Torque limiting method, electronic device, and vehicle

Abstract

The present application provides a torque limiting method, electronic device, and vehicle. When calculating the current slip ratio, the navigation estimated vehicle speed and wheel speed are used to correct the current detected vehicle speed, ensuring the accuracy of the current slip ratio, thereby improving the accuracy of the calculated road adhesion coefficient and the accuracy of the torque limiting. When estimating the road adhesion coefficient, the recognition area of ​​the current scanned image is corrected to improve the accuracy of the road type identification, thereby improving the accuracy of the estimated road adhesion coefficient and ensuring the accuracy of the torque limiting. When determining the limiting torque, the road type confidence level is introduced to narrow the gap between the target road adhesion coefficient and the true value, thereby ensuring the accuracy of the limiting torque determined based on the target road adhesion coefficient. This ensures that when requesting a limit based on the limiting torque, the target road adhesion coefficient can be fully utilized, ensuring that the vehicle's wheels do not slip while maintaining dynamic performance, thereby ensuring driving safety.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a torque limiting method, electronic equipment, and a vehicle. Background Art

[0002] Vehicles are developing towards electrification and intelligence. For vehicles with decoupled front and rear axles, both the front and rear axle motors can output large torques. When driving with high torque, if the requested torque value is too large, it may cause the vehicle wheels to slip, resulting in driving hazards. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a torque limiting method, electronic equipment and vehicle, which ensure that the wheels do not slip when driving on different road surfaces through torque limiting, thereby improving driving safety.

[0004] Based on the above objectives, the present application provides a torque limiting method, comprising:

[0005] determining a reference vehicle speed based on the navigation estimated vehicle speed, the current detected vehicle speed, and the wheel speed of each wheel, determining a current slip ratio based on the reference vehicle speed and the wheel linear speed of the driving wheel, and calculating a road adhesion coefficient based on the current slip ratio and a preset dynamic model;

[0006] Correcting the recognition area of ​​the current scanned image according to the reference vehicle speed and the current front wheel steering angle to obtain a road surface recognition image, and performing image classification on the road surface recognition image to obtain an estimated road surface adhesion coefficient and a road surface type confidence;

[0007] A target road adhesion coefficient is determined based on the road type confidence, the calculated road adhesion coefficient, and the estimated road adhesion coefficient, and a current limit torque is determined based on the target road adhesion coefficient, the front axle load, and the rear axle load, and the requested torque is limited and controlled based on the current limit torque.

[0008] Based on the same inventive concept, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the above-mentioned method when executing the computer program.

[0009] Based on the same inventive concept, the present disclosure also provides a vehicle, comprising the electronic device as described above.

[0010] As can be seen from the above, the torque limiting method, electronic device, and vehicle provided in this application can determine a reference vehicle speed based on the navigation-estimated vehicle speed, the current detected vehicle speed, and the wheel speed of each wheel; determine the current slip ratio based on the reference vehicle speed and the linear velocity of the drive wheels; and calculate the road adhesion coefficient based on the current slip ratio and a preset dynamic model. When calculating the current slip ratio, the navigation-estimated vehicle speed and wheel speed are used to correct the current detected vehicle speed, ensuring the accuracy of the current slip ratio, thereby improving the accuracy of the calculated road adhesion coefficient and enhancing the accuracy of torque limiting. The recognized area of ​​the current scanned image is corrected based on the reference vehicle speed and the current front wheel angle to obtain a road recognition image, which is then classified to obtain an estimated road adhesion coefficient and a road type confidence level. When estimating the road adhesion coefficient, the recognized area of ​​the current scanned image is corrected to improve the accuracy of road type recognition, thereby improving the accuracy of the estimated road adhesion coefficient and ensuring the accuracy of torque limiting. The target road adhesion coefficient is determined based on the road type confidence, the calculated road adhesion coefficient, and the estimated road adhesion coefficient. The current torque limit is then determined based on the target road adhesion coefficient and the front and rear axle loads. The requested torque is then limited based on the current torque limit. The road type confidence factor is incorporated into the torque limit determination to minimize the discrepancy between the target road adhesion coefficient and the actual value, ensuring the accuracy of the torque limit determined based on the target road adhesion coefficient. This ensures that the target road adhesion coefficient is fully utilized when the requested torque limit is applied, ensuring both dynamic performance and wheel slippage, thus ensuring driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is a flow chart of the torque limiting method according to an embodiment of the present application;

[0013] Figure 2 A flow chart for determining a reference vehicle speed for an embodiment of the present application;

[0014] Figure 3 This is a flow chart of a road surface recognition image according to an embodiment of the present application;

[0015] Figure 4a This is a schematic diagram of the deviation recognition area under the straight-line driving condition of an embodiment of the present application;

[0016] Figure 4bThis is a schematic diagram of the offset recognition area under the turning driving condition of an embodiment of the present application;

[0017] Figure 5 A flow chart for determining the estimated road adhesion coefficient and road type confidence for an embodiment of the present application;

[0018] Figure 6 Flowchart for determining the target road adhesion coefficient for an embodiment of the present application;

[0019] Figure 7 A flow chart for determining the current torque limit according to an embodiment of the present application;

[0020] Figure 8 This is a flow chart of controlling the request torque according to the current torque limit according to an embodiment of the present application;

[0021] Figure 9 This is a schematic structural diagram of a torque limiting device according to an embodiment of the present application;

[0022] Figure 10 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] It should be understood herein that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0026] Based on the description of the above background technology, the following situations also exist in the related art:

[0027] Vehicles are developing towards electrification and intelligence. For vehicles with decoupled front and rear axles, both the front and rear axle motors can output large torque. When driving with large torque, such as when driving on off-road conditions, precise torque control is required to prevent the vehicle from slipping, so as to make full use of the road adhesion coefficient and enhance the vehicle's off-road capability and safety. If the requested torque value is large and exceeds the maximum torque allowed by the road adhesion coefficient, it may cause the vehicle wheels to slip, resulting in driving hazards.

[0028] In order to ensure that slippage occurs when torque is requested, the requested torque needs to be precisely controlled. The premise of precise torque control is the accurate calculation of the reference vehicle speed and road adhesion coefficient. The current vehicle reference speed calculation method relies on four wheel speeds, vehicle longitudinal acceleration, lateral acceleration, yaw acceleration, etc. When there are working conditions such as wheel spin and slope speed, the reference speed calculation is inaccurate, resulting in large errors in torque control, which in turn makes slippage more likely to occur, causing driving hazards.

[0029] The calculation method of the road adhesion coefficient mainly relies on vehicle dynamics, and the vehicle dynamics model and tire model are used to estimate the road adhesion coefficient. Since this method requires the use of more input parameters when estimating the road adhesion coefficient, such as slip rate, the calculation of the slip rate also requires the use of a reference vehicle speed. The reference vehicle speed has a large error due to working conditions such as wheel spinning and flying slopes, which will cause the error of the road adhesion coefficient to further increase, resulting in a large error in the estimation of the road adhesion coefficient. When performing torque control, due to the accuracy of the estimation of the vehicle state (represented by parameters such as the road adhesion coefficient and slip rate), the vehicle's torque cannot be accurately controlled, which makes it easy to slip.

[0030] The torque limiting method, electronic device, and vehicle provided in embodiments of the present application utilize navigation-based estimated vehicle speed and wheel speed to correct the current detected vehicle speed when calculating the current slip ratio, thereby ensuring the accuracy of the current slip ratio and, in turn, improving the accuracy of the calculated road adhesion coefficient and the precision of the torque limiting. When estimating the road adhesion coefficient, the recognition area of ​​the current scanned image is corrected to improve the accuracy of road type identification, thereby improving the precision of the estimated road adhesion coefficient and ensuring the accuracy of the torque limiting. When determining the limiting torque, a road type confidence level is introduced to narrow the gap between the target road adhesion coefficient and the true value, thereby ensuring the accuracy of the limiting torque determined based on the target road adhesion coefficient. This ensures that, when requesting a limiting torque based on the limiting torque, the target road adhesion coefficient is fully utilized, ensuring that the vehicle's wheels do not slip while maintaining dynamic performance and ensuring driving safety.

[0031] The torque limiting method provided by the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0032] In some embodiments, as Figure 1 As shown, a torque limiting method includes:

[0033] Step 101: Determine a reference vehicle speed based on the navigation estimated vehicle speed, the current detected vehicle speed, and the wheel speed of each wheel, determine the current slip rate based on the reference vehicle speed and the wheel linear speed of the drive wheel, and calculate the road adhesion coefficient based on the current slip rate and a preset dynamic model.

[0034] In specific implementation, the proportion of the sliding component in the wheel movement is the slip rate. The slip rate is calculated as follows: slip rate = (wheel linear velocity - reference vehicle speed) / wheel linear velocity. Therefore, in order to improve the calculation accuracy of the slip rate, it is necessary to improve the data accuracy of the reference vehicle speed and the wheel linear velocity. The wheel linear velocity is determined by the wheel speed and the wheel radius, where the wheel radius is a fixed value. Even if the wheel is worn, the change in the wheel radius is small before reaching the scrap standard, and will not affect the calculation of the wheel linear velocity. The measurement technology of wheel speed is relatively mature, so the calculated value of the wheel linear velocity is reliable and accurate. It is only necessary to improve the accuracy of the reference vehicle speed to achieve an improvement in the accuracy of the slip rate.

[0035] First, it is necessary to determine whether there is wheel flywheel (i.e., the wheel is separated from the ground). The degree of separation between the wheel and the ground can be quantified by the mean square difference of the wheel speeds of the four wheels:

[0036]

[0037] Among them, MSE stands for mean square error, which is the separation coefficient, indicating the degree of separation between the wheel and the ground; i represents the wheel speed (linear speed) of the th wheel, Indicates the average wheel speed.

[0038] After the user enters the destination in the navigation software, the navigation software will provide the user with multiple paths to choose from. After the user performs the selection operation, the path from the starting location to the destination selected by the user is the planned path. By reading the navigation data, the planned path selected by the user can be determined. After the user selects the planned path, the navigation software will obtain the path information corresponding to the planned path. The path information includes the speed information of the planned path, congestion information, whether there is construction, etc. Among them, the speed information of the planned path is the navigation estimated speed V of the vehicle traveling on the planned path. g (Generally the predicted value of average vehicle speed).

[0039] Then, the vehicle state information is determined according to the vehicle state sensor, and the current detected vehicle speed V is calculated based on the vehicle state information based on the vehicle dynamics model. d, and compare the current detected speed with the navigation estimated speed to obtain the speed error value representing the relative error: Then, the corresponding reference speed is determined according to the separation coefficient and the speed error value. The reference speed V C The determination process is as follows:

[0040] (1)0≤MSE<K1,

[0041] (2) K1≤MSE<K2, and u <k u When V C =avg(V g ,V d );

[0042] (3) K1≤MSE<K2, and u≥k u When V C =V g ;

[0043] (4)K2≤MSE, and u <k u When V C =f(V C ,V g );

[0044] (5) K2≤MSE, and u≥k u When V C =V g .

[0045] Among them, K1 is the first coefficient threshold, K2 is the second coefficient threshold, and the first coefficient threshold is less than the second coefficient threshold, k2 represents the vehicle speed error threshold, avg(,) represents the calculated average value, f(V C ,V g ) represents the fitting function relationship between the reference vehicle speed and the navigation estimated vehicle speed.

[0046] Among them, considering that some scenes may not be covered by navigation signals, such as tunnel road conditions, etc., resulting in the inability to obtain navigation estimated vehicle speed, in this case, another V C =V d Therefore, the speed is corrected according to the navigation data. The navigation correction strategy will be turned on only when the signal strength of the navigation signal reaches a certain requirement (for example, greater than the preset signal strength threshold). When the navigation signal is abnormal, the navigation correction function is turned off and the vehicle speed is directly adjusted. C =V d .

[0047] Determine the corresponding reference speed V according to the current vehicle operating state C Then, the current slip ratio is determined based on the reference vehicle speed and the linear velocity of the driving wheel. Current slip ratio = (linear velocity of wheel - reference vehicle speed VC ) / wheel linear speed, which enables the vehicle speed to be corrected through navigation data to obtain a more accurate reference speed.

[0048] The road adhesion coefficient is the ratio of adhesion to wheel normal pressure (perpendicular to the road surface). In rough calculations, it can be thought of as the coefficient of static friction between the tire and the road surface. It is determined by both the road surface and the tire. The larger the coefficient, the greater the available adhesion, and the less likely the car is to slip. The road adhesion coefficient determines the tire's ability to adhere to different road surfaces. The value of the road adhesion coefficient is primarily determined by factors such as the road material, road condition, and tire structure, tread pattern, material, and vehicle speed. Generally speaking, dry, good asphalt or concrete roads have the highest adhesion coefficient, reaching 0.7-0.8. Ice and snow roads, on the other hand, have the lowest adhesion coefficient and are most prone to slipping.

[0049] Both braking and driving forces of a vehicle are related to the road adhesion coefficient. When the wheels are in a semi-slipping, semi-rolling state, the road adhesion coefficient is maximized, meaning both braking and driving forces are greater, and the vehicle's stability is also better. When the wheels are completely locked and not rolling, ground adhesion decreases, the road adhesion coefficient decreases, and lateral stability reaches zero, making sideslip and tailspin very likely to occur, which can easily lead to accidents. Therefore, the road adhesion coefficient must be fully utilized to output high torque without slipping.

[0050] Adhesion rate μ is the ratio of the longitudinal force of the tire to the vertical load. There is a certain relationship between adhesion rate μ and slip rate s. As the slip rate increases, adhesion first increases and then decreases. Generally, the maximum adhesion rate under a certain road surface is called the road adhesion coefficient, that is, the road adhesion coefficient is μ. max In general, when the slip ratio is in the range of 0.1 to 0.3, there will be μ max The road adhesion coefficient can be calculated based on the current slip rate and the preset dynamic model.

[0051] Alternatively, the calculation process of the road adhesion coefficient in the dynamic model can be simplified as follows:

[0052]

[0053] Among them, a x represents the lateral acceleration, a y represents longitudinal acceleration, fac represents correction factor, and g represents acceleration due to gravity.

[0054] Step 102: Correct the recognition area of ​​the current scanned image according to the reference vehicle speed and the current front wheel steering angle to obtain a road surface recognition image, and classify the road surface recognition image to obtain an estimated road surface adhesion coefficient and road surface type confidence.

[0055] In machine vision and image processing, a region of interest (ROI) is defined within an image using a box, circle, ellipse, or irregular polygon to outline the desired area. In image processing, a ROI is a region of interest selected from an image that serves as the focus of image analysis. This region is then delineated for further processing. Using a ROI to delineate the desired target can reduce processing time and increase accuracy.

[0056] Therefore, the region of interest can be circled in the current scanned image to obtain the corresponding recognition area. For example, the recognition area can be a fixed rectangular area 3.5m*10m, 3.5m in front of the vehicle. Since the recognition area is circled in the scanned image when the vehicle is stationary, the real-time transmission of the scanned image may be delayed due to network latency while the vehicle is in motion. Therefore, it is necessary to correct the errors caused by network latency during driving to improve the accuracy of the road surface recognition image. The scanned image is a real-time image captured by the vehicle's forward-looking camera.

[0057] Image classification is performed using the collected images in front of the vehicle. This can be performed using machine learning, such as support vector machines and decision trees, or deep learning image classification methods, such as MobileNet and ResNet. This embodiment of the present application employs deep learning methods for image classification, using the MobileNetV2 model to classify road surface recognition images and obtain the road surface type and road surface type confidence α. The corresponding estimated road surface adhesion coefficient is then determined based on the road surface type. Different road surface types have corresponding road surface adhesion coefficient ranges. The relationship between road surface type and road surface adhesion coefficient range is shown in Table 1:

[0058] Table 1 Relationship between road surface types and road adhesion coefficient ranges

[0059] Road surface type Road adhesion coefficient range Asphalt or concrete 0.8～1.2 Asphalt (wet) 0.6～0.85 dirt road 0.6～0.8 sandy land 0.4～0.6 mud 0.4～0.5 grassland 0.3～0.4 Snow (snow tires) 0.3～0.4 Ice surface 0.1～0.25

[0060] The corresponding road adhesion coefficient range is determined according to the road type, and the maximum value in the road adhesion coefficient range is determined as the estimated road adhesion coefficient.

[0061] In deep learning, confidence generally refers to the degree to which a model is certain of its predictions. The model assigns a probability to each category, indicating how likely the model believes the input data belongs to that category. Confidence is part of the model's output and is typically derived using a softmax function, Q function, or other probabilistic function.

[0062] For example, in an image classification task, the model might need to classify an input image as belonging to a specific road surface type. For a specific input image, the MobileNetV2 model might output the following probabilities: Asphalt or Concrete = 0.70, Asphalt (Wet) = 0.25, Dirt Road = 0.05. The model's confidence level for the road surface type being Asphalt or Concrete is the highest, at 70%. Therefore, the road surface type confidence indicates the model's confidence in the image classification result.

[0063] Determining the confidence level of a road surface type has the following key functions:

[0064] 1. Decision Basis: In practical applications, further decisions can be made based on the confidence of the model. For example, in an automated system, relevant actions are only performed when the model is sufficiently confident about a certain prediction result.

[0065] 2. Performance evaluation: By analyzing the model's confidence in its predictions, we can better understand the model's performance, including under what circumstances it is more or less confident, which can help diagnose model deficiencies and make improvements.

[0066] 3. Uncertainty Management: In some applications, it is very important to understand the uncertainty of model predictions. When the model's confidence in the prediction is too low, it may indicate that human intervention or the use of other information sources is needed to make the final decision, because the classification results obtained at this time are unreliable and may have a significant impact on the subsequent calculation process.

[0067] Step 103: Determine a target road adhesion coefficient based on the road type confidence, the calculated road adhesion coefficient, and the estimated road adhesion coefficient, determine a current torque limit based on the target road adhesion coefficient, the front axle load, and the rear axle load, and perform limit control on the requested torque based on the current torque limit.

[0068] In specific implementation, the estimated road adhesion coefficient is expressed as f1; the calculated road adhesion coefficient is expressed as f2; then the relative coefficient error between the estimated road adhesion coefficient and the calculated road adhesion coefficient is Then the process of determining the target road adhesion coefficient f is:

[0069] (1) Confidence of road surface type α>α1, u f <k f , target road adhesion coefficient f = f1.

[0070] (2) Confidence of road surface type α>α1, u f ≥k f , target road adhesion coefficient

[0071] (3) Confidence of road surface type α≤α1, f=f2.

[0072] Among them, α1 represents the confidence threshold, k f Represents the relative error threshold.

[0073] Different values ​​for the target road adhesion coefficient are determined based on the road type confidence level and relative coefficient error, ensuring the accuracy of the target road adhesion coefficient in different situations and the precision of the torque limit. Finally, a table lookup is performed based on the target road adhesion coefficient f, the front axle load m1, and the rear axle load m2 to determine the current torque limit that meets the current operating conditions. The requested torque is then limited to a range less than or equal to the current torque limit, ensuring that excessive requested torque does not cause wheel slip. This ensures that the target road adhesion coefficient is fully utilized while maintaining both power performance and safety.

[0074] In summary, the torque limiting method provided in the embodiments of the present application incorporates navigation-estimated vehicle speed and wheel speed into the calculation of the current slip ratio to correct the current detected vehicle speed, ensuring the accuracy of the current slip ratio, thereby improving the accuracy of the calculated road adhesion coefficient and the precision of the torque limiting. When estimating the road adhesion coefficient, the accuracy of road type identification is improved by correcting the identified area of ​​the current scanned image, thereby improving the precision of the estimated road adhesion coefficient and ensuring the accuracy of the torque limiting. When determining the limiting torque, a road type confidence factor is introduced to narrow the gap between the target road adhesion coefficient and the true value, thereby ensuring the accuracy of the limiting torque determined based on the target road adhesion coefficient. This ensures that when requesting a limit based on the limiting torque, the target road adhesion coefficient can be fully utilized, ensuring that the vehicle's wheels do not slip while maintaining dynamic performance, thereby ensuring driving safety.

[0075] In some embodiments, as Figure 2 As shown, the reference vehicle speed is determined based on the navigation estimated vehicle speed, the current detected vehicle speed, and the wheel speed of each wheel, including:

[0076] Step 201: Determine an average wheel speed based on the wheel speed of each wheel, and determine a separation coefficient based on the average wheel speed and the wheel speed.

[0077] In specific implementation, if the wheel speed of the left front wheel is y1, if the wheel speed of the right front wheel is y2, if the wheel speed of the left rear wheel is y3, if the wheel speed of the right rear wheel is y4, then the average wheel speed is

[0078] The separation coefficient is expressed as the mean square deviation of the wheel speeds, and the calculation process of the separation coefficient is:

[0079]

[0080] Among them, y i represents the wheel speed of wheel No. i.

[0081] Step 202: Determine a vehicle speed error value based on the current detected vehicle speed and the navigation estimated vehicle speed.

[0082] In specific implementation, the speed error value is the relative error value of the current detected speed relative to the navigation estimated speed, so the speed error value is:

[0083]

[0084] In order to facilitate the subsequent determination and calculation process, the absolute value of the difference between the current detected vehicle speed and the navigation estimated vehicle speed is used to calculate the relative error value.

[0085] Step 203: Determine a reference vehicle speed from the average wheel speed, the navigation estimated vehicle speed, and the current detected vehicle speed according to the vehicle speed error value and the separation coefficient.

[0086] In some embodiments, step 203 includes:

[0087] Step 2031: In response to the separation coefficient being less than a preset first coefficient threshold, determining the average wheel speed as a reference vehicle speed.

[0088] In specific implementation, if the separation coefficient is less than the preset first coefficient threshold K1, the wheel speeds of the four wheels are similar, indicating that there is almost no wheel spin, vehicle flying uphill (four wheels hanging in the air), or cross-axle situation, and the credibility of the average wheel speed is high. The average wheel speed is determined as the reference vehicle speed, that is, when 0≤MSE<K1, let To ensure the accuracy of the reference speed.

[0089] Step 2032: In response to the vehicle speed error value being greater than or equal to a preset vehicle speed error threshold, and the separation coefficient being greater than or equal to a preset first coefficient threshold, determining the navigation estimated vehicle speed as the reference vehicle speed.

[0090] In specific implementation, when the speed error value is greater than or equal to the preset speed error threshold, that is, u≥k u When , it means that there is a large difference between the calculated value of the dynamic model and the estimated value predicted by the navigation data, indicating that there is unreliable data in the navigation estimated vehicle speed and the current detected vehicle speed. If the separation coefficient at this time is greater than or equal to the preset first coefficient threshold K1, it means that there is a certain difference between the wheel speeds of the four wheels, which means that the vehicle may have wheel spin, vehicle flying uphill (four wheels hanging in the air), cross axles, etc. during driving, resulting in a large error between the current detected vehicle speed and the average wheel speed. The navigation estimated vehicle speed is more accurate than the current detected vehicle speed. The more accurate navigation estimated vehicle speed is determined as the reference vehicle speed, that is, K1≤MSE, and u≥k u At any time, let V C =V g , to ensure the accuracy of the reference speed.

[0091] Step 2033: In response to the vehicle speed error value being less than the preset vehicle speed error threshold, and the separation coefficient being greater than or equal to the preset first coefficient threshold, and less than the preset second coefficient threshold, the average of the navigation estimated vehicle speed and the current detected vehicle speed is determined as the reference vehicle speed.

[0092] In specific implementation, the speed error value is less than the preset speed error threshold, that is, u <k u When , it indicates that there is a small difference between the calculated value of the dynamic model and the estimated value predicted by the navigation data, indicating that the navigation estimated vehicle speed and the current detected vehicle speed are both reliable data. If the separation coefficient is greater than or equal to the preset first coefficient threshold and less than the preset second coefficient threshold, that is, K1≤MSE<K2, it can be further verified that the probability of wheel spin, vehicle flying uphill (four wheels hanging in the air), and cross axle during driving is low, and the accuracy of the navigation estimated vehicle speed is similar to that of the current detected vehicle speed. Then, the average of the navigation estimated vehicle speed and the current detected vehicle speed is determined as the reference vehicle speed, that is, K1≤MSE<K2, and u <k u When V C =avg(V g ,V d ) to balance the error between the two and ensure the accuracy of the reference speed.

[0093] Step 2034: In response to the vehicle speed error value being less than a preset vehicle speed error threshold, and the separation coefficient being greater than or equal to a preset second coefficient threshold, a navigation-corrected vehicle speed corresponding to the navigation-estimated vehicle speed is determined according to preset navigation detection relationship data, and the navigation-corrected vehicle speed is determined as the reference vehicle speed.

[0094] In specific implementation, the speed error value is less than the preset speed error threshold, that is, u <k u When , it means that there is a small difference between the calculated value of the dynamic model and the estimated value predicted by the navigation data, indicating that the navigation estimated vehicle speed and the current detected vehicle speed are both reliable data. If the separation coefficient at this time is greater than or equal to the preset second coefficient threshold, that is, K2≤MSE, it means that there is a high probability of wheel spinning, vehicle flying uphill (four wheels hanging in the air), cross-axle, etc. during driving. The current detected vehicle speed and average wheel speed calculated according to the vehicle state parameters are not reliable. Because the navigation estimated vehicle speed is close to the current detected vehicle speed, the credibility of the navigation estimated vehicle speed is also low. At this time, it is necessary to determine the navigation corrected vehicle speed corresponding to the navigation estimated vehicle speed according to the preset navigation detection relationship data, and determine the navigation corrected vehicle speed as the reference vehicle speed, that is, when K2≤MSE, and u <k u When V C =f(V C ,V g ); where f(V C,V g ) represents the fitting function relationship between the reference speed and the navigation estimated speed. In this case, the navigation estimated speed is used as input, and the corresponding output value is used as the reference speed to correct the speed and ensure the accuracy of the reference speed.

[0095] In some embodiments, as Figure 3 As shown, the recognition area of ​​the current scanned image is corrected according to the reference vehicle speed and the current front wheel angle to obtain a road recognition image, including:

[0096] Step 301: Determine the current network delay and the vertex pixel coordinates of the identification area.

[0097] When implementing it specifically, Figure 4a The fan-shaped area in the image represents the current scanned image. The recognition area when the vehicle is stationary is Figure 4a The area corresponding to the dotted box is a fixed area of ​​3.5m×10m, 3.5m in front of the vehicle, that is, Xmin=3.5m, Xmax=3.5+10=13.5m, Ymin=-1.75m, Ymin=1.75m (the distance value in the vehicle coordinate system in the actual space). At this time, the vertex pixel coordinates of the recognition area in the current scanned image are x_spd[i], i∈(1,2,3,4), which means Figure 4a The pixel coordinates of the four vertices of the dashed box.

[0098] Step 302: Determine the longitudinal offset according to the reference vehicle speed and the current network delay.

[0099] In specific implementation, due to network delay, the current scanned image is relatively lagging, so the recognition area needs to be offset forward (away from the vehicle direction) to ensure that the recognition area is the estimated area of ​​the previous scanned image that is transmitted later, so as to offset the offset caused by network delay. The calculation formula of the longitudinal offset Plex of the forward offset is:

[0100] Plex=V c ×(1000 / FPS+90)×0.034 / x

[0101] Among them, FPS is the camera frame rate of the vehicle's front-view camera, 90 represents the network delay, and 0.034 is the scale of the scanned image. When the vehicle is moving straight forward, Figure 4a As shown, the recognition area is offset forward by Plex.

[0102] Step 303: Determine the lateral offset according to the vertex pixel coordinates and the current front wheel steering angle.

[0103] In specific implementation, if the vehicle is Figure 4bFor the turning driving condition shown, the lateral offset pts_l needs to be determined based on the vertex pixel coordinates and the current front wheel angle:

[0104]

[0105] Where v represents the front wheel turning angle and L represents the vehicle wheelbase.

[0106] Step 304: Correct the position of the recognition area according to the longitudinal offset and the lateral offset to obtain a corrected recognition area, and select an image in the current scanned image according to the corrected recognition area to obtain a road surface recognition image.

[0107] In specific implementation, since the vehicle will move forward at the same time when turning, there will be both lateral and longitudinal offsets. Therefore, the recognition area in the stationary state needs to be corrected in two directions, that is, the longitudinal offset is offset forward and the lateral offset is offset in the direction of turning. The corrected recognition area is as follows: Figure 4b As shown in the figure, the part of the current scanned image selected by the corrected recognition area is the road surface recognition image. By classifying and recognizing the road surface recognition image, the corresponding road surface type and road surface type confidence can be determined, providing data support for determining the estimated road adhesion coefficient.

[0108] In some embodiments, as Figure 5 As shown, the road surface recognition image is classified to obtain the estimated road adhesion coefficient and road surface type confidence, including:

[0109] Step 501: performing image classification on the road surface recognition image to obtain the road surface type and road surface type confidence corresponding to the road surface recognition image.

[0110] In specific implementations, collected road surface recognition images are used for image classification. Image classification can be performed using machine learning, such as support vector machines, decision trees, and other machine learning models, or deep learning image classification methods, such as MobileNet and ResNet. This embodiment of the present application employs deep learning methods for image classification, using a trained MobileNetV2 model to classify road surface recognition images and obtain a road surface type and road surface type confidence α. The MobileNetV2 model can be trained using a preset road surface image library to obtain a trained MobileNetV2 model.

[0111] Step 502: Determine a search value corresponding to the road surface type according to a preset type coefficient relationship, and determine the search value as the estimated road surface adhesion coefficient.

[0112] In specific implementation, the type coefficient relationship can be, optionally, a table showing a relationship between road surface type and road surface adhesion coefficient range as shown in Table 1, or a table showing a relationship between road surface type and road surface adhesion coefficient value. For example, when the type coefficient relationship is a table showing a relationship between road surface type and road surface adhesion coefficient range as shown in Table 1,

[0113] For example, in an image classification task, the model may need to classify the input image into the corresponding road surface type. For a road surface recognition image, the MobileNetV2 model might output the following probabilities: Asphalt or Concrete = 0.70, Asphalt (Wet) = 0.25, Dirt Road = 0.05. The model's confidence level for the road surface type being asphalt or concrete is the highest, at 70%. The corresponding road surface type is asphalt or concrete, and the corresponding road adhesion coefficient range is 0.8 to 1.2. The corresponding search value is the maximum value of the road adhesion coefficient range, so the search value is 1.2, and the corresponding estimated road adhesion coefficient is 1.2, achieving a single estimate of the corresponding road adhesion coefficient.

[0114] In some embodiments, as Figure 6 As shown, the target road adhesion coefficient is determined according to the road type confidence, the calculated road adhesion coefficient and the estimated road adhesion coefficient, including:

[0115] Step 601: In response to the road type confidence being less than a preset confidence threshold, the calculated road adhesion coefficient is determined as a target road adhesion coefficient.

[0116] In specific implementation, the estimated road adhesion coefficient is represented as f1; the calculated road adhesion coefficient is represented as f2; if the road type confidence is less than the preset confidence threshold, it means that the credibility of the estimated road adhesion coefficient is low. At this time, the calculated road adhesion coefficient is determined as the target road adhesion coefficient, that is, when the road type confidence α is less than α1, let f = f2 to ensure the accuracy of the target road adhesion coefficient.

[0117] Step 602: In response to the road type confidence being greater than or equal to a preset confidence threshold, determining a relative coefficient error based on the calculated road adhesion coefficient and the estimated road adhesion coefficient.

[0118] In specific implementation, if the road type confidence is greater than or equal to the preset confidence threshold, it means that the estimated road adhesion coefficient is more reliable. At this time, the relative coefficient error is determined based on the calculated road adhesion coefficient and the estimated road adhesion coefficient. Used to represent the error of the calculated road adhesion coefficient f2 relative to the estimated road adhesion coefficient f1.

[0119] Step 603: In response to the relative coefficient error being less than a preset relative error threshold, the estimated road adhesion coefficient is determined as the target road adhesion coefficient.

[0120] In specific implementation, if the relative coefficient error is less than the preset relative error threshold k f , indicating that the calculated road adhesion coefficient f2 and the estimated road adhesion coefficient f1 are close. Since the road type confidence is large at this time, the estimated road adhesion coefficient is determined as the target road adhesion coefficient. That is, the road type confidence α≥α1, u f <k f When f=f1, the target road adhesion coefficient is guaranteed to be accurate.

[0121] Step 604: In response to the relative coefficient error being greater than or equal to a preset relative error threshold, determining an average value of the calculated road adhesion coefficient and the estimated road adhesion coefficient as a target road adhesion coefficient.

[0122] In specific implementation, if the relative coefficient error is greater than or equal to the preset relative error threshold k f , indicating that the calculated road adhesion coefficient f2 and the estimated road adhesion coefficient f1 have a large difference. In this case, the calculated road adhesion coefficient is expressed as the average value of f2 and the estimated road adhesion coefficient f1. Determine the target road adhesion coefficient to balance the error and ensure the accuracy of the target road adhesion coefficient. That is, the road type confidence α≥α1, u f ≥k f season To ensure the accuracy of the target road adhesion coefficient.

[0123] In some embodiments, as Figure 7 As shown, the current limit torque is determined according to the target road adhesion coefficient, the front axle load and the rear axle load, including:

[0124] Step 701: Determine a target coefficient torque relationship in a preset relationship database according to the front axle load and the rear axle load.

[0125] During specific implementation, a four-dimensional relationship of front axle load-rear axle load-target road adhesion coefficient-limiting torque exists in a preset relational database. Since the front axle load and the rear axle load can be monitored in real time, the target coefficient torque relationship is first determined in the preset relational database through the front axle load and the rear axle load, that is, the four-dimensional relationship is reduced to a two-dimensional target coefficient torque relationship. The target coefficient torque relationship is a two-dimensional relationship between the target road adhesion coefficient and the limiting torque.

[0126] Step 702: Determine a search torque corresponding to a target road adhesion coefficient according to the target coefficient-torque relationship, and determine the search torque as a current limit torque.

[0127] In specific implementation, in the two-dimensional target coefficient torque relationship, the target road adhesion coefficient is the input value, and the search is performed based on the input value. The output search torque is the current limit torque, thereby limiting the requested torque, ensuring power while preventing wheel slippage and ensuring driving safety.

[0128] In some embodiments, as Figure 8 As shown, the requested torque is limited and controlled according to the current limited torque, including:

[0129] Step 801 : In response to the requested torque being less than or equal to the current torque limit, the drive motor is controlled to output according to the requested torque.

[0130] In specific implementation, if the requested torque is less than or equal to the current limit torque, it means that the output with the requested torque will not cause wheel slippage. At this time, there is no safety risk. The main focus is on power, and the drive motor is controlled according to the requested torque to output to meet the user's request.

[0131] Step 802 : In response to the requested torque being greater than the current torque limit, the drive motor is controlled to output according to the current torque limit.

[0132] During specific implementation, if the requested torque is greater than or equal to the current limited torque, it means that outputting the requested torque will cause wheel slippage. At this time, there is a safety risk. With safety as the priority, the maximum torque that will not cause slippage - the current limited torque is used as the actual requested torque, and the drive motor is controlled to output according to the current limited torque, ensuring power while preventing wheel slippage and ensuring driving safety.

[0133] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0134] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0135] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a torque limiting device.

[0136] refer to Figure 9 , the torque limiting device comprises:

[0137] a first coefficient determination module 10 configured to: determine a reference vehicle speed based on the navigation estimated vehicle speed, the current detected vehicle speed, and the wheel speed of each wheel; determine a current slip ratio based on the reference vehicle speed and the wheel linear speed of the driving wheel; and determine and calculate a road adhesion coefficient based on the current slip ratio and a preset dynamic model;

[0138] The second coefficient determination module 20 is configured to: modify the recognition area of ​​the current scan image according to the reference vehicle speed and the current front wheel steering angle to obtain a road surface recognition image, and perform image classification on the road surface recognition image to obtain an estimated road adhesion coefficient and a road surface type confidence;

[0139] The torque limit control module 30 is configured to determine a target road adhesion coefficient based on the road type confidence, the calculated road adhesion coefficient, and the estimated road adhesion coefficient, determine a current torque limit based on the target road adhesion coefficient, the front axle load, and the rear axle load, and perform limit control on the requested torque based on the current torque limit.

[0140] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0141] The device of the above embodiment is used to implement the corresponding torque limiting method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0142] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the torque limiting method described in any of the above embodiments is implemented.

[0143] Figure 10 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0144] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0145] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0146] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0147] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0148] The bus 1050 comprises a path for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0149] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0150] The electronic device of the above embodiment is used to implement the corresponding torque limiting method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0151] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the torque limiting method described in any of the above embodiments.

[0152] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0153] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the torque limiting method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0154] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a vehicle, including the electronic device or torque limiting device of the above-mentioned embodiment, and executing the torque limiting method described in any of the above embodiments through the electronic device or torque limiting device of the above-mentioned embodiment, and having the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0155] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0156] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the disclosed technical solution based on the prompt message.

[0157] As an optional but non-limiting implementation, in response to a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0158] It is understandable that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0159] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application is limited to these examples. In line with the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0160] In addition, for simplicity of description and discussion, and in order not to make the embodiment of the application difficult to understand, the known power supply / ground connection with integrated circuit (IC) chip and other components may or may not be shown in the accompanying drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the application difficult to understand, and this also takes into account the following fact, that is, the details of the embodiment of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (for example, circuit) are set forth to describe exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiment of the application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0161] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0162] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A torque limiting method, characterized in that: include: determining a reference vehicle speed based on the navigation estimated vehicle speed, the current detected vehicle speed, and the wheel speed of each wheel, determining a current slip ratio based on the reference vehicle speed and the wheel linear speed of the driving wheel, and calculating a road adhesion coefficient based on the current slip ratio and a preset dynamic model; Determining the reference vehicle speed based on the navigation estimated vehicle speed, the current detected vehicle speed, and the wheel speed of each wheel includes: determining an average wheel speed based on the wheel speed of each wheel, and determining a separation coefficient based on the average wheel speed and the wheel speed; quantifying the degree of separation between the wheel and the ground using the mean square difference of the wheel speeds of the four wheels, the mean square difference being the separation coefficient; determining a vehicle speed error value based on the current detected vehicle speed and the navigation estimated vehicle speed; and determining the reference vehicle speed from the average wheel speed, the navigation estimated vehicle speed, and the current detected vehicle speed based on the vehicle speed error value and the separation coefficient. Correcting the recognition area of ​​the current scanned image according to the reference vehicle speed and the current front wheel steering angle to obtain a road surface recognition image, and performing image classification on the road surface recognition image to obtain an estimated road surface adhesion coefficient and a road surface type confidence; A target road adhesion coefficient is determined based on the road type confidence, the calculated road adhesion coefficient, and the estimated road adhesion coefficient, and a current limit torque is determined based on the target road adhesion coefficient, the front axle load, and the rear axle load, and the requested torque is limited and controlled based on the current limit torque.

2. The torque limiting method according to claim 1, characterized in that: The determining the reference vehicle speed from the average wheel speed, the navigation estimated vehicle speed, and the current detected vehicle speed according to the vehicle speed error value and the separation coefficient includes: In response to the separation coefficient being less than a preset first coefficient threshold, determining the average wheel speed as the reference vehicle speed; In response to the vehicle speed error value being greater than or equal to a preset vehicle speed error threshold, and the separation coefficient being greater than or equal to a preset first coefficient threshold, determining the navigation estimated vehicle speed as the reference vehicle speed; In response to the vehicle speed error value being less than a preset vehicle speed error threshold, and the separation coefficient being greater than or equal to a preset first coefficient threshold and less than a preset second coefficient threshold, determining an average of the navigation estimated vehicle speed and the current detected vehicle speed as the reference vehicle speed; In response to the vehicle speed error value being less than a preset vehicle speed error threshold and the separation coefficient being greater than or equal to a preset second coefficient threshold, a navigation-corrected vehicle speed corresponding to the navigation-estimated vehicle speed is determined according to preset navigation detection relationship data, and the navigation-corrected vehicle speed is determined as the reference vehicle speed.

3. The torque limiting method according to claim 1, characterized in that: The step of correcting the recognition area of ​​the current scanned image according to the reference vehicle speed and the current front wheel steering angle to obtain a road surface recognition image includes: Determining the current network latency and vertex pixel coordinates of the identified area; determining a longitudinal offset according to the reference vehicle speed and the current network delay; determining a lateral offset according to the vertex pixel coordinates and the current front wheel turning angle; The position of the recognition area is corrected according to the longitudinal offset and the lateral offset to obtain a corrected recognition area, and an image is selected from the current scanned image according to the corrected recognition area to obtain the road surface recognition image.

4. The torque limiting method according to claim 1, characterized in that: The performing image classification on the road surface recognition image to obtain an estimated road surface adhesion coefficient and a road surface type confidence level includes: performing image classification on the road surface recognition image to obtain a road surface type corresponding to the road surface recognition image and a confidence level of the road surface type; A retrieval value corresponding to the road surface type is determined according to a preset type coefficient relationship, and the retrieval value is determined as the estimated road surface adhesion coefficient.

5. The torque limiting method according to claim 1, characterized in that: The determining of a target road surface adhesion coefficient according to the road surface type confidence, the calculated road surface adhesion coefficient, and the estimated road surface adhesion coefficient includes: In response to the road surface type confidence being less than a preset confidence threshold, determining the calculated road surface adhesion coefficient as the target road surface adhesion coefficient; In response to the road type confidence being greater than or equal to a preset confidence threshold, determining a relative coefficient error based on the calculated road adhesion coefficient and the estimated road adhesion coefficient; In response to the relative coefficient error being less than a preset relative error threshold, determining the estimated road adhesion coefficient as the target road adhesion coefficient; In response to the relative coefficient error being greater than or equal to a preset relative error threshold, an average value of the calculated road adhesion coefficient and the estimated road adhesion coefficient is determined as the target road adhesion coefficient.

6. The torque limiting method according to claim 1, characterized in that: The determining of the current torque limit according to the target road adhesion coefficient, the front axle load, and the rear axle load includes: Determining a target coefficient torque relationship in a preset relationship database according to the front axle load and the rear axle load; A search torque corresponding to the target road surface adhesion coefficient is determined according to the target coefficient-torque relationship, and the search torque is determined as the current limit torque.

7. The torque limiting method according to claim 1, characterized in that: The limiting control of the requested torque according to the current limited torque includes: In response to the requested torque being less than or equal to the current limit torque, controlling the drive motor to output according to the requested torque; In response to the requested torque being greater than the current limit torque, the drive motor is controlled to output power according to the current limit torque.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

9. A vehicle, characterized in that: Comprising the electronic device as claimed in claim 8.

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