the peak adhesion coefficient of the road surface is estimated in real time based on the collected signals, resulting in a convergent curve. The estimation results are unaffected by uneven road conditions and are relatively stable. The method proposed in this invention is applicable to the estimation of peak adhesion coefficient in off-road environments and exhibits good stability and accuracy. It is of great significance for improving vehicle perception capabilities in complex environments and enhancing vehicle driving safety.

[0120] The embodiments of the present invention have achieved some positive results during the research and development or use process, and have indeed great advantages compared with the prior art. The following content describes them in conjunction with the data, charts and other information of the experimental process.

[0121] The method provided by this invention exhibits strong stability and high accuracy on typical unstructured road surfaces such as off-road terrain. Existing methods, however, are only applicable to structured road surfaces, and often suffer from convergence and low accuracy on unstructured surfaces.

[0122] Existing algorithms treat the vehicle body and wheels as a rigid connection, ignoring vehicle suspension characteristics. Therefore, the calculated tire vertical load has significant errors under uneven road conditions, leading to inaccurate estimation results. Furthermore, existing algorithms often ignore the influence of gravity on vehicle acceleration, directly inputting vehicle acceleration as an observation, making it impossible to accurately estimate on roads with a certain slope. The method proposed in this invention introduces an equivalent suspension model to optimize the calculation of wheel vertical loads and incorporates vehicle pose data to correct vehicle acceleration, improving the accuracy of the vehicle dynamics model on unstructured road surfaces and enabling it to maintain good estimation results even on complex unstructured road surfaces.

[0123] The proposed method was validated through co-simulation experiments using Simulink and CarSim. In CarSim, uneven road surfaces and slope conditions were set to simulate a vehicle driving on an unstructured road surface, acquiring the necessary signals for estimation, which were then input into Simulink for estimation. The simulation results were plotted as curves, as shown below. Figure 6 As shown, under simulated unstructured pavement conditions, the estimated peak adhesion coefficient converges without significant fluctuations due to pavement undulations, and the estimated result is quite close to the set peak adhesion coefficient. Therefore, the method proposed in this invention solves the problems of poor stability and low accuracy of existing methods under unstructured pavement conditions.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method of estimating peak adhesion coefficient of a road surface under a non-structural road surface, characterized by, An equivalent suspension model is introduced to optimize the calculation of wheel vertical loads and vehicle acceleration is corrected by combining vehicle posture data, thereby improving the accuracy of the vehicle dynamics model under unstructured road surfaces. Includes the following steps: (1) Acquire vehicle speed signal, front wheel angle signal and yaw rate signal by common sensors installed on the vehicle, calculate each wheel side slip angle ; The following represents four wheels; ； In equation (1), and These are the vehicle's longitudinal and lateral speeds, For vehicle yaw rate, For the front wheel steering angle, and For the front wheel track and rear wheel track of the vehicle, and This is the distance from the center of gravity to the front and rear axles; (2) Calculate the speed of the wheel center in the longitudinal direction of the wheel train : ； Then the wheel speed sensor is used to obtain the wheel speed of each wheel, and the slip rate of each wheel is further calculated ; ； In formula (3), is the rotational speed of each wheel, is the wheel radius; (3) Calculate the vertical static load of the wheels using the vehicle parameters; ； In formula (4), is the vehicle sprung mass, is the wheel mass, is the gravitational acceleration; (4) Obtain the vertical acceleration and vertical displacement signals of the wheel through the accelerometer and angle sensor, process them to obtain the vertical velocity signal, and then combine the displacement and velocity signals of the vehicle's center of gravity and the vehicle body attitude to calculate the vertical dynamic load of the wheel. (5) The current vertical load of the wheel is obtained by adding the calculated vertical static load and dynamic load of the wheel; (6) Calculate the Dugoff normalized force using wheel slip angle, slip ratio and vertical load; (7) Calculate the acceleration component caused by tire force by combining the vehicle body posture and longitudinal acceleration; (8) Input the calculated normalized force, the processed acceleration signal and the collected wheel angle into the extended Kalman filter module to estimate the peak adhesion coefficient of the road surface.

2. The method of estimating peak adhesion coefficient of a road surface under a non-structural road surface according to claim 1, wherein, This includes a system for estimating the peak adhesion coefficient of pavements under unstructured pavements.

3. The method of estimating peak adhesion coefficient of a road surface under a non-structural road surface according to claim 2, wherein, The peak adhesion coefficient estimation system for unstructured pavements includes: A wheel side slip angle module for calculating each wheel side slip angle by acquiring a vehicle speed signal, a front wheel steering angle signal, and a yaw rate signal using common sensors mounted on a vehicle ; A wheel slip ratio module for calculating the speed of the wheel center longitudinal axis direction of the wheel train The wheel speed of each wheel is further obtained by the wheel speed sensor, and the slip ratio of each wheel is further calculated ; The wheel vertical static load module is used to calculate the wheel vertical static load based on the vehicle's parameters. The wheel vertical dynamic load module is used to acquire the wheel vertical acceleration and vertical displacement signals through accelerometers and angle sensors, process them to obtain the vertical velocity signal, and then combine the displacement and velocity signals of the vehicle's center of gravity and the vehicle's attitude to calculate the wheel vertical dynamic load. The wheel current vertical load module is used to add the calculated wheel vertical static load and dynamic load to obtain the wheel current vertical load; The wheel normalized force module is used to calculate Dugoff normalized force based on wheel slip angle, slip ratio, and vertical load. The wheel acceleration component module is used to calculate the acceleration component caused by tire force by combining vehicle attitude and longitudinal acceleration; The Kalman filter module is used to input the calculated normalized force, the processed acceleration signal, and the acquired wheel rotation angle into the extended Kalman filter module to estimate the peak adhesion coefficient of the road surface.

4. A computer device, comprising: The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for estimating the peak adhesion coefficient of a road surface under any one of claims 1-3.

5. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for estimating the peak adhesion coefficient of a road surface under unstructured pavement as described in any one of claims 1-3.

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