Road adhesion coefficient adaptive estimation method and device based on terrain preview, equipment and storage medium

Abstract

The invention discloses a terrain preview-based road adhesion coefficient adaptive estimation method, device and equipment and a storage medium, and the method comprises the steps: obtaining vehicle preview terrain information and vehicle real-time operation data, and the preview terrain information comprises a road type; adjusting an initial parameter value of a road adhesion coefficient estimation algorithm based on the preview terrain information to obtain an adjusted road adhesion coefficient estimation algorithm; based on the real-time operation data of the vehicle, executing the adjusted road adhesion coefficient estimation algorithm to obtain a current road adhesion coefficient; and generating a vehicle driving control instruction based on the current road adhesion coefficient. The initial parameter value of the road adhesion coefficient estimation algorithm is adjusted based on the preview terrain information, and then the adjusted road adhesion coefficient estimation algorithm is executed, so that compared with the prior art, the road adhesion coefficient estimation convergence speed is improved, and the vehicle driving stability and safety are further improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle dynamics control technology, and in particular to an adaptive estimation method, device, equipment, and storage medium for road surface adhesion coefficient based on terrain prediction. Background Technology

[0002] The road adhesion coefficient is a key parameter in vehicle dynamics control systems. Accurate and rapid estimation of this coefficient is crucial for optimizing torque distribution, improving vehicle traction, handling stability, and active safety. Traditional methods for estimating the adhesion coefficient primarily rely on the fusion of vehicle dynamics models and real-time sensor data.

[0003] Currently, estimation algorithms based on Recursive Least Squares (RLS) are widely used in this field. The general process includes: real-time acquisition of signals such as wheel speed and drive torque; calculation of tire longitudinal force and slip ratio; prediction of the current tire force based on the parameter estimates from the previous moment, and comparison with the actual measured values ​​to obtain the identification error; and then iteratively updating the gain, parameter vector, and error covariance matrix to finally obtain the estimated value of the current road surface adhesion coefficient.

[0004] However, this traditional RLS estimation method, which relies entirely on real-time feedback data, has inherent limitations, mainly in the following aspects: 1. Estimation lag. Effective estimation by the algorithm depends on the tire entering a certain degree of slippage to provide sufficient excitation. This means that the vehicle often needs to approach or reach its slippage limit before the system can reliably identify the coefficient of friction, resulting in an unavoidable delay (usually tens of milliseconds) between road surface changes and accurate estimation. When a vehicle suddenly moves from a high-friction surface to a low-friction surface (such as from asphalt to ice), the system cannot predict this in advance, and the lag in control response will increase the risk of vehicle instability. 2. Fixed algorithm parameters and insufficient adaptive capability. On the one hand, the iteration of the parameter vector (containing the estimated coefficient of friction) usually starts from a fixed empirical initial value (e.g., preset to 0.8 for high-friction surfaces), regardless of the actual road conditions ahead. When entering a low-friction surface, the algorithm needs to slowly converge from the erroneous high initial value to the true low value, severely slowing down the estimation speed in the initial stage. On the other hand, the forgetting factor used to control the weight of historical data is usually a fixed value, making it difficult to simultaneously meet the estimation stability requirements of high-adhesion road surfaces (requiring long memory and a factor close to 1) and the rapid tracking requirements of low-adhesion or abrupt road surfaces (requiring short memory and a smaller factor). 3. This results in a passive vehicle control strategy. Upper-level strategies such as torque distribution and anti-slip control are entirely based on reactive adjustments to the adhesion coefficient value estimated after the fact. The entire perception-estimation-decision-execution chain has a cumulative delay, which is a post-event remedial mode and cannot achieve proactive control.

[0005] Therefore, there is an urgent need for an adaptive estimation method for road surface adhesion coefficient based on terrain prediction, which can improve the convergence speed of road surface adhesion coefficient estimation and thus improve vehicle driving stability and safety. Summary of the Invention

[0006] The main objective of this invention is to provide an adaptive estimation method, device, equipment, and storage medium for road surface adhesion coefficient based on terrain prediction, aiming to solve the technical problem that the lag in road surface adhesion coefficient estimation in the prior art leads to vehicle control response delay, thereby affecting vehicle driving stability and safety.

[0007] To achieve the above objectives, this invention provides an adaptive estimation method for road surface adhesion coefficient based on terrain prediction, the method comprising the following steps: Acquire vehicle preview terrain information and real-time vehicle operation data, wherein the preview terrain information includes road surface type; Based on the pre-targeted terrain information, the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm are adjusted to obtain the adjusted road surface adhesion coefficient estimation algorithm. Based on the real-time vehicle operating data, the adjusted road surface adhesion coefficient estimation algorithm is executed to obtain the current road surface adhesion coefficient. Based on the current road surface adhesion coefficient, vehicle drive control commands are generated.

[0008] Optionally, the step of adjusting the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on the pre-targeted terrain information to obtain the adjusted road surface adhesion coefficient estimation algorithm includes: Based on the road surface type, a preset mapping relationship is queried to determine the expected initial value of the road surface adhesion coefficient and the target value of the forgetting factor corresponding to the road surface type; The initial value of the road adhesion coefficient and the forgetting factor in the road adhesion coefficient estimation algorithm are adjusted to the expected initial value of the road adhesion coefficient and the target value of the forgetting factor, respectively, to obtain the adjusted road adhesion coefficient estimation algorithm.

[0009] Optionally, the step of executing the adjusted road surface adhesion coefficient estimation algorithm based on the real-time vehicle operating data to obtain the current road surface adhesion coefficient includes: Based on the real-time vehicle operating data, the tire longitudinal force and tire slip ratio are determined; Using the tire longitudinal force as the output and the regression vector constructed based on the tire slip ratio as the input, the adjusted road surface adhesion coefficient estimation algorithm is used for iterative estimation to obtain the current road surface adhesion coefficient.

[0010] Optionally, the step of iterative estimation using the adjusted road surface adhesion coefficient estimation algorithm includes: The identification error is determined by comparing the actual value of the tire longitudinal force at the current moment with the model prediction value based on the parameters estimated at the previous moment. The current gain is determined based on the identification error, the current regression vector, and the error covariance matrix of the previous time step. Based on the current gain and the recognition error, update the parameter vector containing the road surface adhesion coefficient; Update the error covariance matrix based on the target value of the forgetting factor and the error covariance matrix of the previous time step.

[0011] Optionally, before the step of iterative estimation using the adjusted road adhesion coefficient estimation algorithm, the method further includes: Monitor the real-time operating condition of the vehicle and determine whether the real-time operating condition meets the preset freezing conditions; When the real-time operating conditions meet the preset freezing conditions, the updating of the parameter vector and the error covariance matrix is ​​paused.

[0012] Optionally, the step of generating vehicle drive control commands based on the current road surface adhesion coefficient includes: When the type of road surface ahead is identified to be changing based on the pre-aimed terrain information, the target torque distribution parameters are determined according to the expected adhesion coefficient range corresponding to the target road surface type. During the process of the vehicle entering the target road surface type, vehicle drive control commands are generated based on the current road surface adhesion coefficient and the target torque distribution parameters.

[0013] Optionally, after the step of generating vehicle drive control commands based on the current road surface adhesion coefficient and the target torque distribution parameters, the method further includes: During the vehicle's operation based on the target torque distribution parameters, monitor whether the vehicle slips and obtain monitoring results; Based on the monitoring results, the ratio of the number of times the vehicle skidded during the pre-aiming control activation period to the total number of pre-aiming control activations was calculated. When the ratio value is lower than the preset ratio threshold or the pre-aimed terrain information is invalid, the system switches to a road surface adhesion coefficient estimation algorithm that does not rely on the pre-aimed terrain information to generate vehicle drive control commands.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes an adaptive estimation device for road surface adhesion coefficient based on terrain prediction, the device comprising: The data acquisition module is used to acquire vehicle preview terrain information and vehicle real-time operation data, wherein the preview terrain information includes road surface type; The parameter adjustment module is used to adjust the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on the pre-targeted terrain information, so as to obtain the adjusted road surface adhesion coefficient estimation algorithm. The coefficient estimation module is used to execute the adjusted road surface adhesion coefficient estimation algorithm based on the real-time vehicle operation data to obtain the current road surface adhesion coefficient. The instruction generation module is used to generate vehicle drive control instructions based on the current road surface adhesion coefficient.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a terrain-preview-based adaptive estimation device for road surface adhesion coefficient. The device includes: a memory, a processor, and a terrain-preview-based adaptive estimation program for road surface adhesion coefficient stored in the memory and executable on the processor. The terrain-preview-based adaptive estimation program for road surface adhesion coefficient is configured to implement the steps of the terrain-preview-based adaptive estimation method for road surface adhesion coefficient as described above.

[0016] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a terrain-predicting adaptive estimation program for road surface adhesion coefficient, wherein when the terrain-predicting adaptive estimation program for road surface adhesion coefficient is executed by a processor, it implements the steps of the terrain-predicting adaptive estimation method for road surface adhesion coefficient as described above.

[0017] This invention discloses a method for acquiring vehicle pre-aiming terrain information and real-time vehicle operation data. The pre-aiming terrain information includes road surface type. Based on the pre-aiming terrain information, the initial parameter values ​​of a road surface adhesion coefficient estimation algorithm are adjusted to obtain an adjusted road surface adhesion coefficient estimation algorithm. Based on the real-time vehicle operation data, the adjusted road surface adhesion coefficient estimation algorithm is executed to obtain the current road surface adhesion coefficient. Based on the current road surface adhesion coefficient, vehicle drive control commands are generated. Because this invention adjusts the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on pre-aiming terrain information, then executes the adjusted road surface adhesion coefficient estimation algorithm to obtain the current road surface adhesion coefficient, and finally generates vehicle drive control commands based on the current road surface adhesion coefficient, compared with existing technologies, this invention improves the convergence speed of road surface adhesion coefficient estimation, thereby improving vehicle driving stability and safety. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the first embodiment of the adaptive estimation method for road surface adhesion coefficient based on terrain prediction of the present invention. Figure 2 This is a flowchart illustrating the second embodiment of the adaptive estimation method for road surface adhesion coefficient based on terrain prediction of the present invention; Figure 3 This is a flowchart illustrating the third embodiment of the adaptive estimation method for road surface adhesion coefficient based on terrain prediction of the present invention. Figure 4This is a structural block diagram of the first embodiment of the adaptive estimation device for road surface adhesion coefficient based on terrain prediction of the present invention; Figure 5 This is a schematic diagram of the structure of a terrain-predictive adaptive estimation device for road surface adhesion coefficient in the hardware operating environment involved in the embodiments of the present invention.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0021] This invention provides an adaptive estimation method for road surface adhesion coefficient based on terrain prediction, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the adaptive estimation method for road surface adhesion coefficient based on terrain prediction of the present invention.

[0022] In this embodiment, the adaptive estimation method for road surface adhesion coefficient based on terrain prediction includes steps S10~S40: Step S10: Obtain vehicle preview terrain information and real-time vehicle operation data, wherein the preview terrain information includes road surface type.

[0023] It should be noted that the executing entity in this embodiment can be a vehicle controller applied in a vehicle drive control scenario, or an electronic device capable of realizing the above functions (such as a terrain-predicting adaptive estimation device for road adhesion coefficient). The following uses a system including a terrain-predicting adaptive estimation device for road adhesion coefficient (hereinafter referred to as the system) as an example to illustrate this embodiment and the following embodiments.

[0024] It should be explained that the terrain preview information refers to road attribute information about the road segment that the vehicle is about to enter, obtained by the vehicle through its onboard environmental perception system (intelligent driving system) or external communication link within a preset distance in front of the vehicle's current position. This information may include road surface type, such as snow, sand, mud, and asphalt.

[0025] It should be understood that the terrain information can be periodically sent to the vehicle controller or chassis domain controller in the form of digital signals (such as CAN or CANFD bus signals) to provide the main control system with a forward-looking decision-making basis.

[0026] Understandably, real-time vehicle operating data can refer to a set of physical quantities that reflect the vehicle's current motion state, dynamic state, and actuator state, either measured in real time by the vehicle's own sensor network or calculated in real time based on the measured values.

[0027] It should be noted that before the step of acquiring vehicle preview terrain information, the method further includes: determining whether the preview system communication is valid and whether the road surface type ahead is about to change; when the preview system communication is valid and the road surface type ahead is about to change, the step of acquiring vehicle preview terrain information is executed. For example, when the preview system detects a change in road surface type within 200 meters ahead (such as asphalt → snow), it determines that the road surface type ahead is about to change.

[0028] In a specific implementation, the step of determining whether the pre-aiming system communication is valid may include: detecting whether data packets from the pre-aiming system are received periodically and without timeout; and / or detecting whether the road surface type signal value in the data packet is within a preset valid value range; and / or detecting whether the data packet contains a status flag indicating that the communication is valid.

[0029] Step S20: Based on the pre-targeted terrain information, adjust the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm to obtain the adjusted road surface adhesion coefficient estimation algorithm.

[0030] It should be noted that the above-mentioned road surface adhesion coefficient estimation algorithm can be a recursive least squares (RLS) algorithm.

[0031] It should be understood that initial parameter values ​​refer to the values ​​assigned to the internal state variables of the road adhesion coefficient estimation algorithm at startup or reset. These initial values ​​directly affect the algorithm's convergence speed, estimation accuracy in the initial stage, and stability. Initial parameter values ​​include the initial value of the road adhesion coefficient and the forgetting factor.

[0032] In a specific implementation, the expected initial value of the road adhesion coefficient and the target value of the forgetting factor corresponding to the road surface type can be determined by querying a preset mapping relationship based on the road surface type; the initial value of the road adhesion coefficient and the forgetting factor in the road adhesion coefficient estimation algorithm are adjusted to the expected initial value of the road adhesion coefficient and the target value of the forgetting factor, respectively, to obtain the adjusted road adhesion coefficient estimation algorithm.

[0033] Understandably, the preset mapping relationship can refer to a predefined mapping table between road surface type and the expected initial value of road surface adhesion coefficient and the target value of forgetting factor.

[0034] It should be noted that in this embodiment, the initial value of the road surface adhesion coefficient and the forgetting factor in the road surface adhesion coefficient estimation algorithm are adjusted to the expected initial value of the road surface adhesion coefficient and the target value of the forgetting factor, respectively, which shortens the convergence time of the road surface adhesion coefficient estimation and realizes forward-looking parameter initialization.

[0035] It should be understood that the forgetting factor λ determines the algorithm's ability to remember historical data. A fixed λ (e.g., often set to 0.99) means the algorithm has excellent memory but reacts slowly to new data. This is an advantage on stable, high-adhesion road surfaces, but when the road surface changes abruptly (e.g., from asphalt to ice), the algorithm may become too attached to old data and fail to quickly track the characteristics of the new road surface, resulting in poor adaptability. Adjusting the forgetting factor according to the target road surface type can ensure both estimation stability and adaptability.

[0036] Step S30: Based on the real-time vehicle operation data, execute the adjusted road surface adhesion coefficient estimation algorithm to obtain the current road surface adhesion coefficient.

[0037] In a specific implementation, the tire longitudinal force and tire slip ratio can be determined based on the real-time vehicle operating data; the tire longitudinal force is used as the output, and the regression vector constructed based on the tire slip ratio is used as the input, and the adjusted road adhesion coefficient estimation algorithm is used for iterative estimation to obtain the current road adhesion coefficient.

[0038] It should be noted that the longitudinal force of the tire can be estimated using a tire dynamics model or a longitudinal tire brush model.

[0039] The equations corresponding to the tire dynamics model are: .

[0040] By transforming the equations corresponding to the above tire dynamics model, we obtain the estimation formula for the tire longitudinal force: .

[0041] In the formula, Indicates the moment of inertia of the wheel, This represents the angular acceleration of the wheel. This represents the driving torque acting on the wheels. Indicates the tire's rolling radius. Indicates the longitudinal force of the tire. Indicates braking torque. ,in Indicates the rolling resistance of the wheel. Indicates the rolling resistance coefficient. This indicates the vertical load on the tire.

[0042] It should be noted that the wheel angular acceleration The tire vertical load is obtained by differential calculation of wheel speed signals. Provided by the vehicle's wheel load estimation function. Wheel moment of inertia, driving torque acting on the wheel, tire rolling radius, braking torque, wheel rolling resistance, rolling resistance coefficient, and tire vertical load can be determined based on real-time vehicle operating data.

[0043] The longitudinal tire brush model describes the physical relationship between tire longitudinal force, slip ratio, vertical load, road adhesion coefficient, and tire longitudinal stiffness, and forms the theoretical basis for estimating the road adhesion coefficient. The equations corresponding to the longitudinal tire brush model are: ; in, , .

[0044] In the formula, Indicates the longitudinal force of the tire. Indicates the road surface adhesion coefficient. This represents the vertical load on the tire, and k represents the tire slip ratio. This indicates the longitudinal stiffness of the tire.

[0045] It should be noted that, It is an intermediate variable in the longitudinal tire brush model, and its calculation is divided into two intervals, depending on whether the tire's stress state has reached the friction limit.

[0046] When the tires are not saturated, ( ): Physical meaning: At this point, the tire is in a nonlinear elastic deformation stage, and the longitudinal force and tire slip ratio have a nonlinear relationship. The formula approximates the force characteristics of the tire brush model through a third-order polynomial (Taylor expansion), including: linear terms ( ): Elastic deformation at small slip ratio; quadratic term ( ): Corrects nonlinear deformation of tread rubber due to increased slip ratio; cubic term ( ): Further fit the force saturation trend under high slip ratio.

[0047] When the tire force is saturated, ( ): Physical meaning: When the tire reaches its friction limit, the longitudinal force of the tire no longer increases with the slip ratio, but is determined solely by the road surface adhesion coefficient. and vertical load Decide.

[0048] The longitudinal tire brush model reflects the nonlinear characteristics of the tire, namely, the longitudinal force of the tire before reaching a specific slip ratio. It increases with the increase of tire slip ratio k; after reaching a certain tire slip ratio, Reaching its maximum value .

[0049] This model reflects the nonlinear characteristics of the tire: before reaching the critical slip value, It increases with increasing slip ratio k; after reaching a critical value, Reaching the maximum value The tire slip ratio k is defined as: ; In the formula, Wheel speed; Vehicle speed; Tire radius.

[0050] It should be understood that the mathematical model corresponding to the Recursive Least Squares (RLS) algorithm is: y(k) =φ (k)×θ(k); In the formula, y(k) represents the model output, i.e., the longitudinal force of the tire. φ(k) represents the regression vector (Jacobi matrix), calculated from tire slip ratio, tire vertical load, etc.; θ(k) represents the parameter vector to be estimated, θ(k)=[ , ] That is, simultaneously estimating the longitudinal stiffness of the tire. and road surface adhesion coefficient .

[0051] Understandably, the Recursive Least Squares (RLS) algorithm is a recursive process that performs the following core steps at each time step t: 1. Measurement and Calculation: Obtain the longitudinal force y(t) of the tire at the current moment and calculate the regression vector φ(t).

[0052] 2. Calculate the recognition error: e(t) = y(t) - φ (t)*θ(t-1). That is, the difference between the current actual force and the model's predicted force based on the parameters estimated at the previous moment.

[0053] 3. Calculate the update gain: K(t) = P(t-1) * φ(t) / [R + φ [(t)* P(t-1)*φ(t)]; where P(t-1) is the error covariance matrix of the previous time step; R is the noise covariance matrix, which is used to introduce the uncertainty of measurement noise and increase the robustness of the system.

[0054] 4. Update parameter estimates: θ(t) = θ(t-1) + K(t)*e(t). Correct the parameter vector using the gain K(t) and the error e(t).

[0055] 5. Update the covariance matrix: P(t) = [I - K(t) *φ (t)] * P(t-1) / λ.

[0056] It should be understood that the iterative estimation steps using the adjusted road adhesion coefficient estimation algorithm include: determining the identification error based on the actual value of the tire longitudinal force at the current moment and the model prediction value based on the parameter estimation at the previous moment; determining the current gain based on the identification error, the current regression vector, and the error covariance matrix at the previous moment; updating the parameter vector containing the road adhesion coefficient based on the current gain and the identification error; and updating the error covariance matrix based on the forgetting factor target value and the error covariance matrix at the previous moment.

[0057] It should be noted that the parameter vector also includes the tire longitudinal stiffness parameter; in the step of iterative estimation using the adjusted road adhesion coefficient estimation algorithm, independent forgetting factors are configured for the road adhesion coefficient parameter and the tire longitudinal stiffness parameter respectively. Forgetting factor ( The parameters can be dynamically adjusted based on the slip ratio, since the Recursive Least Squares (RLS) algorithm needs to estimate two parameters simultaneously: tire longitudinal stiffness. and road surface adhesion coefficient However, the rates of change of these two parameters are different, as follows: (Tire's inherent properties) change very slowly and are relatively stable; (Road properties) can change rapidly (e.g., connecting road surfaces, from ice to asphalt).

[0058] It should be understood that the RLS algorithm adjusts the gain vector K(t) by updating the covariance P(t), and K(t) directly determines the magnitude of the correction of the parameter estimate θ(t) by the innovation (i.e. the current error e(t)).

[0059] In practical implementation, to improve the performance and robustness of the road surface adhesion coefficient estimation algorithm, it is possible to... and Assign different, independent forgetting factors, i.e., use vector forgetting factors. For : Use larger (For example, 0.97, 0.99), meaning the algorithm forgets tire stiffness very slowly, maintaining the stability of its estimation; for Use smaller A value (e.g., 0.98) means that the algorithm forgets historical data on road adhesion coefficients relatively slowly, can quickly adapt to changes in the road surface, ensuring speed. The corresponding formula for the covariance matrix is: ; Where Λ is the forgetting factor matrix, expressed as: Λ=diag[λ1, λ2]. Where λ1 corresponds to... The forgetting factor, λ2 corresponds to The forgetting factor. In this way, differentiated memory management is applied to the failure of different parameters within an algorithm iteration.

[0060] To ensure the robustness and stability of the system under complex, extreme, or unreliable operating conditions, in the specific implementation, before the step of iterative estimation using the adjusted road adhesion coefficient estimation algorithm, the following steps are included: monitoring the real-time operating conditions of the vehicle and determining whether the real-time operating conditions meet the preset freezing conditions; when the real-time operating conditions meet the preset freezing conditions, pausing the updating of the parameter vector and the error covariance matrix.

[0061] It should be understood that real-time operating conditions refer to a collection of instantaneous physical quantities and logical states that dynamically change over time during vehicle operation, reflecting the vehicle's own motion state, driver's operational intentions, and interactions with the external environment. This can include vehicle motion states (such as vehicle speed, wheel speed, wheel angular acceleration, and longitudinal acceleration), driver operational commands (such as drive torque requests, brake pedal opening or status, and steering wheel angle), tire-ground interaction states (such as tire slip ratio and slip ratio change rate), and subsystem operating states (such as the validity indicators of relevant sensor signals and network communication status).

[0062] It should be explained that the preset freeze conditions can include excessive tire steering angle, excessively low vehicle speed, insufficient wheel output torque, brake pedal depressed, excessively high or low wheel slip ratio, and excessively rapid changes in wheel slip ratio. If any one of these conditions is met, it means the real-time operating conditions meet the preset freeze conditions, and the forgetting factor will be temporarily disabled. Setting it to 1 means entering hold mode, where old data is no longer forgotten and new observations will not affect the current parameter vector and error covariance matrix, thus freezing the parameter estimates at the values ​​from the previous time step.

[0063] Step S40: Generate vehicle drive control commands based on the current road surface adhesion coefficient.

[0064] In practice, vehicle drive control commands can be generated based on the current road surface adhesion coefficient and the pre-aimed terrain information.

[0065] This embodiment discloses a method for acquiring vehicle preview terrain information and real-time vehicle operation data. The preview terrain information includes road surface type. Based on the preview terrain information, the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm are adjusted to obtain an adjusted road surface adhesion coefficient estimation algorithm. Based on the real-time vehicle operation data, the adjusted road surface adhesion coefficient estimation algorithm is executed to obtain the current road surface adhesion coefficient. Based on the current road surface adhesion coefficient, vehicle drive control commands are generated. Because this embodiment adjusts the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on preview terrain information, then executes the adjusted road surface adhesion coefficient estimation algorithm to obtain the current road surface adhesion coefficient, and finally generates vehicle drive control commands based on the current road surface adhesion coefficient, compared to existing technologies, this embodiment improves the convergence speed of road surface adhesion coefficient estimation, thereby improving vehicle driving stability and safety.

[0066] refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the adaptive estimation method for road surface adhesion coefficient based on terrain prediction of the present invention.

[0067] Based on the first embodiment described above, in this embodiment, step S40 includes steps S401 to S402: Step S401: When it is identified that the road surface type ahead will change based on the pre-aiming terrain information, the target torque distribution parameters are determined according to the expected adhesion coefficient range corresponding to the target road surface type.

[0068] Step S402: During the process of the vehicle entering the target road surface type, a vehicle drive control command is generated based on the current road surface adhesion coefficient and the target torque distribution parameters.

[0069] It should be understood that the target road surface type can refer to the road surface type that the vehicle is about to enter, as identified by a pre-aiming system (such as a camera, LiDAR, or high-precision map). For example, if the vehicle is currently driving on an asphalt road and the pre-aiming system detects that there is snow 100 meters ahead, then the snow is the target road surface type.

[0070] Understandably, the expected range of adhesion coefficients can be determined by looking up a table of correspondences between road surface types and the possible ranges of adhesion coefficient values ​​for each type. For example, the expected range of adhesion coefficients for snow is 0.2–0.4.

[0071] It should be explained that the target torque distribution parameters can be a set of control parameters pre-set to adapt to the target road surface type and its expected range of adhesion coefficients.

[0072] In practical implementation, in order to avoid abrupt changes in torque caused by sudden road surface changes and improve driving comfort and vehicle stability, the target torque distribution parameters can be corrected or interpolated in real time according to the current road surface adhesion coefficient during the process of the vehicle entering the target road surface type, so as to generate real-time torque distribution parameters; then, based on the real-time torque distribution parameters, vehicle drive control commands are generated.

[0073] This embodiment discloses a method for determining target torque distribution parameters based on the expected coefficient of adhesion range corresponding to the target road surface type when a change in road surface type is detected based on the pre-aimed terrain information. During the vehicle's entry into the target road surface type, vehicle drive control commands are generated based on the current road surface adhesion coefficient and the target torque distribution parameters. Because this embodiment determines the target torque distribution parameters based on the expected coefficient of adhesion range corresponding to the target road surface type using the pre-aimed terrain information, and generates vehicle drive control commands based on the current road surface adhesion coefficient and the target torque distribution parameters during the vehicle's entry into the target road surface type, compared to existing technologies, this embodiment avoids abrupt torque changes caused by sudden road surface changes, thus improving driving comfort and vehicle stability.

[0074] refer to Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the adaptive estimation method for road surface adhesion coefficient based on terrain prediction of the present invention.

[0075] Based on the above embodiments, in this embodiment, after step S402, steps S50~S70 are further included: Step S50: During the process of the vehicle driving based on the target torque distribution parameters, monitor whether the vehicle slips and obtain the monitoring results.

[0076] Step S60: Based on the monitoring results, calculate the ratio of the number of times the vehicle skids during the pre-aiming control activation period to the total number of pre-aiming control activations.

[0077] Step S70: When the ratio value is lower than the preset ratio threshold or the pre-aimed terrain information is invalid, switch to the road surface adhesion coefficient estimation algorithm that does not rely on the pre-aimed terrain information to generate vehicle drive control commands.

[0078] It should be noted that advance control activation can refer to the system (such as the vehicle controller) having executed the step of adjusting the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on valid advance terrain information (such as recognizing snow 200 meters ahead).

[0079] It should be understood that the number of times a vehicle slips during the pre-aiming control activation period can refer to the actual time a vehicle enters the pre-aimed road segment during the pre-aiming control activation period. The system monitors this in real time through sensors such as wheel speed and vehicle speed. Once the wheel slip rate is detected to exceed the preset safety threshold, it is determined that a slip event has occurred.

[0080] It should be explained that the total number of times the pre-aiming control is activated refers to the cumulative number of times the system has successfully entered the pre-aiming control mode. Each time the pre-aiming system detects a valid change in the road surface type ahead, and the system successfully adjusts the parameters accordingly to enable the pre-aiming control function, it is counted as one activation.

[0081] Invalid terrain information for aiming can mean that the signal source providing the aiming information is faulty or unreliable.

[0082] Understandably, this embodiment enables the system to automatically and smoothly switch to a fully validated road adhesion coefficient estimation algorithm that does not rely on pre-aiming when reliability is substandard (proportion value is lower than a preset proportion threshold) or when the pre-aiming terrain information is invalid. This ensures that the vehicle's most basic adhesion coefficient estimation and torque distribution functions remain reliable even when the pre-aiming system performance degrades or fails, avoiding control risks caused by relying on erroneous pre-aiming information, thereby improving the fault tolerance and driving safety of the entire system.

[0083] This embodiment discloses a method for monitoring whether vehicle slippage occurs during vehicle operation based on the target torque distribution parameters, and obtaining monitoring results. Based on the monitoring results, the ratio of the number of times the vehicle slips during the pre-aiming control activation period to the total number of pre-aiming control activations is calculated. When the ratio is lower than a preset ratio threshold or the pre-aiming terrain information is invalid, the system switches to a road adhesion coefficient estimation algorithm that does not rely on pre-aiming terrain information to generate vehicle drive control commands. Because this embodiment dynamically decides whether to continue using the pre-aiming control scheme by real-time statistical analysis of the actual effect (slippage rate) of the pre-aiming control and the state of the pre-aiming terrain information, compared with the prior art, this embodiment enhances the robustness and safety of the system and achieves fault-safe rollback.

[0084] Furthermore, this embodiment of the invention also proposes a storage medium storing a terrain-predicting adaptive estimation program for road surface adhesion coefficient. When the terrain-predicting adaptive estimation program for road surface adhesion coefficient is executed by a processor, it implements the steps of the terrain-predicting adaptive estimation method for road surface adhesion coefficient as described above.

[0085] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the adaptive estimation device for road surface adhesion coefficient based on terrain prediction of the present invention.

[0086] like Figure 4As shown, the adaptive estimation device for road surface adhesion coefficient based on terrain prediction proposed in this embodiment of the invention includes: a data acquisition module 501, a parameter adjustment module 502, a coefficient estimation module 503, and an instruction generation module 504.

[0087] The data acquisition module 501 is used to acquire vehicle preview terrain information and vehicle real-time operation data, wherein the preview terrain information includes road surface type.

[0088] The parameter adjustment module 502 is used to adjust the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on the pre-aimed terrain information, so as to obtain the adjusted road surface adhesion coefficient estimation algorithm.

[0089] The coefficient estimation module 503 is used to execute the adjusted road surface adhesion coefficient estimation algorithm based on the real-time vehicle operation data to obtain the current road surface adhesion coefficient.

[0090] The instruction generation module 504 is used to generate vehicle drive control instructions based on the current road surface adhesion coefficient.

[0091] The parameter adjustment module 502 is further configured to query a preset mapping relationship based on the road surface type to determine the expected initial value of the road surface adhesion coefficient and the target value of the forgetting factor corresponding to the road surface type; and adjust the initial value of the road surface adhesion coefficient and the forgetting factor in the road surface adhesion coefficient estimation algorithm to the expected initial value of the road surface adhesion coefficient and the target value of the forgetting factor, respectively, to obtain the adjusted road surface adhesion coefficient estimation algorithm.

[0092] The coefficient estimation module 503 is further used to determine the tire longitudinal force and tire slip ratio based on the real-time vehicle operation data; using the tire longitudinal force as the output and the regression vector constructed based on the tire slip ratio as the input, iterative estimation is performed using the adjusted road adhesion coefficient estimation algorithm to obtain the current road adhesion coefficient.

[0093] The coefficient estimation module 503 is further configured to: determine the identification error based on the actual value of the tire longitudinal force at the current moment and the model prediction value based on the parameter estimation at the previous moment; determine the current gain based on the identification error, the current regression vector and the error covariance matrix at the previous moment; update the parameter vector containing the road adhesion coefficient based on the current gain and the identification error; and update the error covariance matrix based on the forgetting factor target value and the error covariance matrix at the previous moment.

[0094] The coefficient estimation module 503 is also used to monitor the real-time operating condition of the vehicle and determine whether the real-time operating condition meets the preset freezing condition; when the real-time operating condition meets the preset freezing condition, the updating of the parameter vector and the error covariance matrix is ​​paused.

[0095] This device embodiment discloses the acquisition of vehicle preview terrain information and real-time vehicle operation data. The preview terrain information includes road surface type. Based on the preview terrain information, the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm are adjusted to obtain an adjusted road surface adhesion coefficient estimation algorithm. Based on the real-time vehicle operation data, the adjusted road surface adhesion coefficient estimation algorithm is executed to obtain the current road surface adhesion coefficient. Based on the current road surface adhesion coefficient, vehicle drive control commands are generated. Because this device embodiment adjusts the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on preview terrain information, then executes the adjusted road surface adhesion coefficient estimation algorithm to obtain the current road surface adhesion coefficient, and finally generates vehicle drive control commands based on the current road surface adhesion coefficient, compared with the prior art, this device embodiment improves the convergence speed of road surface adhesion coefficient estimation, thereby improving vehicle driving stability and safety.

[0096] Based on the first embodiment of the adaptive estimation device for road surface adhesion coefficient based on terrain prediction of the present invention, a second embodiment of the adaptive estimation device for road surface adhesion coefficient based on terrain prediction of the present invention is proposed.

[0097] In this embodiment, the instruction generation module 504 is further configured to determine the target torque distribution parameter based on the expected adhesion coefficient range corresponding to the target road surface type when the road surface type ahead is identified to change based on the pre-aiming terrain information; and generate vehicle drive control instructions based on the current road surface adhesion coefficient and the target torque distribution parameter during the process of the vehicle entering the target road surface type.

[0098] The instruction generation module 504 is further configured to monitor whether the vehicle slips during the process of driving based on the target torque distribution parameters and obtain monitoring results; based on the monitoring results, calculate the ratio of the number of times the vehicle slips during the pre-aiming control activation to the total number of pre-aiming control activations; when the ratio is lower than a preset ratio threshold or the pre-aiming terrain information is invalid, switch to a road surface adhesion coefficient estimation algorithm that does not rely on the pre-aiming terrain information to generate vehicle drive control instructions.

[0099] Other embodiments or specific implementations of the adaptive estimation device for road surface adhesion coefficient based on terrain prediction of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0100] This application provides a terrain-preview-based adaptive estimation device for road surface adhesion coefficient. The terrain-preview-based adaptive estimation device for road surface adhesion coefficient includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the terrain-preview-based adaptive estimation method for road surface adhesion coefficient in the above embodiment 1.

[0101] The following is for reference. Figure 5 This document illustrates a structural schematic diagram of a terrain-predicting adaptive road adhesion coefficient estimation device suitable for implementing embodiments of this application. The terrain-predicting adaptive road adhesion coefficient estimation device in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The terrain-predictive adaptive road adhesion coefficient estimation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0102] like Figure 5As shown, the terrain-predicting adaptive road adhesion coefficient estimation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the terrain-predicting adaptive road adhesion coefficient estimation device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the terrain-predicting adaptive road adhesion coefficient estimation device to communicate wirelessly or wiredly with other devices to exchange data. While the figures show terrain-predicting adaptive road adhesion coefficient estimation devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0103] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0104] The terrain-predicting adaptive road adhesion coefficient estimation device provided in this application employs the terrain-predicting adaptive road adhesion coefficient estimation method described in the above embodiments. This solves the technical problem in existing technologies where lag in road adhesion coefficient estimation leads to vehicle control response delay, thereby affecting vehicle driving stability and safety. Compared with the prior art, the beneficial effects of the terrain-predicting adaptive road adhesion coefficient estimation device provided in this application are the same as those of the terrain-predicting adaptive road adhesion coefficient estimation method provided in the above embodiments. Furthermore, other technical features of this terrain-predicting adaptive road adhesion coefficient estimation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0105] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0107] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0108] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0110] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An adaptive estimation method for road surface adhesion coefficient based on terrain prediction, characterized in that, The method includes: Acquire vehicle preview terrain information and real-time vehicle operation data, wherein the preview terrain information includes road surface type; Based on the pre-targeted terrain information, the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm are adjusted to obtain the adjusted road surface adhesion coefficient estimation algorithm. Based on the real-time vehicle operating data, the adjusted road surface adhesion coefficient estimation algorithm is executed to obtain the current road surface adhesion coefficient. Based on the current road surface adhesion coefficient, vehicle drive control commands are generated.

2. The adaptive estimation method for road surface adhesion coefficient based on terrain prediction as described in claim 1, characterized in that, The step of adjusting the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on the pre-targeted terrain information to obtain the adjusted road surface adhesion coefficient estimation algorithm includes: Based on the road surface type, a preset mapping relationship is queried to determine the expected initial value of the road surface adhesion coefficient and the target value of the forgetting factor corresponding to the road surface type; The initial value of the road adhesion coefficient and the forgetting factor in the road adhesion coefficient estimation algorithm are adjusted to the expected initial value of the road adhesion coefficient and the target value of the forgetting factor, respectively, to obtain the adjusted road adhesion coefficient estimation algorithm.

3. The adaptive estimation method for road surface adhesion coefficient based on terrain prediction as described in claim 2, characterized in that, The step of executing the adjusted road surface adhesion coefficient estimation algorithm based on the real-time vehicle operating data to obtain the current road surface adhesion coefficient includes: Based on the real-time vehicle operating data, the tire longitudinal force and tire slip ratio are determined; Using the tire longitudinal force as the output and the regression vector constructed based on the tire slip ratio as the input, the adjusted road surface adhesion coefficient estimation algorithm is used for iterative estimation to obtain the current road surface adhesion coefficient.

4. The adaptive estimation method for road surface adhesion coefficient based on terrain prediction as described in claim 3, characterized in that, The step of iterative estimation using the adjusted road adhesion coefficient estimation algorithm includes: The identification error is determined by comparing the actual value of the tire longitudinal force at the current moment with the model prediction value based on the parameters estimated at the previous moment. The current gain is determined based on the identification error, the current regression vector, and the error covariance matrix of the previous time step. Based on the current gain and the recognition error, update the parameter vector containing the road surface adhesion coefficient; Update the error covariance matrix based on the target value of the forgetting factor and the error covariance matrix of the previous time step.

5. The adaptive estimation method for road surface adhesion coefficient based on terrain prediction as described in claim 4, characterized in that, Before the step of iterative estimation using the adjusted road adhesion coefficient estimation algorithm, the method further includes: Monitor the real-time operating condition of the vehicle and determine whether the real-time operating condition meets the preset freezing conditions; When the real-time operating conditions meet the preset freezing conditions, the updating of the parameter vector and the error covariance matrix is ​​paused.

6. The adaptive estimation method for road surface adhesion coefficient based on terrain prediction as described in claim 1, characterized in that, The step of generating vehicle drive control commands based on the current road surface adhesion coefficient includes: When the type of road surface ahead is identified to be changing based on the pre-aimed terrain information, the target torque distribution parameters are determined according to the expected adhesion coefficient range corresponding to the target road surface type. During the process of the vehicle entering the target road surface type, vehicle drive control commands are generated based on the current road surface adhesion coefficient and the target torque distribution parameters.

7. The adaptive estimation method for road surface adhesion coefficient based on terrain prediction as described in claim 6, characterized in that, Following the step of generating vehicle drive control commands based on the current road surface adhesion coefficient and the target torque distribution parameters, the method further includes: During the vehicle's operation based on the target torque distribution parameters, monitor whether the vehicle slips and obtain monitoring results; Based on the monitoring results, the ratio of the number of times the vehicle skidded during the pre-aiming control activation period to the total number of pre-aiming control activations was calculated. When the ratio value is lower than the preset ratio threshold or the pre-aimed terrain information is invalid, the system switches to a road surface adhesion coefficient estimation algorithm that does not rely on the pre-aimed terrain information to generate vehicle drive control commands.

8. An adaptive estimation device for road surface adhesion coefficient based on terrain prediction, characterized in that, The device includes: The data acquisition module is used to acquire vehicle preview terrain information and vehicle real-time operation data, wherein the preview terrain information includes road surface type; The parameter adjustment module is used to adjust the initial parameter values ​​of the road surface adhesion coefficient estimation algorithm based on the pre-targeted terrain information, so as to obtain the adjusted road surface adhesion coefficient estimation algorithm. The coefficient estimation module is used to execute the adjusted road surface adhesion coefficient estimation algorithm based on the real-time vehicle operation data to obtain the current road surface adhesion coefficient. The instruction generation module is used to generate vehicle drive control instructions based on the current road surface adhesion coefficient.

9. An adaptive estimation device for road surface adhesion coefficient based on terrain prediction, characterized in that, The device includes: a memory, a processor, and a terrain-predicting adaptive estimation program for road surface adhesion coefficient stored in the memory and executable on the processor, the terrain-predicting adaptive estimation program for road surface adhesion coefficient configured to implement the steps of the terrain-predicting adaptive estimation method for road surface adhesion coefficient as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a road surface adhesion coefficient adaptive estimation program based on terrain prediction, which, when executed by a processor, implements the steps of the road surface adhesion coefficient adaptive estimation method based on terrain prediction as described in any one of claims 1 to 7.

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