Locking degree reminding method and device and vehicle

Abstract

The invention relates to a locking degree reminding method and device and a vehicle. The method comprises the following steps: acquiring vehicle operation data of a target vehicle at the current moment and tire operation data of each tire in the target vehicle; for each tire in the target vehicle, according to the vehicle operation data and the tire operation data corresponding to the tire, respectively determining a current slip rate and a corresponding predicted slip rate corresponding to the tire at the current moment; determining a target locking margin of the target vehicle at the current moment according to the current slip rate corresponding to each tire and the corresponding predicted slip rate; and according to the current driving mode of the target vehicle and the target locking allowance, carrying out locking alarm reminding. By adopting the method, the locking degree of the vehicle can be accurately pre-warned and reminded.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle braking, and particularly to a method and device for reminding of locking degree and a vehicle. Background Art

[0002] In modern vehicle safety technologies, the Anti-lock Braking System (ABS) plays a crucial role. It can effectively prevent the wheels from locking during emergency braking, thus maintaining the steering ability and stability of the vehicle and greatly improving driving safety. When the traditional ABS system brakes, users can perceive the triggering of the ABS through hearing, touch (pedal force), and vision (ABS indicator light), but it does not provide information such as "brake margin" or "ABS triggering critical value" to the driver in real time.

[0003] Traditional technologies often rely only on the estimation of a fixed road adhesion coefficient, which easily leads to a decrease in control accuracy and reminder accuracy. Although some solutions attempt to use look-up tables or linear gain calibration, they cannot perform real-time adaptive optimization for different road and tire conditions, resulting in misjudgment or frequent ABS shocks under emergency conditions and making it difficult to meet the refined braking warning requirements. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, and vehicle for reminding of locking degree that can accurately warn of the locking degree of a vehicle.

[0005] In a first aspect, the present application provides a method for reminding of locking degree, including:

[0006] Obtaining the vehicle operation data of a target vehicle at the current moment and the tire operation data of each tire in the target vehicle;

[0007] For each tire in the target vehicle, respectively determining the current slip ratio and the corresponding predicted slip ratio of the tire at the current moment according to the vehicle operation data and the tire operation data corresponding to the tire;

[0008] Determining the target locking margin of the target vehicle at the current moment according to the current slip ratio and the corresponding predicted slip ratio of each tire;

[0009] Performing locking alarm reminder according to the current driving mode of the target vehicle and the target locking margin.

[0010] In a second aspect, the present application further provides a device for reminding of locking degree, including:

[0011] A data acquisition module, configured to acquire the vehicle operation data of the target vehicle at the current moment and the tire operation data of each tire in the target vehicle;

[0012] A first determination module, configured to, for each tire in the target vehicle, respectively determine the current slip ratio and the corresponding predicted slip ratio of the tire at the current moment according to the vehicle operation data and the tire operation data corresponding to the tire;

[0013] A second determination module, configured to determine the target locking margin of the target vehicle at the current moment according to the current slip ratio and the corresponding predicted slip ratio of each tire;

[0014] A locking warning module, configured to perform a locking alarm reminder according to the current driving mode of the target vehicle and the target locking margin.

[0015] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps involved in the first aspect are implemented.

[0016] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps involved in the first aspect are implemented.

[0017] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps involved in the first aspect are implemented.

[0018] In a sixth aspect, the present application further provides a vehicle, in which a device for implementing the method steps provided in the first aspect is provided.

[0019] After the above-mentioned locking degree reminder method, device and vehicle acquire the vehicle operation data of the target vehicle at the current moment and the tire operation data of each tire in the target vehicle, for each tire in the target vehicle, according to the vehicle operation data and the tire operation data corresponding to the tire, respectively determine the current slip ratio and the corresponding predicted slip ratio of the tire in real time at the current moment. Since the current slip ratio can characterize the slip ratio situation of the tire in real time, combined with the predicted slip ratio obtained by prediction, the target locking margin of the target vehicle at the current moment is comprehensively determined, so that the real-time situation of the target vehicle and the prediction situation of the slip ratio of the target vehicle are comprehensively considered in the process of determining the target locking margin, thereby improving the accuracy of the determination result of the target locking margin; further, combined with the actual current driving mode of the target vehicle and the target locking margin with higher accuracy, a locking alarm reminder is performed, improving the timeliness and accuracy of the reminder. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the accompanying drawings required for describing the embodiments of the present application or related technologies. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0021] Figure 1 It is an application environment diagram of the locking degree reminder method in an embodiment;

[0022] Figure 2 It is a flowchart of the locking degree reminder method in an embodiment;

[0023] Figure 3 It is a flowchart of determining the current slip ratio and the corresponding predicted slip ratio in an embodiment;

[0024] Figure 4 It is a flowchart of determining the target locking margin in an embodiment;

[0025] Figure 5 It is a flowchart of determining the target locking margin in another embodiment;

[0026] Figure 6 It is a flowchart of determining the target locking margin in yet another embodiment;

[0027] Figure 7 It is a flowchart of performing a locking alarm reminder on the target vehicle in an embodiment;

[0028] Figure 8 It is a flowchart of determining the weight of the target locking margin in an embodiment;

[0029] Figure 9 It is a structural block diagram of the locking degree reminder device in an embodiment;

[0030] Figure 10 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0031] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further details the present application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0032] The locking degree reminder method provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the anti-lock warning system 110 in the target vehicle is used to monitor the anti-lock margin of the target vehicle in real time and give warnings about anti-lock conditions; the data acquisition device 120 is used to acquire various operating data in the target vehicle. The data acquisition device 120 includes, but is not limited to, sensing devices installed on the tires and acquisition devices in the target vehicle for monitoring the vehicle operating conditions.

[0033] In an exemplary embodiment, as Figure 2 shown, a method for reminding of anti-lock degree is provided. Taking the anti-lock warning system 110 applied in Figure 1 as an example, the specific steps are as follows:

[0034] S210, obtain the vehicle operating data of the target vehicle at the current moment and the tire operating data of each tire in the target vehicle.

[0035] Among them, the target vehicle can be any vehicle equipped with an anti-lock warning system; the vehicle operating data characterizes the operating conditions of the target vehicle at the current moment. For example, the vehicle operating data includes, but is not limited to, the current vehicle speed, the current road surface adhesion coefficient, and the current acceleration, etc. Among them, the current vehicle speed is the driving speed of the target vehicle at the current moment; the current road surface adhesion coefficient characterizes the adhesion ability of the tires of the target vehicle on the road surface at the current moment.

[0036] Among them, the tire operating data of any tire characterizes the operating conditions of the tire of the target vehicle at the current moment. For example, the tire operating data includes, but is not limited to, the current pressure increment, the current tire pressure, the current tire temperature, the current axle load information, and the current wheel speed corresponding to the tire, etc. Among them, the current pressure increment is the growth amount of the braking pressure of the tire compared with the previous moment at the current moment; the current tire pressure is the tire pressure of the tire at the current moment; the current tire temperature is the temperature of the tire at the current moment; the current axle load information characterizes the load condition borne by the axle of the tire at the current moment; the current wheel speed corresponding to the tire is the wheel speed of the tire at the current moment.

[0037] Optionally, the tire operating data of each tire in the target vehicle at the current moment can be obtained through sensors installed on each tire and the tire monitoring system in the target vehicle. At the same time, the vehicle operating data of the target vehicle at the current moment can be obtained from the monitoring devices in the target vehicle through the bus. Exemplarily, for the tire operating data of any tire, the current tire pressure in the tire operating data can be obtained from the tire pressure monitoring system.

[0038] Optionally, for some vehicle operation data, it may not be directly obtainable by monitoring devices. In this case, operations need to be performed on the obtainable data to calculate some vehicle operation states that cannot be directly obtained. Exemplarily, for the current road surface adhesion coefficient in the vehicle operation state, the wheel speed and longitudinal and lateral accelerations of the target vehicle at the current moment can be obtained, and based on the obtained wheel speed and longitudinal and lateral accelerations of the vehicle, the current road surface adhesion coefficient can be calculated.

[0039] S220. For each tire in the target vehicle, based on the vehicle operation data and the tire operation data corresponding to the tire, respectively determine the current slip ratio and the corresponding predicted slip ratio of the tire at the current moment.

[0040] Among them, the slip ratio corresponding to the tire is the proportion of the sliding component in the movement of the wheel, reflecting the degree of sliding of the tire relative to the ground during braking or driving; the current slip ratio of the tire is the slip ratio of the tire at the current moment; the corresponding predicted slip ratio of the tire is the slip ratio of the tire predicted through technical means.

[0041] It should be noted that since the directly obtained vehicle operation data and the tire operation data corresponding to the tire may have defects such as data loss and cannot be directly used, therefore, the obtained vehicle operation data and the tire operation data corresponding to the tire can be preprocessed. For example, the preprocessing can include low-pass filtering, denoising, data synchronization and timestamp alignment processing, etc.

[0042] Optionally, for any tire in the target vehicle, based on the vehicle operation data of the target vehicle and the tire operation data corresponding to the tire, analyze the degree of sliding of the tire of the target vehicle relative to the ground; further, based on the degree of sliding of the tire of the target vehicle relative to the ground, determine the current slip ratio corresponding to the tire at the current moment.

[0043] In addition, it is also necessary to predict the slip ratio corresponding to the tire of the target vehicle at the current moment through a theoretical formula or a trained neural network model based on the vehicle operation data and the tire operation data corresponding to the tire, so as to obtain the predicted slip ratio corresponding to the tire at the current moment.

[0044] S230. Based on the current slip ratio and the corresponding predicted slip ratio of each tire, determine the target anti-lock margin of the target vehicle at the current moment.

[0045] Among them, the target anti-lock margin of the target vehicle is the maximum braking torque range within which the wheels can maintain a certain rolling and not completely lock during the braking process of the target vehicle at the current moment. This range ensures that the target vehicle can maintain direction control and stability during emergency braking, preventing the vehicle from skidding or losing control.

[0046] It can be understood that since the current slip ratio corresponding to the tire is calculated according to the actual situation, while the predicted slip ratio corresponding to the tire is predicted by technical means, therefore, the current slip ratio can be adjusted by combining the predicted slip ratios corresponding to each tire to obtain a relatively accurate slip ratio.

[0047] Furthermore, based on the relatively accurate slip ratio obtained, a slip ratio analysis software can be used to evaluate the torque changes and wheel sliding conditions of each tire during the braking process, and according to the evaluation results, calculate the target anti-lock margin of the target vehicle at the current moment.

[0048] S240. Perform an anti-lock alarm reminder according to the current driving mode and the target anti-lock margin of the target vehicle.

[0049] Optionally, the driving mode of the target vehicle can be comprehensively considered, and different anti-lock margin thresholds can be set for each driving mode; furthermore, determine the anti-lock margin threshold corresponding to the current driving mode, and compare the determined anti-lock margin threshold with the target anti-lock margin. Correspondingly, if the target anti-lock margin exceeds the anti-lock margin threshold, an anti-lock alarm reminder is required. It can be understood that the anti-lock alarm reminder methods include but are not limited to warning light flashing reminder, voice reminder, pedal vibration reminder, etc.

[0050] In the above anti-lock degree reminder method, after obtaining the vehicle operation data of the target vehicle and the tire operation data of each tire in the target vehicle at the current moment, for each tire in the target vehicle, according to the vehicle operation data and the tire operation data corresponding to the tire, respectively determine the current slip ratio and the corresponding predicted slip ratio of the tire in real time at the current moment. Since the current slip ratio can represent the slip ratio situation of the tire in real time, combined with the predicted slip ratio obtained by prediction, comprehensively determine the target anti-lock margin of the target vehicle at the current moment, so that the real-time situation of the target vehicle and the prediction situation of the slip ratio of the target vehicle are comprehensively considered during the determination of the target anti-lock margin, thereby improving the accuracy of the determination result of the target anti-lock margin; furthermore, perform an anti-lock alarm reminder in combination with the actual current driving mode of the target vehicle and the more accurate target anti-lock margin, improving the timeliness and accuracy of the reminder.

[0051] Based on the technical solutions of the above embodiments, in order to ensure the accuracy of the determined current slip ratio and the corresponding predicted slip ratio of the tire at the current moment, in an exemplary embodiment, it is determined that the vehicle operation data includes the current vehicle speed and the current road surface adhesion coefficient; the tire operation data includes the current pressure increment and the current wheel speed corresponding to the tire. On this basis, the present application also provides an optional embodiment, as Figure 3 shown, in this optional embodiment, the determination steps of the current slip ratio and the corresponding predicted slip ratio of S220 are refined, and this step includes:

[0052] S310. Determine the current slip ratio corresponding to the tire at the current moment according to the current vehicle speed and the current wheel speed corresponding to the tire.

[0053] Optionally, since the current slip ratio of the tire characterizes the degree of sliding of the tire relative to the ground at the current moment, therefore, the degree of sliding of the tire relative to the ground can be calculated and analyzed through the current vehicle speed of the target vehicle and the current wheel speed of the tire. Exemplarily, for any tire of the target vehicle, the current slip ratio of the tire can be calculated by the following formula (1):

[0054] (1)

[0055] Where, is the current slip ratio of the th tire; is the current vehicle speed; is the th current wheel speed of the tire; is the protection threshold to avoid the situation where formula (1) cannot be calculated when the current vehicle speed is 0. It should be noted that when , .

[0056] S320. Determine the predicted slip ratio corresponding to the tire at the current moment according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment, and the current slip ratio corresponding to the tire.

[0057] Optionally, a slip ratio analysis software can be introduced, and the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment, and the current slip ratio corresponding to the tire are input into the slip ratio analysis software, so that the slip ratio analysis software predicts the predicted slip ratio corresponding to the tire at the current moment according to a preset prediction formula or prediction algorithm.

[0058] In this embodiment, by comprehensively considering the current vehicle speed and the current wheel speed of the target vehicle, the accuracy of the determined current slip ratio is ensured; in addition, by comprehensively analyzing the pressure and slip ratio of the tire, as well as the vehicle speed and road surface adhesion coefficient of the target vehicle, the accuracy of the predicted slip ratio corresponding to the tire at the current moment is ensured.

[0059] Based on the technical solutions of the above embodiments, in order to accurately predict the predicted slip ratio corresponding to the tire at the current moment, the present application also provides an optional embodiment. In this optional embodiment, the braking control step in S320 is refined. In the embodiment of the present application, three prediction methods for the predicted slip ratio corresponding to the tire at the current moment are provided.

[0060] Optionally, the prediction method for the predicted slip ratio of the tire at the first current moment is as follows: Based on a preset calibration parameter table, according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment, and the current slip ratio corresponding to the tire, determine the predicted slip ratio of the tire at the current moment. It should be noted that when the braking pressure of the vehicle changes, the slip ratio will change accordingly. In addition, the current slip ratio will also affect the future slip ratio. On this basis, a first calibration parameter and a second calibration parameter are introduced to quantify the influence of the pressure increment of the braking pressure and the current slip ratio on the predicted slip ratio. Among them, the first calibration parameter represents the change amount of the slip ratio caused by the change of the unit pressure increment; the second calibration parameter represents the change rate of the predicted slip ratio caused by the change of the current slip ratio. And the first calibration parameter and the second calibration parameter can generally be obtained by looking up the preset calibration parameter table. The preset calibration parameter table is a parameter table preset for adjusting and optimizing the braking system performance of the target vehicle. It should be noted that the preset calibration parameter table contains parameters for adjusting the braking system performance of the target vehicle under the current working conditions, such as the first calibration parameter and the second calibration parameter; the preset calibration parameter table can be determined through experimental tests. For example, various working condition tests are carried out in the laboratory or on the actual road, data is collected, and professional software is used to process and analyze the collected data to determine the optimal data of the preset calibration parameter table, and finally a specific calibration table is generated according to the analysis results.

[0061] Exemplarily, tests can be carried out under various combinations of working conditions. As shown in Table 1 below, it is a combination of various working conditions provided. The method of controlling variables can be adopted to carry out tests under various working conditions. The specific test steps are as follows: Set a specific road surface adhesion coefficient, a stable vehicle speed, and establish an initial braking pressure to make the vehicle reach a stable initial slip ratio; further, apply a known pressure increment (usually 0.1 - 0.3 MPa), and record the change of the slip ratio with time during the process. For example, fix the road surface adhesion coefficient at 0.9, the initial slip ratio at 0.05, and the vehicle speed at 20 unchanged, and change the pressure increment for testing. Then, fix the road surface adhesion coefficient at 0.9, the initial slip ratio at 0.05, and the vehicle speed at 50 unchanged, and change the pressure increment for testing, and so on. It should be noted that the initial slip ratio in Table 1 is 0.05, and different initial slip ratios, such as 0.1, 0.15, 0.2, etc., also need to be repeated.

[0062] Table 1 Combinations of various working conditions

[0063]

[0064] Furthermore, record the change of the slip ratio with time during the test process, and calculate the slip ratio increment. For each working condition point, establish the average value of multiple tests, and use multiple regression analysis to quantify the influence degree of the pressure increment and the initial slip ratio on the slip ratio.

[0065] On this basis, for any tire in the target vehicle, the calibration parameter for adjusting the current slip ratio corresponding to the current vehicle speed, current road surface adhesion coefficient, and current pressure increment of the target vehicle can be found from the preset calibration parameter table. Further, the current slip ratio corresponding to the tire is adjusted according to the found calibration parameter to obtain the predicted slip ratio corresponding to the tire. In addition, to ensure the accuracy of the determined predicted slip ratio, the current pressure increment and current slip ratio of the tire can be adjusted respectively, and the current slip ratio is compensated according to the adjusted values to obtain the predicted slip ratio corresponding to the tire. Specifically, it can be expressed by the following formula (2):

[0066] (2)

[0067] ‌Wherein, is a function that limits the function value within the range of [0, 1]; is the predicted slip ratio of the th tire predicted by the first method; is the current pressure increment; is the peak slip ratio determined in the test stage. is the change amount of the slip ratio caused by the change of the unit pressure increment obtained by looking up the table in the preset calibration coefficient table using the current road surface adhesion coefficient ( ), current vehicle speed ( ), and peak slip ratio ( ); is the change amount of the predicted slip ratio caused by the change of the unit current slip ratio obtained by looking up the table in the preset calibration coefficient table using the current road surface adhesion coefficient ( ), current vehicle speed ( ), and peak slip ratio ( ). It should be noted that and can be found from the same preset calibration coefficient table, or can be found from the corresponding preset calibration coefficient tables respectively.

[0068] At a fixed vehicle speed, the slip ratio of the vehicle will change with the increase of the road surface adhesion coefficient: First, the slip ratio will increase steadily with the increase of the road surface adhesion coefficient. After the slip ratio increases to a certain value, it will decrease with the increase of the road surface adhesion coefficient. The maximum value of the slip ratio in this process is the peak slip ratio ( ). In the embodiments of the present application, curves of the slip ratio varying with the road surface adhesion coefficient at different vehicle speeds can be constructed, and the area on the left side of the slip ratio peak is defined as the stable area, while the area on the right side of the slip ratio is defined as the unstable area. If the current slip ratio is less than the slip ratio peak, it is determined that the current slip ratio is in the stable area. Correspondingly, if the current slip ratio is greater than the slip ratio peak, it is determined that the current slip ratio is in the unstable area.

[0069] Among them, it can be obtained through calculation and analysis of working condition tests at different vehicle speeds in the laboratory or on the actual road. For example, at different vehicle speeds, by changing the road surface adhesion coefficient of the vehicle, the curve of the slip ratio varying with the road surface adhesion coefficient can be obtained, and according to the change curve, the corresponding to each vehicle speed can be obtained, and a corresponding table can be constructed. Therefore, it can be obtained by looking up the table according to the vehicle speed and the road surface adhesion coefficient.

[0070] In addition, and can be respectively obtained by looking up from the same preset calibration coefficient table. For example, and can be looked up from Table 2-5 below.

[0071] Table 2 Preset Calibration Coefficient Table under the Condition of Dry Asphalt Road Surface (μ ≈ 0.9)

[0072]

[0073] Table 3 Preset Calibration Coefficient Table under the Condition of Wet Slippery Road Surface (μ ≈ 0.6)

[0074]

[0075] Table 4 Preset Calibration Coefficient Table under the Condition of Snowy Road Surface (μ ≈ 0.3)

[0076]

[0077] Table 5 Preset Calibration Coefficient Table under the Condition of Icy Road Surface (μ ≈ 0.15)

[0078]

[0079] In addition, it can be seen from the data in Table 2-5 above that the positive or negative value of the is determined by the magnitude relationship between the current slip ratio and the slip ratio peak, that is, it is related to the stable area or unstable area where the current slip ratio is located. Generally, When it is a positive value, it indicates that the current slip has a self-amplifying trend, and the larger the positive value, the stronger the self-amplifying effect; When it is negative, it indicates that the slip has a self-inhibiting characteristic, and the larger the negative value, the stronger the self-stabilizing effect.

[0080] Table 6 Value range

[0081]

[0082] Optionally, a second prediction method for the predicted slip ratio of the tire at the current moment is as follows: Based on a pre-trained slip ratio prediction model, according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment, each current tire pressure, each current tire temperature, the current slip ratio corresponding to the tire, and the historical slip ratios of at least one adjacent historical moment corresponding to the current moment, predict the predicted slip ratio of the tire at the current moment. Among them, the slip ratio prediction model is a pre-trained model for predicting the slip ratio of each tire. In the embodiments of the present application, the slip ratio prediction model is constructed by using a multi-layer perceptron or a time series network model. Specifically, the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment, each current tire pressure, each current tire temperature, the current slip ratio corresponding to the tire, and the historical slip ratios of at least one adjacent historical moment corresponding to the current moment can be input into the pre-trained slip ratio prediction model, so that the slip ratio prediction model analyzes and calculates the input data based on the trained model parameters, and uses the output of the slip ratio prediction model as the predicted slip ratio of the tire at the current moment.

[0083] Exemplarily, taking the slip ratio prediction model constructed based on a multi-layer perceptron as an example, the training process of the slip ratio prediction model is described. In the embodiments of the present application, the slip ratio prediction model includes an input layer, a hidden layer, and an output layer. Among them, the hidden layer includes four layers, and each layer includes 50 to 128 neurons. The activation function can adopt a rectified linear unit (ReLU) or a leaky rectified linear unit (LeakyReLU); in addition, Dropout(0.1~0.3) can be added to prevent overfitting. The loss function can calculate the mean square error of the prediction error of each tire and sum them up with weights.

[0084] It should be noted that if the output of the slip ratio prediction model , then set ; if , the flag of "reached the limit" can be switched again, where is the predicted slip ratio obtained by using the second method. In the case of individual rare inputs (such as extreme temperature / tire pressure), the predicted slip ratios of each tire obtained by the first method above can be used as auxiliary labels to enable the slip ratio prediction model to learn a wider range of scenarios.

[0085] During the training process, the training dataset can be recorded and obtained in a bench or real vehicle scenario; the loss function is shown in the following formula (3):

[0086] (3)

[0087] Wherein, is the regularization constraint function, When it is slightly larger, it can prevent the network from overfitting and ensure a certain smoothness. is the actual slip ratio.

[0088] After the training is completed, it is evaluated in the test set and the real vehicle verification scenario. If the error meets the engineering goal (such as an average error of ±0.02 - 0.03 slip ratio), it can be deployed online.

[0089] Optionally, the third method for predicting the predicted slip ratio corresponding to the tire at the current moment is: respectively calculate the two predicted slip ratios of each tire at the current moment by using the above-mentioned first method and the second method. That is to say, the number of predicted slip ratios of each tire can also be two, that is, calculated by the above-mentioned first method and the second method; the number of predicted slip ratios of each tire can be one, that is, calculated by the above-mentioned first method and / or the second method. It should be noted that if the predicted slip ratio of a tire is calculated by the above-mentioned first method or the second method, the weight of each method can be determined according to experience or experiment, and the predicted slip ratios of the tire calculated by the first method and the second method are weighted and summed to obtain a predicted slip ratio.

[0090] It can be understood that by introducing two different methods to calculate the predicted slip ratio of each tire, that is, based on the preset calibration parameter table and the pre-trained slip ratio prediction model, and by combining the predicted slip ratios calculated by the two different methods, the calculation method of the predicted slip ratio of the tire is enriched, and at the same time, the accuracy of the predicted slip ratio of the tire calculated is ensured.

[0091] Based on the technical solutions of the above embodiments, in order to accurately calculate the target anti-lock margin of the target vehicle at the current moment, it is stipulated that the tire operation data includes the current axle load information of the tire. On this basis, as Figure 4 shown, the present application also provides an optional embodiment. In this optional embodiment, the step of determining the target anti-lock margin in S230 is refined, and this step includes:

[0092] S410, respectively determine the slip weights of each tire according to the current axle load information of each tire.

[0093] Wherein, the slip weight of the tire is the weight of the slip ratio of the tire.

[0094] Optionally, in order to ensure the accuracy and pertinence of the determined slip weights, in the embodiments of the present application, two methods are provided to determine the slip weights of each tire.

[0095] Optionally, the first method for determining the slip weight of each tire is as follows: for each tire, the proportion of the load of the tire in the total load of all four tires can be used as the slip weight of the tire, that is, according to the proportion of the axle load distribution of the current axle load information of each tire, the slip weight of each tire is determined respectively. It should be noted that the load of each tire can be obtained from the current axle load information of the tire. Specifically, the slip weight of each tire can be calculated by the following formula (4):

[0096] (4)

[0097] where, is the slip weight of the th tire calculated by the first method; is the th tire load.

[0098] Optionally, the second method for determining the slip weight of each tire is as follows: for each tire, according to at least one of the current slip rate of the tire, the predicted slip rate of the tire, the current tire pressure of the tire, the current tire temperature of the tire, and the wheel speed change of the tire, the abnormal probability of the tire is determined, and according to the proportion of the axle load distribution of the current axle load information of the tire and the corresponding abnormal probability, the slip weight of the tire is determined. Wherein, the abnormal probability corresponding to each tire characterizes the probability of the tire having an abnormality. Specifically, this method takes into account the abnormal probability of each tire having an abnormality and assigns corresponding weights to different tires. For example, if the abnormal probability of a certain tire is higher than a preset threshold (such as 0.5), it means that the tire may be off the ground or the sensor fails. For this tire, its slip weight needs to be reduced.

[0099] In addition, an anomaly detection model can be introduced to calculate the anomaly probability of each tire. For example, a lightweight convolutional neural network (CNN) or a multi-layer perceptron (MLP) model can be introduced to construct an anomaly detection model. The anomaly detection model includes an input layer, a hidden layer, and an output layer. The data input into the input layer are the current slip rate of the tire, the predicted slip rate of the tire, the current tire pressure, the current tire temperature, and the wheel speed change of the tire; the hidden layer includes 23 layers, and each layer includes 50 - 100 units. BatchNorm (Batch Normalization) can be used for stable training, and the activation function can be ReLU / LeakyReLU. The output layer outputs the anomaly probability of each tire. In the embodiments of the present application, the anomaly probability ranges from [0, 1]. It should be noted that during the training process of the anomaly detection model, the labels in the training dataset can be generated through manual marking or comparison with a high-precision test bench.

[0100] After obtaining the anomaly probability of each tire by using the second method, the slip weight of each tire can be calculated by using the following formula (5):

[0101] (5)

[0102] Where, is the slip weight of the th tire calculated by using the second method; is the th tire's anomaly probability.

[0103] S420. Determine the current comprehensive slip rate of the target vehicle according to the slip weights of each tire and the corresponding current slip rates.

[0104] Where, the current comprehensive slip rate of the target vehicle is the comprehensive slip rate of the four tires of the target vehicle at the current moment.

[0105] Optionally, the slip weights of each tire and the corresponding current slip rates can be weighted and summed to obtain the current comprehensive slip rate of the target vehicle. Specifically, the current comprehensive slip rate can be calculated by using the following formula (6):

[0106] (6)

[0107] Where, is the current comprehensive slip rate of the target vehicle; can be the calculated by using the first method in the above embodiments, or can be the 。

[0108] It should be noted that the number of the current comprehensive slip ratios of the target vehicle can be one or two.

[0109] S430. Determine the comprehensive predicted slip ratio of the target vehicle according to the slip weights of the respective tires and the corresponding predicted slip ratios.

[0110] Correspondingly, the slip weights of the respective tires and the corresponding predicted slip ratios can be weighted and summed to obtain the comprehensive predicted slip ratio of the target vehicle. Specifically, the comprehensive predicted slip ratio can be calculated by the following formula (7):

[0111] (7)

[0112] Wherein, is the comprehensive predicted slip ratio of the target vehicle; can be the one calculated by using the first method in the above embodiments or can be the one calculated by using the second method in the above embodiments 。 can be the one calculated by using the first method in the above embodiments or can be the one calculated by using the second method in the above embodiments 。

[0113] It should be noted that generally, corresponds to , corresponds to 。That is to say, the number of the comprehensive predicted slip ratios of the target vehicle can be one or two.

[0114] S440. Determine the target locking margin of the target vehicle at the current moment according to the current comprehensive slip ratio of the target vehicle and the comprehensive predicted slip ratio of the target vehicle.

[0115] Optionally, since the current comprehensive slip ratio corresponding to the tire is calculated according to the actual situation, and the comprehensive predicted slip ratio corresponding to the tire is predicted by technical means, therefore, the current comprehensive slip ratio of the target vehicle can be adjusted in combination with the comprehensive predicted slip ratio of the target vehicle to obtain a relatively accurate comprehensive slip ratio.

[0116] Furthermore, the braking condition of the target vehicle during braking can be analyzed and evaluated by using slip ratio analysis software according to the obtained relatively accurate comprehensive slip ratio, and the target locking margin of the target vehicle at the current moment can be calculated according to the evaluation result.

[0117] In this embodiment, since there is a certain relationship between the slip ratio of the tire and the load borne by the tire, the slip weight coefficients for each tire are determined according to the current axle load information of each tire, ensuring that the slip weight coefficients corresponding to each tire can better represent the load conditions of each tire, and further ensuring the accuracy of the determined comprehensive predicted slip ratio and the current comprehensive slip ratio; furthermore, the accuracy of the determined target anti-lock margin is ensured.

[0118] Based on the technical solutions of the above embodiments, in order to accurately calculate the target anti-lock margin of the target vehicle, in one embodiment, as Figure 5 shown, an alternative embodiment is provided. In this alternative embodiment, the steps for determining the target anti-lock margin in S440 are refined, and the steps include:

[0119] S510, respectively determine the slip ratio threshold and the maximum acceleration according to the current road surface adhesion coefficient.

[0120] Among them, the slip ratio threshold is the maximum allowable slip ratio at the current moment; the maximum acceleration is the maximum allowable acceleration at the current moment.

[0121] Optionally, the road surface conditions can be analyzed according to the current road surface adhesion coefficient, and the maximum allowable slip ratio and the maximum acceleration under such conditions can be calculated systematically and used as the slip ratio threshold and the maximum acceleration respectively.

[0122] Optionally, in order to ensure the accuracy and pertinence of the determined slip ratio threshold and the maximum acceleration, in the embodiments of the present application, two methods are respectively provided to calculate the slip ratio threshold and the maximum acceleration.

[0123] For the slip ratio threshold, the first determination method provided in the embodiments of the present application is: determine the slip ratio threshold according to the coefficient range to which the current road surface adhesion coefficient belongs. Among them, the coefficient range is a pre-set range for dividing the road surface adhesion coefficient. For example, the road surface adhesion coefficient can be divided into [0, 0.3), [0.3, 0.7), and [0.7, 1], and each range corresponds to a different slip ratio value range. Exemplarily, when the coefficient range of the road surface adhesion coefficient is [0, 0.3), the corresponding slip ratio value range is [0.2, 0.25]; when the coefficient range of the road surface adhesion coefficient is [0.3, 0.7), the corresponding slip ratio value range is [0.15, 0.2); when the coefficient range of the road surface adhesion coefficient is [0.7, 1], the corresponding slip ratio value range is [0.1, 0.15).

[0124] Therefore, according to the coefficient range to which the current road surface adhesion coefficient belongs, the value range of the slip ratio can be determined, and then the average value of the maximum value and the minimum value of the slip ratio value range can be used as the slip ratio threshold.

[0125] For the slip ratio threshold, the second determination method provided by the embodiments of the present application is: determining the slip ratio threshold according to the current road surface adhesion coefficient, the current tire pressure of each tire, the current temperature of each tire, the current axle load information of each tire, and the tire wear condition of each tire. Among them, the tire wear condition characterizes the health condition of the tire. Optionally, a slip ratio threshold calculation model can be introduced to calculate the slip ratio threshold.

[0126] For example, the slip ratio calculation model can be constructed using a neural network model and includes an input layer, a hidden layer, and an output layer. Among them, the data input into the input layer is the current road surface adhesion coefficient, the current tire pressure of each tire, the current temperature of each tire, the current axle load information of each tire, and the tire wear condition of each tire. The hidden layer includes 12 layers, each layer includes 3264 neurons, and the activation function can be ReLU. The output layer outputs the maximum and minimum slip ratios. The Softplus activation can be used to ensure that it is greater than 0, and the maximum slip ratio output is made greater than the minimum slip ratio, either through an internal constraint (such as a sorting layer) or after output adjustment. The slip ratio calculation model can be supervised and trained using training data, and the training data can be collected on a test bench and in a real vehicle. In addition, the loss function can be:

[0127] (8)

[0128] Among them, is the actual minimum slip ratio; is the minimum slip ratio predicted by the slip ratio calculation model; is the actual maximum slip ratio; is the maximum slip ratio predicted by the slip ratio calculation model.

[0129] Furthermore, the mean value between the maximum and minimum slip ratios output by the slip ratio calculation model is obtained as the slip ratio threshold.

[0130] Combining the above two methods for determining the slip ratio threshold, if the gap between the maximum and minimum slip ratios obtained by the two methods is too large, a compromise needs to be made. It can be understood that the theoretical formula method and the neural network model method are respectively used to calculate the slip ratio threshold, which improves the flexibility of the calculation process; in addition, by introducing the two calculation methods, the advantages of complementary advantages and fault tolerance of the two methods can be used to improve the accuracy of the determined slip ratio threshold.

[0131] In addition, for the maximum acceleration, the first determination method provided by the embodiments of the present application is: determining the maximum acceleration according to the current road surface adhesion coefficient and the preset gravitational acceleration. Among them, the preset gravitational acceleration is the gravitational acceleration, generally taking 9.8. Optionally, the product of the current road surface adhesion coefficient and the preset gravitational acceleration can be used as the maximum acceleration. Specifically, it can be expressed by the following formula (9):

[0132] (9)

[0133] Wherein, is the maximum acceleration calculated by the first method; is the current road surface adhesion coefficient; is the preset gravitational acceleration.

[0134] Regarding the maximum acceleration, the second determination method provided by the embodiments of the present application is: determine the maximum acceleration according to the current road surface adhesion coefficient, the current vehicle speed, the current tire pressure of each tire, the current temperature of each tire, the tire wear condition of each tire, the current axle load information of each tire, and the current acceleration. Optionally, an acceleration calculation model can be introduced to calculate the maximum acceleration of the target vehicle. For example, the acceleration calculation model can be constructed using a neural network model, including an input layer, a hidden layer, and an output layer. Among them, the data input into the input layer is the current road surface adhesion coefficient, the current vehicle speed, the current tire pressure of each tire, the current temperature of each tire, the tire wear condition of each tire, the current axle load information of each tire, and the current acceleration; the hidden layer includes 4 fully connected layers, each layer uses a ReLU activation function, and the BatchNorm layer is used to stabilize the training, and Dropout(0.2) is used to prevent overfitting. The output layer outputs the maximum acceleration.

[0135] In addition, the acceleration calculation model can be trained using a training data set. The training data set can be constructed by collecting data through a professional test field with multiple road conditions (dry / wet / snow / ice), and the Adam optimizer is used and a segmented training strategy is implemented to enable the model to maintain good performance on low-adhesion road surfaces.

[0136] S520. Determine the remaining acceleration margin of the target vehicle according to the maximum acceleration and the current acceleration.

[0137] Wherein, the remaining acceleration margin of the target vehicle is the maximum acceleration that the target vehicle is allowed to continue accelerating at the current moment.

[0138] Optionally, the absolute value of the current acceleration can be taken, and the difference between the maximum acceleration and the absolute value of the current acceleration is used as the remaining acceleration margin of the target vehicle.

[0139] S530. Determine the target locking margin of the target vehicle at the current moment according to the slip rate threshold, the current comprehensive slip rate, the comprehensive predicted slip rate, the remaining acceleration margin, and the maximum acceleration.

[0140] Optionally, a lock-up margin calculation function can be pre-constructed based on theoretical knowledge and experience, and the slip rate threshold, current comprehensive slip rate, comprehensive predicted slip rate, remaining acceleration margin, and maximum acceleration are substituted into the lock-up margin calculation function to obtain the target lock-up margin of the target vehicle at the current moment.

[0141] In this embodiment, since there is a certain relationship between the slip rate threshold and the maximum acceleration and the road surface adhesion coefficient, therefore, by analyzing the current road surface adhesion coefficient, the slip rate threshold and the maximum acceleration can be accurately determined; furthermore, through the maximum acceleration, the remaining acceleration margin can be more accurately determined. In addition, by introducing the slip rate threshold, the accuracy of the determined target lock-up margin is ensured.

[0142] Based on the technical solutions of the above embodiments, in order to achieve the accurate calculation of the target lock-up margin of the target vehicle, in one embodiment, as Figure 6 shown, an optional embodiment is provided. In this optional embodiment, the calculation steps in S530 are refined, and the steps include:

[0143] S610, determine the current slip rate deviation according to the difference between the slip rate threshold and the current comprehensive slip rate.

[0144] Wherein, the current slip rate deviation is the value by which the current comprehensive slip rate of the target vehicle deviates from the slip rate threshold at the current moment.

[0145] Optionally, the difference between the slip rate threshold and the current comprehensive slip rate can be used as the current slip rate deviation. Specifically, it can be expressed by the following formula (10):

[0146] (10)

[0147] Wherein, is the current slip rate deviation; is the slip rate threshold; is the current comprehensive slip rate.

[0148] S620, determine the predicted slip rate deviation according to the difference between the slip rate threshold and the comprehensive predicted slip rate.

[0149] Wherein, the predicted slip rate deviation is the value by which the comprehensive predicted slip rate of the target vehicle deviates from the slip rate threshold at the current moment.

[0150] Optionally, the difference between the slip rate threshold and the comprehensive predicted slip rate can be used as the predicted slip rate deviation. Specifically, it can be expressed by the following formula (11):

[0151] (11)

[0152] Wherein, is the predicted slip rate deviation; is the comprehensive predicted slip rate.

[0153] S630. Determine the acceleration ratio according to the ratio between the remaining acceleration margin and the maximum acceleration.

[0154] Optionally, the ratio between the remaining acceleration margin and the maximum acceleration can be used as the acceleration ratio. Specifically, it can be expressed by the following formula (12):

[0155] (12)

[0156] Wherein, is the acceleration ratio; is the remaining acceleration margin; is the maximum acceleration. In the embodiments of the present application, can be the determined by the first method in the above embodiments, or can be the

[0157] S640. Determine the target anti-lock margin of the target vehicle at the current moment according to the current slip rate deviation, the predicted slip rate deviation, the slip rate threshold and the acceleration ratio.

[0158] In the embodiments of the present application, through a preset calculation function of the target anti-lock margin, the current slip rate deviation, the predicted slip rate deviation, the slip rate threshold and the acceleration ratio can be substituted into the calculation function to calculate the target anti-lock margin.

[0159] In addition, in order to ensure the accuracy of the determined target anti-lock margin, in the embodiments of the present application, three methods for determining the target anti-lock margin are provided. Optionally, the first method for determining the target anti-lock margin is: determine the target anti-lock margin of the target vehicle at the current moment according to the first proportion of the current slip rate deviation in the slip rate threshold, the second proportion of the predicted slip rate deviation in the slip rate threshold and the acceleration ratio. Optionally, the ratio between the current slip rate deviation and the slip rate threshold can be used as the first proportion, and the ratio between the predicted slip rate deviation and the slip rate threshold can be used as the second proportion; further, the first proportion, the second proportion and the acceleration ratio are weighted and summed to obtain the target anti-lock margin. It should be noted that the weights of the first proportion, the second proportion and the acceleration ratio can be determined according to tests or experience. Specifically, the target anti-lock margin can be calculated by the following formula (13):

[0160] (13)

[0161] Wherein, The target anti-lock margin of the target vehicle determined by the first method; , and are weights respectively. It should be noted that, The slip ratio threshold can be calculated by the first method in the above-mentioned embodiment S510.

[0162] Optionally, the second method for determining the target anti-lock margin is: based on the trained anti-lock margin prediction model, according to the current comprehensive slip ratio, comprehensive predicted slip ratio, current slip ratio deviation, predicted slip ratio deviation, slip ratio threshold, acceleration ratio, current road surface adhesion coefficient, current tire pressure of each tire, current tire temperature of each tire, and current vehicle speed, determine the target anti-lock margin of the target vehicle at the current moment. Among them, the trained anti-lock margin prediction model is used to predict the target anti-lock margin of the target vehicle. In the embodiments of the present application, the anti-lock margin prediction model can be constructed by using deep learning or a small MLP, including an input layer, a hidden layer, and an output layer. Among them, the data input into the input layer is the current comprehensive slip ratio, comprehensive predicted slip ratio, current slip ratio deviation, predicted slip ratio deviation, acceleration ratio, current road surface adhesion coefficient, current tire pressure of each tire, current tire temperature of each tire, and current vehicle speed. The hidden layer includes 23 layers, each layer includes 64 units, and the activation function can adopt ReLU. The output of the output layer is the target anti-lock margin of the target vehicle, and the value range is [0,1]. It should be noted that the current comprehensive slip ratio, comprehensive predicted slip ratio, current slip ratio deviation, and predicted slip ratio deviation in the input data are all values calculated by the second calculation method in the above-mentioned embodiment.

[0163] In addition, the anti-lock margin prediction model can be trained by using a training data set. Among them, the true anti-lock margin label in the training data set can be expertly labeled by the ABS trigger critical time difference and pedal force in real vehicle tests; it can also indirectly use "frequent ABS triggering is equivalent to too small anti-lock margin" for scenario comparison. The loss function can be expressed as:

[0164] (14)

[0165] Among them, is the true anti-lock margin; is the anti-lock margin predicted by the anti-lock margin prediction model. Among them, the true anti-lock margin is the true anti-lock margin label input during the process of training the anti-lock margin prediction model.

[0166] Optionally, the method for determining the third target locking margin is as follows: Determine the weight of the above-mentioned first method for determining the target locking margin and the weight of the above-mentioned second method for determining the target locking margin based on experience or experiments. Further, perform a weighted sum of the target locking margins determined by the above-mentioned first method and the second method to obtain the final target locking margin.

[0167] Based on the above three methods for determining the target locking margin, it can be seen that in the embodiments of the present application, the number of target locking margins can be one or two.

[0168] In this embodiment, by introducing the current slip ratio deviation, the predicted slip ratio deviation, and the acceleration ratio, the influencing factors of the locking margin are comprehensively considered. Therefore, by comprehensively analyzing the current slip ratio deviation, the predicted slip ratio deviation, and the acceleration ratio, the accuracy of the determined target locking margin can be ensured.

[0169] Based on the technical solutions of the above embodiments, if the number of target locking margins is at least two, in order to accurately implement the locking alarm reminder for the target vehicle, as Figure 7 shown, the present application also provides an optional embodiment. In this optional embodiment, the calculation steps in S240 are refined, and the steps include:

[0170] S710, determine the target locking margin weight according to at least one of the tire running data, the current health status of the wheel speed sensor in the target vehicle, and the current driving mode of the target vehicle.

[0171] Among them, the current health status of the wheel speed sensor includes but is not limited to all sensors being in good condition, some sensors being slightly degraded, most sensors being degraded, and most sensors being faulty; the tire running data can to a certain extent characterize the wear degree of the tire; the target locking margin weight is the weight of each target locking margin. The current driving mode of the target vehicle includes but is not limited to the normal mode, the comfort mode, the sport mode, the professional track mode, the snow / low adhesion mode, and the off-road mode. Among them, the normal mode focuses on energy conservation and is suitable for medium and low speeds or congested road conditions; the comfort mode focuses on balancing power and fuel consumption and is applicable to comprehensive road conditions such as urban commuting or long-distance highways; the sport mode prioritizes performance and is suitable for intense driving or complex road conditions; the professional track mode improves the handling stability and driving performance of the vehicle by adjusting the vehicle's suspension system, traction control, vehicle dynamics control, etc., and is suitable for complex road conditions such as races; the snow / low adhesion mode reduces the risk of the vehicle slipping on snow by restricting the engine output torque and adjusting the transmission reduction ratio, and is applicable to ice and snow roads or snow-covered sections; the off-road mode of the vehicle's four-wheel drive system brakes the slipping or idling wheels and then transfers the power to the other wheels with adhesion, and is applicable to rough roads or road conditions prone to slipping.

[0172] Optionally, at least one of the tire running data, the current health status of the wheel speed sensor in the target vehicle, and the current driving mode of the target vehicle can be selected to evaluate the braking condition of the target vehicle, and the target anti-lock margin weight can be calculated according to the evaluation result.

[0173] S720. Weight at least two target anti-lock margins according to the target anti-lock margin weight to obtain a comprehensive anti-lock margin.

[0174] Exemplarily, the process of weighting at least two target anti-lock margins according to the target anti-lock margin weight to obtain a comprehensive anti-lock margin can be represented by the following formula (15):

[0175] (15)

[0176] Where, is the comprehensive anti-lock margin; is the target anti-lock margin weight.

[0177] S730. Perform anti-lock alarm reminder according to the current driving mode and the comprehensive anti-lock margin of the target vehicle.

[0178] Optionally, the current driving mode of the target vehicle can be comprehensively considered to analyze and evaluate the anti-lock margin threshold of the target vehicle; further, the comprehensive anti-lock margin is compared with the anti-lock margin, and then the anti-lock alarm reminder is performed according to the comparison result.

[0179] Exemplarily, the anti-lock margin can be pre-classified. For example, the anti-lock margin is divided into [0, 0.1], (0.1, 0.3], and (0.3, 1], and each level corresponds to an anti-lock alarm reminder method. For example, the [0, 0.1] level indicates that the safety margin reaches the limit, and the anti-lock warning reminder method is red light flashing or voice warning or pedal vibration reminder; the (0.1, 0.3] level indicates that the safety margin approaches the limit, and the anti-lock warning reminder method is yellow light / orange light reminder; the (0.3, 1] level indicates that the safety margin is large, and the green light is on or no reminder is given.

[0180] Further, specific thresholds can be set for different driving modes. For example, for the normal mode, hysteresis control is added to reduce interference. If the target anti-lock margin is less than 0.2 and no anti-lock alarm reminder has been given previously, the reminder state is entered; if the target anti-lock margin is greater than 0.3 and an anti-lock alarm reminder has been given previously, the reminder state is cancelled. For the sport / high-performance mode, more accurate warnings are provided, and the target anti-lock margin value can be displayed, and the threshold is more sensitive. For the snow / low adhesion mode, a more conservative warning strategy is adopted.

[0181] In this embodiment, when the number of target anti-lock margins is at least two, by comprehensively analyzing at least one influencing factor of the anti-lock margin among tire running data, the current health state of the wheel speed sensor, and the current driving mode of the target vehicle, weights are determined for the target anti-lock margins, ensuring the accuracy and comprehensiveness of the determined weights of the target anti-lock margins. Furthermore, considering the current driving mode of the target vehicle ensures the accuracy and applicability of the anti-lock alarm reminder.

[0182] Based on the technical solutions of the above embodiments, if the number of target anti-lock margins is at least two, in order to further accurately determine the weights of the target anti-lock margins, as Figure 8 shown, the present application also provides an optional embodiment. In this optional embodiment, the step of determining the weights of the target anti-lock margins in S710 is refined, and this step includes:

[0183] S810, determine the expected anti-lock margin weights according to at least one of the tire running data, the current health state of the wheel speed sensor in the target vehicle, and the current driving mode of the target vehicle.

[0184] Among them, the expected anti-lock margin weights are the ideal anti-lock margin weights.

[0185] Optionally, for the current health state of the wheel speed sensor in the target vehicle, the first influence factors corresponding to different health states can be preset, and the healthier the health state, the greater the corresponding first influence factor. Exemplarily, if the current health state of the wheel speed sensor is that all sensors are in good condition, the corresponding first influence factor is 1; if the current health state of the wheel speed sensor is that some sensors are slightly degraded, the corresponding first influence factor is 0.7 - 0.8; if the current health state of the wheel speed sensor is that most sensors are degraded, the corresponding first influence factor is 0.3 - 0.5; if the current health state of the wheel speed sensor is that most sensors are faulty, the corresponding first influence factor is 0 - 0.2.

[0186] It should be noted that the health state of the wheel speed sensor can be determined according to the acquired signal of the wheel speed sensor. Exemplarily, if the signal is within a reasonable range and changes smoothly, it indicates that the wheel speed sensor is normal; if the signal fluctuates violently or exceeds the physical range, it indicates that the wheel speed sensor is faulty; if the signal is close to the boundary of the reasonable range or there are slight abnormalities, it indicates that the wheel speed sensor is partially degraded.

[0187] For the current driving mode of the target vehicle, different second influence factors corresponding to different driving modes can also be preset. Exemplarily, if the current driving mode is the normal mode, the corresponding second influence factor is 0.8; if the current driving mode is the comfort mode, the corresponding second influence factor is 0.9; if the current driving mode is the sport mode, the corresponding second influence factor is 0.3; if the current driving mode is the professional track mode, the corresponding second influence factor is 0.2; if the current driving mode is the snow / low adhesion mode, the corresponding second influence factor is 0.6; if the current driving mode is the off-road mode, the corresponding second influence factor is 0.7.

[0188] For the tire operation data, the tire state can be determined according to the tire operation data, and different third influence factors can be set for different tire states. Exemplarily, the state of each tire can be evaluated according to the current tire temperature, current tire pressure and tire wear degree in the tire operation data. If the tire is a new tire, that is, with normal temperature and pressure, the corresponding third influence factor is 1; if the tire is slightly worn and has normal temperature and pressure, the corresponding third influence factor is 0.9; if the tire is highly worn or has abnormal temperature and pressure, the corresponding third influence factor is 0.7; if the tire is in an extreme state, that is, severely worn and has abnormal temperature and pressure, the corresponding third influence factor is 0.5.

[0189] Furthermore, at least one influencing factor among the tire operation data, the current health state of the wheel speed sensor in the target vehicle, and the current driving mode of the target vehicle can be considered, and the product of the influence factors of each influencing factor is used as the expected anti-lock margin weight.

[0190] When all three influencing factors are considered, a method for determining the expected anti-lock margin weight is provided. Exemplarily, if the current health state is that the sensor is good, the current driving mode is normal driving, and all are new tires, the first influence factor is 0.9, the second influence factor is 0.8, the first influence factor is 1, and the expected anti-lock margin weight is 0.72.

[0191] If the current health status is that the sensors are in good condition, the current driving mode is the normal mode, and all are new tires, then the first influence factor is 0.9, the second influence factor is 0.8, the first influence factor is 1, and the expected locked - up margin weight is 0.72. If the current health status is that some sensors are degraded, the current driving mode is the normal mode, and all are new tires, then the first influence factor is 0.5, the second influence factor is 0.8, the first influence factor is 1, and the expected locked - up margin weight is 0.4. If the current health status is that the sensors are in good condition, the current driving mode is the sport mode, and all are new tires, then the first influence factor is 0.9, the second influence factor is 0.3, the first influence factor is 1, and the expected locked - up margin weight is 0.27. If the current health status is that most sensors are faulty, the current driving mode is the normal mode, and all are new tires, then the first influence factor is 0.2, the second influence factor is 0.8, the first influence factor is 1, and the expected locked - up margin weight is 0.16. If the current health status is that most sensors are degraded, the current driving mode is the sport mode, and the tires are worn, then the first influence factor is 0.6, the second influence factor is 0.3, the first influence factor is 0.7, and the expected locked - up margin weight is 0.126. If the current health status is that the sensors are in good condition, the current driving mode is the snow mode, and all are new tires, then the first influence factor is 0.9, the second influence factor is 0.6, the first influence factor is 1, and the expected locked - up margin weight is 0.54.

[0192] It should be noted that since the expected locked - up margin weight determines to a certain extent the proportion and output of the two target locked - up margins in the process of determining the comprehensive locked - up margin, therefore, by comprehensively considering the above three influencing factors, the weights of the two target locked - up margins can be more accurately allocated.

[0193] S820, determine the target locked - up margin weight according to the expected locked - up margin weight and the historical locked - up margin weight of the adjacent historical moment corresponding to the current moment.

[0194] Optionally, in order to prevent the system from being unstable due to the sudden change of the expected locked - up margin weight, a smoothing filter process can be added, that is, smooth the expected locked - up margin weight with the historical locked - up margin weight of the adjacent historical moment corresponding to the current moment, which can be specifically expressed as:

[0195] (16)

[0196] Among them, The function restricts the single - time change amplitude not to exceed ±0.1; is the expected locked - up margin weight.

[0197] In this embodiment, by comprehensively considering at least one influencing factor among the tire operation data, the current health state of the wheel speed sensor in the target vehicle, and the current driving mode of the target vehicle, the accuracy of the determined desired anti-lock margin weight is ensured. Furthermore, by comparing the historical anti-lock margin weights corresponding to the current moment and the adjacent historical moments, it is ensured that the determined target anti-lock margin weight will not mutate and cause system instability.

[0198] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of the steps or stages in other steps or other steps.

[0199] Based on the same inventive concept, an embodiment of the present application further provides an anti-lock degree reminder device for implementing the above-mentioned anti-lock degree reminder method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the anti-lock degree reminder device provided below can refer to the limitations on the anti-lock degree reminder method in the above text, and will not be repeated here.

[0200] In an exemplary embodiment, as Figure 9 shown, an anti-lock degree reminder device 900 is provided, including: a data acquisition module 910, a first determination module 920, a second determination module 930, and an anti-lock warning module 940, where:

[0201] The data acquisition module 910 is configured to acquire the vehicle operation data of the target vehicle and the tire operation data of each tire in the target vehicle at the current moment;

[0202] The first determination module 920 is configured to, for each tire in the target vehicle, respectively determine the current slip ratio and the corresponding predicted slip ratio of the tire at the current moment according to the vehicle operation data and the tire operation data corresponding to the tire;

[0203] The second determination module 930 is configured to determine the target anti-lock margin of the target vehicle at the current moment according to the current slip ratio and the corresponding predicted slip ratio of each tire;

[0204] The anti-lock warning module 940 is used to give an anti-lock warning reminder according to the current driving mode of the target vehicle and the target anti-lock margin.

[0205] In one embodiment, the vehicle operation data includes the current vehicle speed and the current road surface adhesion coefficient; the tire operation data includes the current pressure increment and the current wheel speed corresponding to the tire, where the current pressure increment is the growth rate of the slip ratio caused by the current brake pressure increment; the first determination module 920 includes:

[0206] The first determination unit is used to determine the current slip ratio corresponding to the tire at the current moment according to the current vehicle speed and the current wheel speed corresponding to the tire.

[0207] The second determination unit is used to determine the predicted slip ratio corresponding to the tire at the current moment according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment and the current slip ratio corresponding to the tire.

[0208] In one embodiment, the tire operation data further includes the current tire pressure and the current tire temperature; the second determination unit is specifically used for:

[0209] Based on a preset calibration parameter table, determine the predicted slip ratio corresponding to the tire at the current moment according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment and the current slip ratio corresponding to the tire; and / or, based on a pre-trained slip ratio prediction model, according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment, each current tire pressure, each current tire temperature, the current slip ratio corresponding to the tire and the historical slip ratios of at least one adjacent historical moment corresponding to the current moment, predict the predicted slip ratio of the tire at the current moment.

[0210] In one embodiment, the tire operation data includes the current axle load information of the tire; the second determination module 930 includes:

[0211] The weight determination unit is used to respectively determine the slip weight values of the tires according to the current axle load information of each tire.

[0212] The third determination unit is used to determine the current comprehensive slip ratio of the target vehicle according to the slip weight values of the tires and the corresponding current slip ratios.

[0213] The fourth determination unit is used to determine the comprehensive predicted slip ratio of the target vehicle according to the slip weight values of the tires and the corresponding predicted slip ratios.

[0214] The fourth determination unit is used to determine the target anti-lock margin of the target vehicle at the current moment according to the current comprehensive slip ratio of the target vehicle and the comprehensive predicted slip ratio of the target vehicle.

[0215] In one embodiment, the vehicle operation data includes the current acceleration of the target vehicle; the fourth determination unit includes:

[0216] A speed determination slave unit for respectively determining a slip ratio threshold and a maximum acceleration according to a current road surface adhesion coefficient.

[0217] A margin determination slave unit for determining a remaining acceleration margin of a target vehicle according to the maximum acceleration and the current acceleration.

[0218] A locking determination slave unit for determining a target locking margin of a target vehicle at a current moment according to the slip ratio threshold, a current comprehensive slip ratio, a comprehensive predicted slip ratio, the remaining acceleration margin, and the maximum acceleration.

[0219] In one embodiment, the speed determination slave unit is specifically configured to:

[0220] Determine the slip ratio threshold according to a coefficient range to which the current road surface adhesion coefficient belongs; or determine the slip ratio threshold according to the current road surface adhesion coefficient, the current tire pressure of each tire, the current temperature of each tire, the current axle load information of each tire, and the tire wear condition of each tire; determine the maximum acceleration according to the current road surface adhesion coefficient and a preset gravitational acceleration; or determine the maximum acceleration according to the current road surface adhesion coefficient, the current vehicle speed, the current tire pressure of each tire, the current temperature of each tire, the tire wear condition of each tire, the current axle load information of each tire, and the current acceleration.

[0221] In one embodiment, the locking determination slave unit includes:

[0222] A first determination subunit for determining a current slip ratio deviation according to a difference between the slip ratio threshold and the current comprehensive slip ratio.

[0223] A second determination subunit for determining a predicted slip ratio deviation according to a difference between the slip ratio threshold and the comprehensive predicted slip ratio.

[0224] A third determination subunit for determining an acceleration ratio according to a ratio between the remaining acceleration margin and the maximum acceleration.

[0225] A fourth determination subunit for determining a target locking margin of a target vehicle at a current moment according to the current slip ratio deviation, the predicted slip ratio deviation, the slip ratio threshold, and the acceleration ratio.

[0226] In one embodiment, the fourth determination subunit is specifically configured to:

[0227] Determine the target anti-lock margin of the target vehicle at the current moment according to the first proportion of the current slip rate deviation in the slip rate threshold, the second proportion of the predicted slip rate deviation in the slip rate threshold, and the acceleration ratio; and / or, based on the trained anti-lock margin prediction model, determine the target anti-lock margin of the target vehicle at the current moment according to the current comprehensive slip rate, the comprehensive predicted slip rate, the current slip rate deviation, the predicted slip rate deviation, the slip rate threshold, the acceleration ratio, the current road adhesion coefficient, the current tire pressure of each tire, the current tire temperature of each tire, and the current vehicle speed.

[0228] In one embodiment, if the number of target anti-lock margins is at least two, the anti-lock warning module 940 includes:

[0229] A fifth determination unit, configured to determine the target anti-lock margin weight according to at least one of the tire operation data, the current health status of the wheel speed sensor in the target vehicle, and the current driving mode of the target vehicle.

[0230] A sixth determination unit, configured to weight at least two target anti-lock margins according to the target anti-lock margin weight to obtain a comprehensive anti-lock margin.

[0231] An anti-lock warning unit, configured to perform anti-lock alarm reminder according to the current driving mode of the target vehicle and the comprehensive anti-lock margin.

[0232] In one embodiment, the fifth determination unit is specifically configured to:

[0233] Determine the expected anti-lock margin weight according to at least one of the tire operation data, the current health status of the wheel speed sensor in the target vehicle, and the current driving mode of the target vehicle; determine the target anti-lock margin weight according to the expected anti-lock margin weight and the historical anti-lock margin weight of the adjacent historical moment corresponding to the current moment.

[0234] Each module in the above anti-lock degree reminder device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or independent of it, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0235] In an alternative embodiment, the present application further provides a vehicle, in which the Figure 10 shown computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 10As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it realizes a method for reminding the degree of wheel lock-up.

[0236] Those skilled in the art can understand that Figure 10 the structure shown in

[0237] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it realizes the method for reminding the degree of wheel lock-up provided in the above embodiment.

[0238] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it realizes the method for reminding the degree of wheel lock-up provided in the above embodiment.

[0239] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it realizes the method for reminding the degree of wheel lock-up provided in the above embodiment.

[0240] It should be noted that the data involved in this application (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data that have been fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0241] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0242] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present application.

[0243] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for reminding of the degree of wheel locking, characterized in that, Including: Obtain the vehicle operation data of the target vehicle at the current moment and the tire operation data of each tire in the target vehicle; For each tire in the target vehicle, respectively determine the current slip ratio and the corresponding predicted slip ratio of the tire at the current moment according to the vehicle operation data and the tire operation data corresponding to the tire; Determine the target locking margin of the target vehicle at the current moment according to the current slip ratio and the corresponding predicted slip ratio of each tire; Perform a locking alarm reminder according to the current driving mode of the target vehicle and the target locking margin.

2. The method according to claim 1, wherein The vehicle operation data includes the current vehicle speed and the current road surface adhesion coefficient; the tire operation data includes the current pressure increment and the current wheel speed corresponding to the tire; Correspondingly, respectively determining the current slip ratio and the corresponding predicted slip ratio of the tire at the current moment according to the vehicle operation data and the tire operation data corresponding to the tire includes: Determine the current slip ratio of the tire corresponding to the current moment according to the current vehicle speed and the current wheel speed corresponding to the tire; Determine the predicted slip ratio of the tire corresponding to the current moment according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment and the current slip ratio corresponding to the tire.

3. The method according to claim 2, wherein The tire operation data further includes the current tire pressure and the current tire temperature; determining the predicted slip ratio of the tire corresponding to the current moment according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment and the current slip ratio corresponding to the tire includes: Based on a preset calibration parameter table, determine the predicted slip ratio of the tire corresponding to the current moment according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment and the current slip ratio corresponding to the tire; and / or, Based on a pre-trained slip ratio prediction model, predict the predicted slip ratio of the tire at the current moment according to the current vehicle speed, the current road surface adhesion coefficient, the current pressure increment, each current tire pressure, each current tire temperature, the current slip ratio corresponding to the tire, and the historical slip ratio of at least one adjacent historical moment corresponding to the current moment.

4. The method according to claim 2, characterized in that The tire operation data includes the current axle load information of the tire; determining the target locking margin of the target vehicle at the current moment according to the current slip ratio and the corresponding predicted slip ratio of each tire includes: Respectively determine the slip weight of each tire according to the current axle load information of each tire; Determine the current comprehensive slip ratio of the target vehicle according to the slip weight of each tire and the corresponding current slip ratio; Determine the comprehensive predicted slip ratio of the target vehicle according to the slip weight of each tire and the corresponding predicted slip ratio; Determine the target locking margin of the target vehicle at the current moment according to the current comprehensive slip ratio of the target vehicle and the comprehensive predicted slip ratio of the target vehicle.

5. The method according to claim 4, wherein The vehicle operation data includes the current acceleration of the target vehicle; determining the target anti-lock margin of the target vehicle at the current moment according to the current comprehensive slip ratio and the comprehensive predicted slip ratio of the target vehicle includes: Determining a slip ratio threshold and a maximum acceleration respectively according to the current road surface adhesion coefficient; Determining the remaining acceleration margin of the target vehicle according to the maximum acceleration and the current acceleration; Determining the target anti-lock margin of the target vehicle at the current moment according to the slip ratio threshold, the current comprehensive slip ratio, the comprehensive predicted slip ratio, the remaining acceleration margin and the maximum acceleration.

6. The method according to claim 5, characterized in that, Determining a slip ratio threshold and a maximum acceleration respectively according to the current road surface adhesion coefficient includes: Determining the slip ratio threshold according to the coefficient range to which the current road surface adhesion coefficient belongs; or Determining the slip ratio threshold according to the current road surface adhesion coefficient, the current tire pressure of each tire, the current temperature of each tire, the current axle load information of each tire and the tire wear condition of each tire; Determining the maximum acceleration according to the current road surface adhesion coefficient and the preset gravitational acceleration; or Determining the maximum acceleration according to the current road surface adhesion coefficient, the current vehicle speed, the current tire pressure of each tire, the current temperature of each tire, the tire wear condition of each tire, the current axle load information of each tire and the current acceleration.

7. The method according to claim 5, characterized in that Determining the target anti-lock margin of the target vehicle at the current moment according to the slip ratio threshold, the current comprehensive slip ratio, the comprehensive predicted slip ratio, the remaining acceleration margin and the maximum acceleration includes: Determining the current slip ratio deviation according to the difference between the slip ratio threshold and the current comprehensive slip ratio; Determining the predicted slip ratio deviation according to the difference between the slip ratio threshold and the comprehensive predicted slip ratio; Determining the acceleration ratio according to the ratio of the remaining acceleration margin to the maximum acceleration; Determining the target anti-lock margin of the target vehicle at the current moment according to the current slip ratio deviation, the predicted slip ratio deviation, the slip ratio threshold and the acceleration ratio.

8. The method according to claim 7, wherein Determining the target anti-lock margin of the target vehicle at the current moment according to the current slip ratio deviation, the predicted slip ratio deviation, the slip ratio threshold and the acceleration ratio includes: Determining the target anti-lock margin of the target vehicle at the current moment according to the first proportion of the current slip ratio deviation in the slip ratio threshold, the second proportion of the predicted slip ratio deviation in the slip ratio threshold and the acceleration ratio; and / or Based on the trained anti-lock margin prediction model, determining the target anti-lock margin of the target vehicle at the current moment according to the current comprehensive slip ratio, the comprehensive predicted slip ratio, the current slip ratio deviation, the predicted slip ratio deviation, the slip ratio threshold, the acceleration ratio, the current road surface adhesion coefficient, the current tire pressure of each tire, the current tire temperature of each tire and the current vehicle speed.

9. The method according to any one of claims 1-8, characterized in that, If the number of the target anti-lock margins is at least two, anti-lock alarm reminders are made according to the current driving mode of the target vehicle and the target anti-lock margins, including: Determining a target anti-lock margin weight according to at least one of the tire operation data, the current health state of the wheel speed sensors in the target vehicle, and the current driving mode of the target vehicle; Weighting the at least two target anti-lock margins according to the target anti-lock margin weight to obtain a comprehensive anti-lock margin; Making anti-lock alarm reminders according to the current driving mode of the target vehicle and the comprehensive anti-lock margin.

10. The method according to claim 9, characterized in that, Determining a target anti-lock margin weight according to at least one of the tire operation data, the current health state of the wheel speed sensors in the target vehicle, and the current driving mode of the target vehicle, including: Determining an expected anti-lock margin weight according to at least one of the tire operation data, the current health state of the wheel speed sensors in the target vehicle, and the current driving mode of the target vehicle; Determining the target anti-lock margin weight according to the expected anti-lock margin weight and the historical anti-lock margin weight at the historical moment adjacent to the current moment.

11. An anti-lock degree reminder device, characterized in that, The device includes: A data acquisition module, configured to acquire the vehicle operation data of the target vehicle at the current moment and the tire operation data of each tire in the target vehicle; A first determination module, configured to, for each tire in the target vehicle, respectively determine the current slip ratio and the corresponding predicted slip ratio of the tire at the current moment according to the vehicle operation data and the tire operation data corresponding to the tire; A second determination module, configured to determine the target anti-lock margin of the target vehicle at the current moment according to the current slip ratio and the corresponding predicted slip ratio of each tire; An anti-lock warning module, configured to make anti-lock alarm reminders according to the current driving mode of the target vehicle and the target anti-lock margin.

12. A vehicle, characterized in that, An apparatus for implementing the method according to any one of claims 1-10 is provided in the vehicle.

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