Braking pressure control method and device

Abstract

A method and apparatus for controlling braking pressure, belonging to the field of vehicle control, are disclosed. The method includes: acquiring driving information of a target vehicle; determining an initial control strategy for controlling the braking pressure of the target vehicle based on the driving information; predicting the braking pressure of the target vehicle after implementing the initial control strategy using a pressure prediction model; determining a target control strategy based on the braking pressure predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current road surface; and controlling the braking pressure of the target vehicle according to the target control strategy. This application can improve the accuracy of controlling the braking pressure of a vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle control, and in particular to a method and apparatus for controlling braking pressure. Background Technology

[0002] The braking pressure of a vehicle refers to the pressure in the reservoir of the vehicle's hydraulic control unit (HCU). In order to ensure the normal operation of the vehicle, it is necessary to accurately control the vehicle's braking pressure.

[0003] Currently, braking pressure is typically controlled based on the vehicle's slip ratio or wheel speed. However, controlling braking pressure based on slip ratio or wheel speed has relatively low accuracy. Summary of the Invention

[0004] This application provides a method and apparatus for controlling braking pressure, which can improve the accuracy of controlling the braking pressure of a vehicle. The technical solution of this application is as follows.

[0005] In a first aspect, a method for controlling braking pressure is provided, the method comprising:

[0006] Obtain the driving information of the target vehicle;

[0007] An initial control strategy for controlling the braking pressure of the target vehicle is determined based on the driving information.

[0008] The braking pressure of the target vehicle after the initial control strategy is executed is predicted using a pressure prediction model.

[0009] The target control strategy is determined based on the braking pressure of the target vehicle after the execution of the initial control strategy, as predicted by the pressure prediction model, and the ideal braking pressure of the target vehicle on the current driving road surface.

[0010] The braking pressure of the target vehicle is controlled according to the target control strategy.

[0011] Optionally, the step of using a pressure prediction model to predict the braking pressure of the target vehicle after executing the initial control strategy includes:

[0012] Based on the test dataset of the target vehicle, it is determined whether the pressure prediction model can accurately predict the braking pressure of the target vehicle. The test dataset is a dataset obtained by conducting braking pressure control tests on the test vehicle. The test dataset includes multiple test data points, each of which includes a pressure control strategy and braking pressure. The braking pressure is the braking pressure of the test vehicle after the pressure control strategy is executed on the test vehicle. The test vehicle is the target vehicle, or the braking pressure control system of the test vehicle is the same as the braking pressure control system of the target vehicle.

[0013] If the pressure prediction model can accurately predict the braking pressure of the target vehicle, the pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed.

[0014] Optionally, the step of using a pressure prediction model to predict the braking pressure of the target vehicle after executing the initial control strategy further includes:

[0015] If the pressure prediction model fails to accurately predict the braking pressure of the target vehicle, the pressure prediction model is updated using the test dataset.

[0016] The updated pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed.

[0017] Optionally, determining the target control strategy based on the braking pressure of the target vehicle after executing the initial control strategy predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving surface includes:

[0018] If the braking pressure of the target vehicle is less than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a boosting strategy.

[0019] If the braking pressure of the target vehicle is equal to the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure-maintaining strategy.

[0020] If the braking pressure of the target vehicle is greater than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure reduction strategy.

[0021] Optionally, controlling the braking pressure of the target vehicle according to the target control strategy includes:

[0022] Generate pressure control commands based on the target control strategy;

[0023] The pressure control command is sent to the brake pressure control system of the target vehicle so that the brake pressure control system controls the brake pressure of the target vehicle according to the pressure control command.

[0024] Secondly, a braking pressure control device is provided, the device comprising:

[0025] The acquisition module is used to acquire the driving information of the target vehicle;

[0026] The first determining module is used to determine an initial control strategy for controlling the braking pressure of the target vehicle based on the driving information.

[0027] The prediction module is used to predict the braking pressure of the target vehicle after the execution of the initial control strategy using a pressure prediction model.

[0028] The second determining module is used to determine the target control strategy based on the braking pressure of the target vehicle after the execution of the initial control strategy predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving road surface.

[0029] The control module is used to control the braking pressure of the target vehicle according to the target control strategy.

[0030] Optionally, the prediction module is used for:

[0031] Based on the test dataset of the target vehicle, it is determined whether the pressure prediction model can accurately predict the braking pressure of the target vehicle. The test dataset is a dataset obtained by conducting braking pressure control tests on the test vehicle. The test dataset includes multiple test data points, each of which includes a pressure control strategy and braking pressure. The braking pressure is the braking pressure of the test vehicle after the pressure control strategy is executed on the test vehicle. The test vehicle is the target vehicle, or the braking pressure control system of the test vehicle is the same as the braking pressure control system of the target vehicle.

[0032] If the pressure prediction model can accurately predict the braking pressure of the target vehicle, the pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed.

[0033] Optionally, the prediction module is further configured to:

[0034] If the pressure prediction model fails to accurately predict the braking pressure of the target vehicle, the pressure prediction model is updated using the test dataset.

[0035] The updated pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed.

[0036] Optionally, the second determining module is used for:

[0037] If the braking pressure of the target vehicle is less than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a boosting strategy.

[0038] If the braking pressure of the target vehicle is equal to the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure-maintaining strategy.

[0039] If the braking pressure of the target vehicle is greater than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure reduction strategy.

[0040] Optionally, the control module is used for:

[0041] Generate pressure control commands based on the target control strategy;

[0042] The pressure control command is sent to the brake pressure control system of the target vehicle so that the brake pressure control system controls the brake pressure of the target vehicle according to the pressure control command.

[0043] Thirdly, a braking pressure control device is provided, including a memory and a processor;

[0044] The memory is used to store computer programs;

[0045] The processor is configured to execute a computer program stored in the memory to cause the control device to perform the method provided by the first aspect or any alternative implementation thereof.

[0046] Fourthly, a vehicle is provided, including the control device provided as in the second aspect or any alternative implementation of the second aspect, or including the control device provided as in the third aspect.

[0047] Fifthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed, the computer program implements the method provided as described in the first aspect or any alternative method of the first aspect.

[0048] In a sixth aspect, a computer program product is provided, the computer program product comprising a program or code that, when executed, implements the method provided as described in the first aspect or any alternative method of the first aspect.

[0049] The beneficial effects of the technical solution provided in this application are:

[0050] This application provides a method and apparatus for controlling braking pressure. The control method is executed by a control device deployed in a target vehicle. After acquiring the driving information of the target vehicle, the control device determines an initial control strategy for controlling the braking pressure of the target vehicle based on the driving information. The control device uses a pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy. The control device determines a target control strategy based on the braking pressure of the target vehicle predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving surface, and controls the braking pressure of the target vehicle according to the target control strategy. This application improves the accuracy of controlling the braking pressure of the target vehicle by using a pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy, determining the target control strategy based on the braking pressure predicted by the pressure prediction model and the ideal braking pressure of the target vehicle (e.g., adjusting the initial control strategy based on the braking pressure predicted by the pressure prediction model to obtain the target control strategy), and then controlling the braking pressure of the target vehicle according to the target control strategy. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of a braking pressure control method provided in an embodiment of this application;

[0053] Figure 2 This is a schematic diagram illustrating a method for predicting the braking pressure of a target vehicle, as provided in an embodiment of this application.

[0054] Figure 3 This is a schematic diagram of a braking pressure control device provided in an embodiment of this application;

[0055] Figure 4 This is a schematic diagram of another braking pressure control device provided in the embodiments of this application.

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] Please refer to Figure 1 The diagram illustrates a flowchart of a braking pressure control method provided in an embodiment of this application. The method is executed by a braking pressure control device. The method includes the following steps S101 to S105.

[0059] S101. Obtain the driving information of the target vehicle.

[0060] The target vehicle includes a sensing system through which the control device can acquire the vehicle's driving information. This driving information includes at least one of slip ratio (also known as slip rate) and wheel speed. The wheel speed includes at least one of wheel linear velocity (i.e., the linear speed of the wheel) and wheel angular velocity (i.e., the angular velocity of the wheel). In an optional embodiment, the sensing system includes at least one of a linear velocity sensor and an angular velocity sensor. The linear velocity sensor is used to acquire the wheel linear velocity of the target vehicle, and the angular velocity sensor is used to acquire the wheel angular velocity of the target vehicle. The vehicle's braking process typically includes a pure rolling phase, a rolling-slipping phase, and a pure sliding phase. The slip ratio refers to the proportion of the sum of the duration of the rolling-slipping phase and the duration of the pure sliding phase to the total duration of the braking process.

[0061] In one embodiment, the driving information of the target vehicle includes wheel linear velocity, the sensing system of the target vehicle includes a linear velocity sensor, the linear velocity sensor is used to collect the wheel linear velocity of the target vehicle, and the control device determines the wheel linear velocity collected by the linear velocity sensor as the wheel linear velocity of the target vehicle.

[0062] In another embodiment, the driving information of the target vehicle includes wheel angular velocity, and the sensing system of the target vehicle includes an angular velocity sensor for collecting the wheel angular velocity of the target vehicle. The control device determines the wheel angular velocity collected by the angular velocity sensor as the wheel angular velocity of the target vehicle.

[0063] In another embodiment, the driving information of the target vehicle includes the slip ratio. The control device acquires the driving speed and wheel speed of the target vehicle, and determines the slip ratio of the target vehicle based on the driving speed and wheel speed. In one example, the control device determines the slip ratio of the target vehicle using a first slip ratio formula based on the driving speed and wheel speed of the target vehicle. The first slip ratio formula is: S = [(UL) / U] × 100%, where S represents the vehicle's slip ratio, U represents the vehicle's driving speed, L represents the vehicle's wheel speed, " / " represents division, and "-" represents subtraction. For example, the control device substitutes the driving speed and wheel speed of the target vehicle into the first slip ratio formula to calculate the slip ratio of the target vehicle. In another example, the control device determines the slip ratio of the target vehicle using a second slip ratio formula based on the driving speed and angular velocity of the target vehicle. The second slip ratio formula is: S = [(UR × W) / U] × 100%, where S represents the vehicle's slip ratio, U represents the vehicle's speed, R represents the vehicle's wheel radius, W represents the vehicle's wheel angular velocity, " / " represents division, and "-" represents subtraction. For example, the control device substitutes the target vehicle's speed, wheel linear velocity, and wheel radius into the above second slip ratio formula to calculate the target vehicle's slip ratio.

[0064] In an optional embodiment, the sensing system of the target vehicle includes a vehicle speed sensor, which is used to collect the driving speed of the target vehicle, and the control device determines the driving speed collected by the vehicle speed sensor as the driving speed of the target vehicle.

[0065] S102. Determine an initial control strategy for controlling the braking pressure of the target vehicle based on the target vehicle's driving information.

[0066] As described in S101, the driving information of the target vehicle includes at least one of slip ratio, wheel linear velocity, and wheel angular velocity. Therefore, in S102, the control device determines an initial control strategy for controlling the braking pressure of the target vehicle based on at least one of the slip ratio, wheel linear velocity, and wheel angular velocity of the target vehicle.

[0067] In one embodiment, the target vehicle's driving information includes wheel linear velocity, and the control device determines an initial control strategy for controlling the braking pressure of the target vehicle based on the wheel linear velocity. In a specific embodiment, the control device determines the initial control strategy for controlling the braking pressure of the target vehicle based on the relationship between the wheel linear velocity of the target vehicle and a preset linear velocity. If the wheel linear velocity of the target vehicle is greater than the preset linear velocity, the control device determines the initial control strategy as a pressure reduction strategy; if the wheel linear velocity of the target vehicle is equal to the preset linear velocity, the control device determines the initial control strategy as a pressure holding strategy; and if the wheel linear velocity of the target vehicle is less than the preset linear velocity, the control device determines the initial control strategy as a pressure reduction strategy.

[0068] In another embodiment, the target vehicle's driving information includes wheel angular velocities, and the control device determines an initial control strategy for controlling the braking pressure of the target vehicle based on these wheel angular velocities. Specifically, in this embodiment, the control device determines the initial control strategy for controlling the braking pressure of the target vehicle based on the relationship between the target vehicle's wheel angular velocities and a preset angular velocity. If the target vehicle's wheel angular velocity is greater than the preset angular velocity, the control device determines the initial control strategy as a pressure-reducing strategy; if the target vehicle's wheel angular velocity is equal to the preset angular velocity, the control device determines the initial control strategy as a pressure-maintaining strategy; and if the wheel angular velocity is less than the preset angular velocity, the control device determines the initial control strategy as a pressure-reducing strategy.

[0069] In another embodiment, the target vehicle's driving information includes a slip ratio, and the control device determines an initial control strategy for controlling the braking pressure of the target vehicle based on the target vehicle's slip ratio. Specifically, in another embodiment, the control device determines the initial control strategy for controlling the braking pressure of the target vehicle based on the relationship between the target vehicle's slip ratio and a preset slip ratio. If the target vehicle's slip ratio is greater than the preset slip ratio, the control device determines the initial control strategy as a pressure reduction strategy; if the target vehicle's slip ratio is equal to the preset slip ratio, the control device determines the initial control strategy as a pressure holding strategy; and if the target vehicle's slip ratio is less than the preset slip ratio, the control device determines the initial control strategy as a pressure reduction strategy.

[0070] S103. Use a pressure prediction model to predict the braking pressure of the target vehicle after the initial control strategy is implemented.

[0071] In an optional embodiment, the control device acquires a test dataset of the target vehicle. Based on this test dataset, the control device determines whether the pressure prediction model can accurately predict the braking pressure of the target vehicle. If the pressure prediction model can accurately predict the braking pressure of the target vehicle, the control device uses the pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy. If the pressure prediction model cannot accurately predict the braking pressure of the target vehicle, the control device uses the test dataset to update the pressure prediction model, and then uses the updated pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy. The test dataset is a dataset obtained by conducting braking pressure control tests on a test vehicle. The test dataset includes multiple test data points, each of which includes a pressure control strategy and braking pressure. The braking pressure in each test data point is the braking pressure of the test vehicle after implementing the pressure control strategy specified in that test data point. The test vehicle can be the target vehicle, or the braking pressure control system of the test vehicle can be the same as that of the target vehicle.

[0072] In an optional embodiment, the control device determines the number of qualified test data in the test dataset based on the pressure prediction model, and determines whether the pressure prediction model can accurately predict the braking pressure of the target vehicle based on the number of qualified test data in the test dataset. In a specific embodiment, the control device determines whether the number of qualified test data in the test dataset is greater than a preset number; if the number of qualified test data in the test dataset is greater than the preset number, the control device determines that the pressure prediction model can accurately predict the braking pressure of the target vehicle; if the number of qualified test data in the test dataset is not greater than the preset number, the control device determines that the pressure prediction model cannot accurately predict the braking pressure of the target vehicle. In a specific embodiment, for each piece of test data in the test dataset: the control device uses the pressure prediction model to predict the braking pressure of the test vehicle after implementing the pressure control strategy (for example, the braking pressure predicted by the pressure prediction model is called the predicted braking pressure), and the control device determines whether the test data is qualified test data based on the predicted braking pressure and the braking pressure in the test data (for example, the braking pressure in the test data is called the measured braking pressure). For example, the control device determines the difference between the predicted braking pressure and the measured braking pressure. If the difference is not greater than a preset difference, the control device determines the test data to be compliant test data; if the difference is greater than the preset difference, the control device determines the test data to be non-compliant test data. As an example, for each test data point in the test dataset: the control device inputs the pressure control strategy from the test data into the pressure prediction model, causing the pressure prediction model to predict the braking pressure of the test vehicle after implementing the pressure control strategy.

[0073] In a specific embodiment, if the pressure prediction model can accurately predict the braking pressure of the target vehicle, the control device inputs the initial control strategy for controlling the braking pressure of the target vehicle determined in S102 into the pressure prediction model. The pressure prediction model then predicts the braking pressure of the target vehicle after implementing the initial control strategy based on the initial control strategy, and outputs the braking pressure of the target vehicle after implementing the initial control strategy. The control device obtains the braking pressure of the target vehicle after implementing the initial control strategy output by the pressure prediction model. If the pressure prediction model cannot accurately predict the braking pressure of the target vehicle, the control device updates the pressure prediction model using the test dataset. The control device inputs the initial control strategy for controlling the braking pressure of the target vehicle determined in S102 into the updated pressure prediction model. The updated pressure prediction model predicts the braking pressure of the target vehicle after implementing the initial control strategy based on the initial control strategy, and outputs the braking pressure of the target vehicle after implementing the initial control strategy. The control device obtains the braking pressure of the target vehicle after implementing the initial control strategy output by the updated pressure prediction model.

[0074] The following describes the implementation process of the control device updating the pressure prediction model using a test dataset. In a specific embodiment, for each test data point in the test dataset: the control device inputs the pressure control strategy from the test data into the pressure prediction model, causing the pressure prediction model to predict the braking pressure after implementing the pressure control strategy; the control device adjusts the model parameters of the pressure prediction model based on the difference between the predicted braking pressure and the braking pressure in the test data (e.g., the braking pressure in the test data is referred to as the measured braking pressure); the control device inputs the pressure control strategy from the test data into the adjusted model, causing the adjusted model to predict the braking pressure after implementing the pressure control strategy; the control device adjusts the model parameters again based on the difference between the predicted braking pressure predicted by the adjusted model and the measured braking pressure, and then predicts again. The control device repeatedly executes the process of inputting the pressure control strategy into the pressure prediction model and adjusting the model parameters to update the model until the first preset condition is met. The process of inputting the pressure control strategy into the pressure prediction model and adjusting the model parameters constitutes one iteration. For example, the first preset condition includes at least one of the following: for each test data in the test dataset, the difference between the predicted braking pressure obtained by the pressure prediction model based on the pressure control strategy in the test data and the measured braking pressure in the test data is less than a preset difference; the number of iterations in the update process (i.e., the number of iterations in the update process) reaches a preset number; for each test data in the test dataset, the degree of change in the predicted braking pressure obtained by the pressure prediction model based on the pressure control strategy in the test data through multiple predictions is small, for example, the difference in the predicted braking pressure obtained from multiple consecutive predictions is less than a preset difference.

[0075] In this embodiment, the pressure prediction model is trained using a training dataset. This training dataset is obtained by controlling the braking pressure of at least one vehicle. The training dataset includes multiple training data points, each of which includes a pressure control strategy and braking pressure. The braking pressure in each training data point is the braking pressure of the vehicle after executing the pressure control strategy in that training data. This pressure prediction model can be trained by the control device described in this embodiment, or it can be trained by other devices and then ported to the control device described in this embodiment. In an optional embodiment, the pressure prediction model is trained by the control device using the training dataset. For each training data point in the training dataset: the control device inputs the pressure control strategy from the training data into the initial model, causing the initial model to predict the braking pressure after implementing the pressure control strategy; the control device adjusts the model parameters of the initial model based on the difference between the predicted braking pressure and the braking pressure in the training data (e.g., the braking pressure in the training data is referred to as the measured braking pressure); the control device inputs the pressure control strategy from the training data into the adjusted model, causing the adjusted model to predict the braking pressure after implementing the pressure control strategy; the control device adjusts the model parameters again based on the difference between the predicted braking pressure predicted by the adjusted model and the measured braking pressure, and then predicts again. The control device repeatedly executes the process of inputting the pressure control strategy into the initial model and adjusting the model parameters to train the model until a second preset condition is met. The control device determines the model obtained when the second preset condition is met as the finally trained pressure prediction model. The process from inputting the pressure control strategy into the initial model to adjusting the model parameters constitutes one iteration. For example, the second preset condition includes at least one of the following: for each training data point in the training dataset, the difference between the predicted braking pressure obtained by the pressure prediction model based on the pressure control strategy in the training data and the measured braking pressure in the training data is less than a preset difference; the number of iterations in the training process (i.e., the number of iterations in the training process) reaches a preset number; for each training data point in the training dataset, the degree of change in the predicted braking pressure obtained by the model through multiple predictions based on the pressure control strategy in the training data is small, for example, the difference in the predicted braking pressure obtained by the pressure control strategy through multiple consecutive predictions is less than a preset difference.

[0076] In an optional embodiment, the control device trains the model based on the training dataset using a generative adversarial algorithm to obtain the stress prediction model. The control device then updates the model based on the test dataset using the same generative adversarial algorithm to obtain an updated stress prediction model. In another optional embodiment, the initial model is a generator in a generative adversarial network (GAN), the stress prediction model is obtained by training this generator, the GAN is a neural network based on the GAN algorithm, and it also includes a discriminator. The control device trains the generator based on the training dataset to obtain the stress prediction model.

[0077] In a specific embodiment, for each piece of training data in the training dataset: the control device inputs the pressure control strategy from the test data into the generator, causing the generator to predict the braking pressure after implementing the pressure control strategy. The control device inputs the braking pressure from the test data (e.g., the braking pressure from the test data is referred to as the measured braking pressure) and the predicted braking pressure into the discriminator, causing the discriminator to discriminate the predicted braking pressure based on the measured braking pressure and output a discrimination result indicating whether the predicted braking pressure matches the measured braking pressure. If the discrimination result indicates that the predicted braking pressure does not match the measured braking pressure, it means that the generator's prediction result is inaccurate, and the control device adjusts the generator's parameters based on the difference between the measured braking pressure and the predicted braking pressure. The control device inputs the pressure control strategy from the training data into the generator with adjusted parameters, causing the generator with adjusted parameters to predict the braking pressure after implementing the pressure control strategy. The control device inputs the measured braking pressure from the test data and the predicted braking pressure predicted by the generator after parameter adjustment into the discriminator. The discriminator then judges the predicted braking pressure based on the measured braking pressure and outputs a judgment result indicating whether the predicted braking pressure matches the measured braking pressure. The control device repeatedly executes the pressure control strategy of inputting the test data into the generator and adjusting the generator's parameters until the discriminator outputs a judgment result indicating that the predicted braking pressure matches the measured braking pressure. If the judgment result indicates that the predicted braking pressure matches the measured braking pressure, it means that the generator's prediction result is relatively accurate. The control device defines the generator obtained when the discriminator outputs a judgment result indicating that the predicted braking pressure matches the measured braking pressure as a pressure prediction model. Matching the predicted braking pressure with the measured braking pressure includes at least one of the following: the predicted braking pressure is equal to the measured braking pressure, or the difference between the predicted braking pressure and the measured braking pressure is within a preset range. The discriminator outputs a result of 1 or 0. 1 indicates that the predicted braking pressure matches the measured braking pressure, while 0 indicates that the predicted braking pressure does not match the measured braking pressure.

[0078] The process by which the control device updates the pressure prediction model using a generative adversarial algorithm can be found in the process of training the pressure prediction model using a generative adversarial algorithm, and will not be elaborated here.

[0079] Figure 2 This is a schematic diagram illustrating a method for predicting the braking pressure of a target vehicle, as provided in an embodiment of this application. Figure 2As shown, after acquiring the training dataset, the control device trains a model based on the training dataset to obtain a pressure prediction model. The control device also acquires a test dataset of the target vehicle and determines whether the pressure prediction model can accurately predict the braking pressure of the target vehicle based on the test dataset and the pressure prediction model. If the pressure prediction model can accurately predict the braking pressure of the target vehicle, the control device uses the pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy. If the pressure prediction model cannot accurately predict the braking pressure of the target vehicle, the control device updates the pressure prediction model using the test dataset and uses the updated pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy.

[0080] S104. Determine the target control strategy based on the braking pressure of the target vehicle after the execution of the initial control strategy predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving road surface.

[0081] In an optional embodiment, the control device determines the road surface information of the target vehicle's current driving surface. Based on this road surface information, the control device determines the ideal braking pressure of the target vehicle on that current driving surface. Furthermore, based on the braking pressure of the target vehicle after executing the initial control strategy predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving surface, the control device determines a target control strategy for controlling the braking pressure of the target vehicle. The road surface information includes the road surface type and may also include information on road surface attachments. Road surface types include asphalt pavement, cement pavement, concrete pavement, gravel pavement, sand and gravel pavement, granite pavement, etc. Information on road surface attachments includes the type of attachments on the road surface, such as water, ice, snow, etc.

[0082] In one embodiment, the control device determines the ideal braking pressure of the target vehicle on the current driving surface based on the road surface information of the target vehicle and a first mapping relationship. The first mapping relationship includes a mapping between the road surface information of the target vehicle's current driving surface and the ideal braking pressure of the target vehicle on that current driving surface. In a specific embodiment, the first mapping relationship includes a one-to-one correspondence between multiple sets of road surface information and multiple braking pressures, where the braking pressure corresponding to each set of road surface information is the ideal braking pressure for the vehicle on the road surface represented by that set of road surface information. The control device searches the first mapping relationship based on the road surface information of the target vehicle's current driving surface to determine the braking pressure corresponding to the road surface information of the target vehicle's current driving surface within the first mapping relationship, and the control device determines the braking pressure corresponding to the road surface information of the target vehicle's current driving surface as the ideal braking pressure of the target vehicle on that current driving surface.

[0083] In one example, the first mapping relationship is shown in Table 1 below.

[0084]

[0085] For example, the road information of the target vehicle's current driving road is road information X2. The control device uses the road information X2 of the target vehicle's current driving road to find the first mapping relationship shown in Table 1 and determines the braking pressure corresponding to the road information X2 as braking pressure Y2. Therefore, the control device determines the braking pressure Y2 as the ideal braking pressure of the target vehicle on the current driving road.

[0086] In an optional embodiment, the road surface information includes road surface type and road surface attachment information, and the first mapping relationship is a one-to-one correspondence between road surface type, road surface attachment information and braking pressure.

[0087] In one example, the first mapping relationship is shown in Table 2 below.

[0088]

[0089] For example, the road surface type of the target vehicle's current driving surface is road surface type S2, and the road surface attachment information of the target vehicle's current driving surface is road surface attachment information K2. The control device uses the road surface type S2 and the road surface attachment information K2 of the target vehicle's current driving surface to find the first mapping relationship shown in Table 2 and determine the braking pressure corresponding to road surface type S2 and road surface attachment information K2 as braking pressure Y2. Therefore, the control device determines the braking pressure Y2 as the ideal braking pressure of the target vehicle on the current driving surface.

[0090] In an optional embodiment, the control device determines a target control strategy for controlling the braking pressure of the target vehicle based on the relationship between the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, and the ideal braking pressure of the target vehicle on the current driving surface. Specifically, if the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is less than the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as a pressure-boosting strategy. If the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is equal to the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as a pressure-maintaining strategy. If the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is greater than the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as a pressure-reducing strategy. The target control strategy includes either the ideal braking pressure of the target vehicle on the current driving surface or the ideal opening degree of the target vehicle's braking pressure valve (i.e., the valve in the target vehicle's braking pressure control system).

[0091] In one embodiment, the target control strategy includes the ideal braking pressure of the target vehicle on the current driving surface. The control device determines the target control strategy based on the braking pressure of the target vehicle after executing the initial control strategy, predicted by the pressure prediction model, and the ideal braking pressure of the target vehicle on the current driving surface. Specifically, in an embodiment, if the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is less than the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as: increasing the braking pressure of the target vehicle to the ideal braking pressure. If the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is equal to the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as: maintaining the braking pressure of the target vehicle at the ideal braking pressure. If the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is greater than the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as: reducing the braking pressure of the target vehicle to the ideal braking pressure.

[0092] In another embodiment, the target control strategy includes the ideal opening degree of the brake pressure valve of the target vehicle. The control device determines the ideal opening degree of the brake pressure valve of the target vehicle based on the ideal braking pressure of the target vehicle on the current driving surface. The control device determines the target control strategy based on the brake pressure of the target vehicle after executing the initial control strategy predicted by the pressure prediction model, the ideal braking pressure of the target vehicle on the current driving surface, and the ideal opening degree. In a specific embodiment, if the brake pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is less than the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as: increasing the opening degree of the brake pressure valve of the target vehicle to the ideal opening degree. If the brake pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is equal to the ideal braking pressure of the target vehicle on the current driving surface, the control device determines the target control strategy as: maintaining the opening degree of the brake pressure valve of the target vehicle at the ideal opening degree. If the braking pressure of the target vehicle is greater than the ideal braking pressure of the target vehicle on the current road surface after the execution of the initial control strategy as predicted by the pressure prediction model, the control device determines the target control strategy as follows: the opening of the brake pressure valve of the target vehicle is reduced to the ideal opening.

[0093] In an optional embodiment, the control device determines the ideal opening degree of the brake pressure valve of the target vehicle based on the ideal braking pressure of the target vehicle on the current driving surface and a second mapping relationship. The second mapping relationship includes a mapping relationship between the ideal braking pressure of the target vehicle on the current driving surface and the ideal opening degree of the brake pressure valve of the target vehicle. In a specific embodiment, the second mapping relationship includes a one-to-one correspondence between multiple braking pressures and multiple valve opening degrees. The control device searches the second mapping relationship based on the ideal braking pressure of the target vehicle on the current driving surface to determine the valve opening degree corresponding to the ideal braking pressure in the second mapping relationship. The control device determines the valve opening degree corresponding to the ideal braking pressure as the ideal opening degree of the brake pressure valve of the target vehicle.

[0094] In one example, the second mapping relationship is shown in Table 3 below.

[0095]

[0096] For example, the ideal braking pressure of the target vehicle on the current driving road surface is braking pressure Y2. The control device uses the braking pressure Y2 of the target vehicle on the current driving road surface to find the second mapping relationship shown in Table 3 to determine the valve opening D2 corresponding to the braking pressure Y2. Therefore, the control device determines the valve opening D2 as the ideal opening of the braking pressure valve of the target vehicle.

[0097] S105. Control the braking pressure of the target vehicle according to the target control strategy.

[0098] In an optional embodiment, the control device generates a pressure control command according to the target control strategy. The control device sends the pressure control command to the brake pressure control system of the target vehicle, causing the brake pressure control system to control the brake pressure of the target vehicle according to the pressure control command. The brake pressure control system includes a brake pressure valve, a hydraulic pump, and a reservoir. The hydraulic pump and the reservoir are connected by a pipeline. The brake pressure valve is located on the pipeline. The reservoir stores liquid. The hydraulic pump pumps liquid into or out of the reservoir to control the pressure within the reservoir (i.e., the brake pressure of the target vehicle). The brake pressure valve controls the flow rate of liquid in the pipeline, thereby controlling the pressure within the reservoir. After receiving the pressure control command from the control device, the brake pressure control system of the target vehicle controls the opening of the brake pressure valve according to the pressure control command, so that the brake pressure of the target vehicle is maintained at the ideal brake pressure for the target vehicle on the current road surface. For example, the pressure control command includes operation instruction information and the adjustment opening of the pressure control valve of the target vehicle. The operation instruction information is used to indicate the specific operation of the brake pressure valve, such as increasing the valve opening, decreasing the valve opening, or maintaining the valve opening. The brake pressure control system adjusts the adjustment opening of the brake pressure valve according to the operation instruction information.

[0099] In one embodiment, the target control strategy includes an ideal opening degree. The control device generates operation instruction information based on the target control strategy. The control device determines the predicted opening degree of the brake pressure valve of the target vehicle based on the predicted braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model. The control device determines the adjusted opening degree of the brake pressure valve based on the ideal opening degree and the predicted opening degree. The control device generates a pressure control command based on the operation instruction information and the adjusted opening degree of the brake pressure valve. The adjusted opening degree is the difference between the ideal opening degree and the predicted opening degree. In a specific embodiment, the control device determines the predicted opening degree of the brake pressure valve based on the predicted braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, and the second mapping relationship.

[0100] In another embodiment, the target control strategy includes an ideal braking pressure. The control device generates operation instruction information based on the target control strategy. The control device determines the ideal opening of the brake pressure valve of the target vehicle based on the ideal braking pressure. The control device determines the predicted opening of the brake pressure valve of the target vehicle based on the predicted braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model. The control device determines the adjusted opening of the brake pressure valve based on the ideal opening and the predicted opening. The control device generates a pressure control command based on the operation instruction information and the adjusted opening of the brake pressure valve. The adjusted opening is the difference between the ideal opening and the predicted opening. In a specific embodiment, the control device determines the ideal opening of the brake pressure valve based on the ideal braking pressure and the second mapping relationship described above. The control device determines the predicted opening of the brake pressure valve based on the predicted braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, and the second mapping relationship described above.

[0101] In one embodiment, the brake pressure valve in the brake pressure control system of the target vehicle is a smart valve with data reading and processing capabilities. The control device sends a pressure control command to the brake pressure valve, which receives the command and controls its opening degree according to the command. In a specific embodiment, the pressure control command includes operation instruction information and an adjustment opening degree for the brake pressure valve. The brake pressure valve determines the specific operation to adjust its opening degree based on the operation instruction information. After adjusting the valve's opening degree, the valve's opening degree is the ideal opening degree.

[0102] In another embodiment, the brake pressure valve in the target vehicle's brake pressure control system is a non-intelligent valve, lacking data reading and processing capabilities. The brake pressure control system also includes a controller for controlling the opening of the brake pressure valve. The control device sends a pressure control command to the controller, which receives the command and controls the opening of the brake pressure valve accordingly. In a specific embodiment, the pressure control command carries operation instruction information and an adjustment opening of the brake pressure valve. The controller determines the specific operation for adjusting the brake pressure valve based on the operation instruction information in the pressure control command, and adjusts the opening of the brake pressure valve to the adjusted opening. After adjusting the opening of the brake pressure valve, the opening of the brake pressure valve reaches the ideal opening.

[0103] This application embodiment illustrates an example where a control device determines the ideal opening degree of a brake pressure valve for a target vehicle based on the target vehicle's ideal braking pressure, and the pressure control command includes this ideal opening degree. In some embodiments, the pressure control command includes the ideal braking pressure but not the ideal opening degree. The target vehicle's brake pressure control system determines the ideal opening degree based on the ideal braking pressure, and then adjusts the opening degree of the brake pressure valve to the ideal opening degree. For example, if the brake pressure valve is a smart valve, the brake pressure valve can determine the ideal opening degree based on the ideal braking pressure, and then adjust the opening degree of the brake pressure valve to the ideal opening degree. If the brake pressure control system includes a controller, the controller can determine the ideal opening degree based on the ideal braking pressure, and then adjust the opening degree of the brake pressure valve to the ideal opening degree. The process by which the brake pressure valve or the controller determines the ideal opening degree based on the ideal braking pressure can be referred to the above-described process by which the control device determines the ideal opening degree based on the ideal braking pressure, and will not be elaborated upon here.

[0104] In summary, the vehicle braking pressure control method provided in this application involves a control device acquiring the driving information of a target vehicle, determining an initial control strategy for controlling the braking pressure of the target vehicle based on the driving information, using a pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy, determining a target control strategy based on the braking pressure predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving surface, and controlling the braking pressure of the target vehicle according to the target control strategy. This application improves the accuracy of controlling the braking pressure of the target vehicle by using a pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy, determining the target control strategy based on the braking pressure predicted by the pressure prediction model and the ideal braking pressure of the target vehicle (e.g., adjusting the initial control strategy based on the braking pressure predicted by the pressure prediction model to obtain the target control strategy), and then controlling the braking pressure of the target vehicle according to the target control strategy.

[0105] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0106] Please refer to Figure 3 The diagram illustrates a braking pressure control device 300 provided in an embodiment of this application. The control device 300 is used to perform... Figure 1 The control method provided in the illustrated embodiment. The control device 300 is deployed in the aforementioned target vehicle. For example... Figure 3As shown, the control device 300 includes, but is not limited to, an acquisition module 301, a first determination module 302, a prediction module 303, a second determination module 304, and a control module 305.

[0107] The acquisition module 301 is used to acquire the driving information of the target vehicle;

[0108] The first determining module 302 is used to determine an initial control strategy for controlling the braking pressure of the target vehicle based on the driving information.

[0109] Prediction module 303 is used to predict the braking pressure of the target vehicle after the execution of the initial control strategy using a pressure prediction model;

[0110] The second determining module 304 is used to determine the target control strategy based on the braking pressure of the target vehicle after the execution of the initial control strategy predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving road surface.

[0111] The control module 305 is used to control the braking pressure of the target vehicle according to the target control strategy.

[0112] Optionally, the prediction module 303 is configured to: determine whether the pressure prediction model can accurately predict the braking pressure of the target vehicle based on the test dataset of the target vehicle, wherein the test dataset is a dataset obtained by performing braking pressure control tests on the test vehicle, the test dataset includes multiple test data, each test data includes a pressure control strategy and braking pressure, the braking pressure being the braking pressure of the test vehicle after the pressure control strategy is executed on the test vehicle, the test vehicle being the target vehicle, or the braking pressure control system of the test vehicle being the same as the braking pressure control system of the target vehicle; if the pressure prediction model can accurately predict the braking pressure of the target vehicle, the pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed.

[0113] Optionally, the prediction module 303 is further configured to: update the pressure prediction model using the test dataset when the pressure prediction model cannot accurately predict the braking pressure of the target vehicle; and use the updated pressure prediction model to predict the braking pressure of the target vehicle after the initial control strategy is executed.

[0114] Optionally, the second determining module 304 is configured to: determine the target control strategy as a boosting strategy when the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is less than the ideal braking pressure; determine the target control strategy as a pressure maintaining strategy when the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is equal to the ideal braking pressure; and determine the target control strategy as a depressurization strategy when the braking pressure of the target vehicle after executing the initial control strategy, as predicted by the pressure prediction model, is greater than the ideal braking pressure.

[0115] Optionally, the control module 305 is configured to: generate a pressure control command according to the target control strategy; and send the pressure control command to the brake pressure control system of the target vehicle so that the brake pressure control system controls the brake pressure of the target vehicle according to the pressure control command.

[0116] In summary, the technical solution provided in this application involves a control device acquiring the driving information of a target vehicle, determining an initial control strategy for controlling the braking pressure of the target vehicle based on this information, using a pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy, determining a target control strategy based on the braking pressure predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current road surface, and controlling the braking pressure of the target vehicle according to the target control strategy. This application improves the accuracy of controlling the braking pressure of the target vehicle by using a pressure prediction model to predict the braking pressure of the target vehicle after implementing the initial control strategy, determining the target control strategy based on the braking pressure predicted by the pressure prediction model and the ideal braking pressure of the target vehicle (e.g., adjusting the initial control strategy based on the braking pressure predicted by the pressure prediction model to obtain the target control strategy), and then controlling the braking pressure of the target vehicle according to the target control strategy.

[0117] This application provides a braking pressure control device, including a memory and a processor. The memory stores a computer program. The processor executes the computer program stored in the memory to cause the control device to perform actions such as... Figure 1 The method provided in the illustrated embodiment. Optionally, the control device includes an Electronic Stability Control (ESC) system.

[0118] This application provides a vehicle that includes the braking pressure control device described in the above embodiments.

[0119] As an example, please refer to Figure 4The diagram illustrates a vehicle 400 according to an embodiment of this application. The vehicle 400 may be the target vehicle described in the foregoing embodiments, and the braking pressure control device described in the foregoing embodiments is deployed in the vehicle 400.

[0120] Typically, vehicle 400 includes a processor 401 and a memory 402.

[0121] Processor 401 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 401 may be implemented using at least one hardware form selected from digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). Processor 401 may include, but is not limited to, a central processing unit (CPU). In some embodiments, processor 401 may integrate a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. Processor 401 may also include an artificial intelligence (AI) processor to handle computational operations related to machine learning.

[0122] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 402 are used to store at least one instruction, which is executed by the processor 401 to implement the braking pressure control method provided in the embodiments of this application.

[0123] In some embodiments, the vehicle 400 may also optionally include a peripheral device interface 403 and at least one peripheral device. The processor 401, memory 402, and peripheral device interface 403 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 403 via a bus, signal line, or circuit board. The peripheral device may include at least one of the following: a radio frequency circuit 404, a touch display screen 405, a camera 406, an audio circuit 407, a positioning component 408, and a power supply 409.

[0124] Peripheral interface 403 can be used to connect at least one input / output (I / O) related peripheral device to processor 401 and memory 402. In some embodiments, processor 401, memory 402 and peripheral interface 403 are integrated on the same chip or circuit board; in some embodiments, any one or two of processor 401, memory 402 and peripheral interface 403 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0125] The radio frequency (RF) circuit 404 is used to receive and transmit radio frequency (RF) signals, also known as electromagnetic signals. The RF circuit 404 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 404 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 404 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, etc. The RF circuit 404 can communicate with other devices through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), and wireless local area networks; this application embodiment does not limit this to any particular protocol.

[0126] Display screen 405 is used to display a user interface (UI). The UI may include graphics, text, icons, video, and any combination thereof. When display screen 405 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 401 for processing. In this case, display screen 405 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, display screen 405 may be a flexible display screen. Furthermore, display screen 405 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 405 may be a liquid crystal display (LCD), an organic light-emitting diode (OLED) display screen, etc.

[0127] The camera assembly 406 is used to capture images or videos.

[0128] The audio circuit 407 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting them into electrical signals that are input to the processor 401 for processing, or to the radio frequency circuit 404 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, positioned in different parts of the vehicle. The microphone can also be an array microphone or an omnidirectional microphone. The speaker is used to convert the electrical signals from the processor 401 or the radio frequency circuit 404 into sound waves. The speaker can be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement.

[0129] Positioning component 408 is used to locate the geographic location of vehicle 400 in order to enable navigation or location-based service (LBS). Positioning component 408 can be a positioning component based on global positioning system (GPS), BeiDou system or Galileo system.

[0130] Power source 409 is used to supply power to various components in the vehicle. Power source 409 can be alternating current, direct current, a disposable battery, or a rechargeable battery. When power source 409 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil.

[0131] In some embodiments, the vehicle 400 further includes one or more sensors 410. The one or more sensors 410 include, but are not limited to: an optical sensor 411, a proximity sensor 412, a clutch sensor 413, a vehicle speed sensor 414, a wheel linear velocity sensor 415, and a wheel angular velocity sensor 416.

[0132] Optical sensor 411 is used to collect ambient light intensity. In one embodiment, processor 401 can control the display brightness of touch screen 405 based on the ambient light intensity collected by optical sensor 411. Specifically, when the ambient light intensity is high, the display brightness of touch screen 405 is increased; when the ambient light intensity is low, the display brightness of touch screen 405 is decreased. In another embodiment, processor 401 can also dynamically adjust the shooting parameters of camera assembly 406 based on the ambient light intensity collected by optical sensor 411.

[0133] The proximity sensor 412, also known as a distance sensor, is typically located on the front panel of the display screen 406 of the personality characteristic determination device 400. The proximity sensor 412 is used to detect the distance between the user and the display screen 405. In one embodiment, when the proximity sensor 412 detects that the distance between the user and the display screen 406 is gradually decreasing, the processor 401 controls the touch display screen 405 to switch from a screen-on state to a screen-off state; when the proximity sensor 412 detects that the distance between the user and the display screen 405 is gradually increasing, the processor 401 controls the touch display screen 405 to switch from a screen-off state to a screen-on state.

[0134] Clutch sensor 413 is used to collect the clutch pedal opening of the vehicle, vehicle speed sensor 414 is used to collect the vehicle's driving speed, wheel linear velocity sensor 415 is used to collect the vehicle's wheel linear velocity, and wheel angular velocity sensor 416 is used to collect the vehicle's wheel angular velocity. Processor 401 determines an initial control strategy for controlling the vehicle's braking pressure based on the vehicle's driving speed, wheel linear velocity, or wheel angular velocity. Processor 401 uses a pressure prediction model to predict the vehicle's braking pressure after executing the initial control strategy. Processor 401 determines a target control strategy based on the vehicle's braking pressure after executing the initial control strategy predicted by the pressure prediction model and the vehicle's ideal braking pressure on the current driving surface, and controls the vehicle's braking pressure according to the target control strategy.

[0135] Those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on vehicle 400. Vehicle 400 may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0136] Optionally, the processor 401 and memory 402 in the vehicle 400 constitute a control device.

[0137] This application provides a computer-readable storage medium storing a computer program that, when executed (e.g., by a braking pressure control device, one or more processors, etc.), implements all or part of the steps of the method provided in the above embodiments.

[0138] This application provides a computer program product that includes a program or code that, when executed (e.g., by a braking pressure control device, one or more processors, etc.), implements all or part of the steps of the method provided in the above embodiments.

[0139] It should be understood that the term "at least one" in this application refers to one or more, and "multiple" refers to two or more. The term "and / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Furthermore, for clarity, the terms "first," "second," and "third" are used in this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," and "third" do not limit the quantity or order of execution.

[0140] The method embodiments and device embodiments provided in this application can be referenced interchangeably, and this application does not limit them. The order of operations in the method embodiments provided in this application can be appropriately adjusted, and operations can be added or removed as needed. Any variations that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application, and therefore will not be elaborated further.

[0141] In the corresponding embodiments provided in this application, it should be understood that the disclosed devices, etc., can be implemented through other configurations. For example, the device embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Modules described as separate components may or may not be physically separate, and components described as modules may or may not be physical modules. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0142] It should be noted that the information (including but not limited to vehicle information, user information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the driving speed, wheel angular velocity, and wheel linear velocity of the target vehicle involved in the embodiments of this application were obtained with full authorization.

[0143] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any equivalent modifications or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling braking pressure, characterized in that, The method includes: Obtain the driving information of the target vehicle; An initial control strategy for controlling the braking pressure of the target vehicle is determined based on the driving information. Based on the test dataset of the target vehicle, it is determined whether the pressure prediction model can accurately predict the braking pressure of the target vehicle. The test dataset is a dataset obtained by conducting braking pressure control tests on the test vehicle. The test dataset includes multiple test data, each test data including a pressure control strategy and braking pressure. The braking pressure is the braking pressure of the test vehicle after the pressure control strategy is executed on the test vehicle. The test vehicle is the target vehicle, or the braking pressure control system of the test vehicle is the same as the braking pressure control system of the target vehicle. If the pressure prediction model can accurately predict the braking pressure of the target vehicle, the pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed. If the pressure prediction model fails to accurately predict the braking pressure of the target vehicle, the pressure prediction model is updated using the test dataset. The updated pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed; The target control strategy is determined based on the braking pressure of the target vehicle after the execution of the initial control strategy, as predicted by the pressure prediction model, and the ideal braking pressure of the target vehicle on the current driving road surface. The braking pressure of the target vehicle is controlled according to the target control strategy.

2. The method according to claim 1, characterized in that, The step of determining the target control strategy based on the braking pressure of the target vehicle after executing the initial control strategy, predicted by the pressure prediction model, and the ideal braking pressure of the target vehicle on the current driving road surface includes: If the braking pressure of the target vehicle is less than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a boosting strategy. If the braking pressure of the target vehicle is equal to the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure-maintaining strategy. If the braking pressure of the target vehicle is greater than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure reduction strategy.

3. The method according to claim 1 or 2, characterized in that, The step of controlling the braking pressure of the target vehicle according to the target control strategy includes: Generate pressure control commands based on the target control strategy; The pressure control command is sent to the brake pressure control system of the target vehicle so that the brake pressure control system controls the brake pressure of the target vehicle according to the pressure control command.

4. A braking pressure control device, characterized in that, The device includes: The acquisition module is used to acquire the driving information of the target vehicle; The first determining module is used to determine an initial control strategy for controlling the braking pressure of the target vehicle based on the driving information. The prediction module is used to determine whether the pressure prediction model can accurately predict the braking pressure of the target vehicle based on the test dataset of the target vehicle. The test dataset is a dataset obtained by performing braking pressure control tests on the test vehicle. The test dataset includes multiple test data, each test data including a pressure control strategy and braking pressure. The braking pressure is the braking pressure of the test vehicle after the pressure control strategy is executed on the test vehicle. The test vehicle is the target vehicle, or the braking pressure control system of the test vehicle is the same as the braking pressure control system of the target vehicle. If the pressure prediction model can accurately predict the braking pressure of the target vehicle, the pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed. If the pressure prediction model fails to accurately predict the braking pressure of the target vehicle, the pressure prediction model is updated using the test dataset. The updated pressure prediction model is used to predict the braking pressure of the target vehicle after the initial control strategy is executed; The second determining module is used to determine the target control strategy based on the braking pressure of the target vehicle after the execution of the initial control strategy predicted by the pressure prediction model and the ideal braking pressure of the target vehicle on the current driving road surface. The control module is used to control the braking pressure of the target vehicle according to the target control strategy.

5. The apparatus according to claim 4, characterized in that, The second determining module is used for: If the braking pressure of the target vehicle is less than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a boosting strategy. If the braking pressure of the target vehicle is equal to the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure-maintaining strategy. If the braking pressure of the target vehicle is greater than the ideal braking pressure after the execution of the initial control strategy, as predicted by the pressure prediction model, the target control strategy is determined to be a pressure reduction strategy.

6. The apparatus according to claim 4 or 5, characterized in that, The control module is used for: Generate pressure control commands based on the target control strategy; The pressure control command is sent to the brake pressure control system of the target vehicle so that the brake pressure control system controls the brake pressure of the target vehicle according to the pressure control command.

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