Vehicle slip prediction and control method and system

Abstract

The invention discloses a vehicle slip prediction and control method and system, and belongs to the technical field of vehicle control, and the method comprises the steps: obtaining wheel speed time sequence data, vehicle body posture time sequence data and steering wheel angle time sequence data of a vehicle; according to the wheel speed time sequence data, the vehicle body posture time sequence data and the steering wheel angle time sequence data, the vehicle slip rate at the next moment is predicted, and a predicted value of the vehicle slip rate at the next moment is obtained; according to the predicted value of the vehicle slip rate at the next moment, determining the slip risk level of the vehicle at the next moment; when the slippage risk grade of the vehicle at the next moment is the middle grade, the vehicle is pre-filled with brake fluid with the set pressure; and when the slippage risk grade of the vehicle at the next moment is a high grade, full-hydraulic braking and anti-lock braking are conducted on the vehicle. Accurate control over the vehicle is achieved according to the slip rate.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of vehicle control, and particularly to a vehicle slip prediction and control method and system. Background Art

[0002] The slip ratio is the proportion of the sliding component in the wheel movement. To ensure the stable and safe operation of the vehicle, it is necessary to obtain the slip ratio of the vehicle, and then perform braking intervention on the vehicle according to the slip ratio of the vehicle.

[0003] When the current method performs vehicle braking according to the slip ratio, it determines the vehicle slip ratio by obtaining the vehicle speed and wheel speed, and then predicts the slip ratio at the next moment through the continuous vehicle slip ratio. After predicting the vehicle slip ratio, it compares the vehicle slip ratio with a preset threshold. When the vehicle slip ratio exceeds the preset threshold, braking intervention is performed on the vehicle.

[0004] When the current method predicts the slip ratio, it only considers the vehicle speed and wheel speed, without considering key parameters such as the vehicle body attitude. When a complex road surface such as ice, snow or wetness suddenly appears in front of the vehicle, an accurate prediction of the slip ratio cannot be achieved; and when the current method determines whether the vehicle needs braking intervention, the threshold used is a preset fixed value, which cannot be adapted to different road surfaces; on this basis, the current method cannot accurately judge whether the vehicle needs to perform braking intervention, resulting in insufficient safety and reliability of the vehicle. Summary of the Invention

[0005] The embodiments of the present application provide a vehicle slip prediction and control method and system, which realizes an accurate judgment of the vehicle slip ratio at the next moment, and then determines the vehicle slip risk level according to the vehicle slip ratio, and performs corresponding braking control on the vehicle according to the vehicle slip risk level, ensuring the stability and safety of the vehicle operation. The technical solutions are as follows:

[0006] On the one hand, a vehicle slip prediction and control method is provided, including:

[0007] Obtain the wheel speed time series data, vehicle body attitude time series data and steering wheel angle time series data of the vehicle;

[0008] Predict the vehicle slip ratio at the next moment according to the wheel speed time series data, vehicle body attitude time series data and steering wheel angle time series data, and obtain the predicted value of the vehicle slip ratio at the next moment;

[0009] Determine the vehicle slip risk level at the next moment according to the predicted value of the vehicle slip ratio at the next moment;

[0010] When the vehicle slip risk level at the next moment is medium level, pre-fill the braking fluid with a set pressure in the vehicle;

[0011] When the slip risk level of the vehicle at the next moment is a high level, full hydraulic braking and anti-lock braking are performed on the vehicle.

[0012] In an alternative embodiment, the target slip ratio of the vehicle at the next moment is also predicted based on the wheel speed time series data, body attitude time series data, and steering wheel angle time series data of the vehicle;

[0013] According to the degree to which the predicted value of the vehicle slip ratio at the next moment exceeds the target slip ratio, the slip risk level of the vehicle at the next moment is determined.

[0014] In an alternative embodiment, when the slip risk level of the vehicle at the next moment is a medium level, the pressure of the pre-filled brake fluid is determined according to the degree to which the predicted value of the vehicle slip ratio at the next moment exceeds the target slip ratio, and this pressure is used as the set pressure.

[0015] In an alternative embodiment, during the process of performing full hydraulic braking and anti-lock braking on the vehicle, the wheel speed and vehicle speed of the vehicle are obtained;

[0016] According to the wheel speed and vehicle speed of the vehicle, the actual value of the vehicle slip ratio is determined;

[0017] According to the actual value of the vehicle slip ratio and the target slip ratio, the degree to which the actual value of the vehicle slip ratio exceeds the target slip ratio is determined;

[0018] According to the degree to which the actual value of the vehicle slip ratio exceeds the target slip ratio, the hydraulic braking force is controlled to reduce the degree to which the actual value of the vehicle slip ratio exceeds the target slip ratio to the target interval.

[0019] In an alternative embodiment, when the slip risk level of the vehicle at the next moment is a low level, an alarm message is issued.

[0020] In an alternative embodiment, based on the wheel speed time series data, body attitude time series data, steering wheel angle time series data, and the trained slip ratio prediction model, the slip ratio of the vehicle at the next moment is predicted, where the slip ratio prediction model takes the wheel speed time series data, body attitude time series data, and steering wheel angle time series data as inputs and the slip ratio of the vehicle at the next moment as the output, and is obtained by constructing a long short-term memory network.

[0021] On the other hand, a vehicle slip prediction and control system is provided, including:

[0022] A data acquisition unit for acquiring the wheel speed time series data, body attitude time series data, and steering wheel angle time series data of the vehicle;

[0023] A slip ratio prediction unit for predicting the slip ratio of the vehicle at the next moment according to the wheel speed time series data, body attitude time series data, and steering wheel angle time series data, and obtaining the predicted value of the slip ratio of the vehicle at the next moment;

[0024] A slip risk level prediction unit for determining the slip risk level of a vehicle at the next moment according to the predicted value of the vehicle slip rate at the next moment;

[0025] A slip control unit for pre-filling the brake fluid with a set pressure for the vehicle when the slip risk level of the vehicle at the next moment is medium; and performing full hydraulic braking and anti-lock braking on the vehicle when the slip risk level of the vehicle at the next moment is high.

[0026] On the other hand, a computer device is provided, and the device includes:

[0027] A processor adapted to execute a computer program;

[0028] A computer-readable storage medium storing a computer program, which when executed by the processor, implements any one of the vehicle slip prediction and control methods in the above embodiments.

[0029] On the other hand, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program, and the computer program is adapted to be loaded and executed by a processor to implement any one of the vehicle slip prediction and control methods in the above embodiments.

[0030] On the other hand, a computer program product is provided, and the computer program product includes a computer program, which when executed by a processor, implements any one of the vehicle slip prediction and control methods in the above embodiments.

[0031] The beneficial effects brought by the technical solutions provided in the embodiments of the present application at least include:

[0032] The embodiments of the present application provide a vehicle slip prediction and control method and system. The method obtains the wheel speed time series data, body attitude time series data, and steering wheel angle time series data of the vehicle; accurately predicts the vehicle slip rate at the next moment according to the wheel speed time series data, body attitude time series data, and steering wheel angle time series data; then determines the slip risk level of the vehicle at the next moment according to the predicted value of the vehicle slip rate at the next moment; when the slip risk level of the vehicle at the next moment is medium, pre-fill the brake fluid with a set pressure for the vehicle to shorten the braking response time, so that when the driver steps on the brake pedal, the vehicle can be braked smoothly and effectively; when the slip risk level of the vehicle at the next moment is high, perform full hydraulic braking and anti-lock braking on the vehicle to ensure the braking effect of the vehicle; can take into account the influence of the slip risk level on the braking effect of the vehicle, improve the braking control effect of the vehicle, and ensure the stability of the vehicle during the braking process. Description of the Drawings

[0033] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the accompanying drawings required for the description of the embodiments. 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 accompanying drawings can be obtained based on these drawings.

[0034] Figure 1 is the overall flowchart of a vehicle slip prediction and control method provided by an exemplary embodiment of the present application;

[0035] Figure 2 is the flowchart of obtaining fusion data in a vehicle slip prediction and control method provided by an exemplary embodiment of the present application;

[0036] Figure 3 is the flowchart of training a slip ratio prediction model in a vehicle slip prediction and control method provided by an exemplary embodiment of the present application;

[0037] Figure 4 is the flowchart of hierarchical braking control in a vehicle slip prediction and control method provided by an exemplary embodiment of the present application. Detailed implementation manners

[0038] To make the objectives, technical solutions, and advantages of the present application clearer, the following further describes the embodiments of the present application in detail with reference to the accompanying drawings.

[0039] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0040] The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a", "the", and "said" used in the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0041] It should be noted that the information and data involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0042] It should be understood that depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0043] First, a brief introduction to the nouns involved in the embodiments of the present application is given:

[0044] Anti-lock Braking System (ABS): The main function of the ABS system is to prevent the wheels from locking during emergency braking, maintaining the vehicle's maneuverability and stability. It controls the release and application of the brake hydraulic pressure, enabling the wheels to operate in a rolling state with a critical locking gap, thereby increasing the braking deceleration, shortening the braking distance, and ensuring the vehicle's directional stability. When the ABS is working, it is equivalent to performing point braking at a high frequency, keeping the wheels in rolling friction with the road surface and making full use of the maximum adhesion between the wheels and the road surface for braking.

[0045] The Electronic Hydraulic Brake System (EHB for short) combines the advantages of electronic technology and traditional hydraulic braking systems, providing a more efficient, safer, and more intelligent braking solution for vehicles.

[0046] The EHB system mainly consists of an Electronic Control Unit (ECU), a hydraulic actuator, sensors, and a brake pedal simulator, etc. Its working principle can be summarized as follows: When the driver presses the brake pedal, the sensors on the pedal convert the displacement and force signals of the pedal into electrical signals and transmit them to the ECU. The ECU calculates the braking force required for each wheel through complex algorithms based on the received signals and other vehicle state information (such as vehicle speed, wheel speed, acceleration, etc.), and controls the hydraulic actuator to generate the corresponding hydraulic pressure, thereby achieving braking of the wheels.

[0047] The EHB has a pre-filled brake fluid function. In a general braking system, there is a gap between the friction pads and the brake discs to prevent premature wear of the friction pads. However, during emergency braking, the existence of this gap will increase the braking distance because it takes a certain amount of time for the friction pads to contact the brake discs. The pre-filled brake fluid function in the EHB system eliminates this gap by pre-filling the wheel brakes with low pressure in advance, enabling the brake pads to contact the brake discs faster during braking, thereby shortening the braking response time.

[0048] The slip ratio is a parameter that describes the degree of sliding when a vehicle's tire contacts the road surface during driving. It is usually used to measure the friction condition between the vehicle's tire and the road surface and is an important indicator in vehicle dynamic stability analysis.

[0049] The value range of the slip ratio is usually between 0 and 1. Among them, a slip ratio of 0 means that the tire does not slip, and the vehicle travels completely according to the rolling of the tire; a slip ratio of 1 means that the tire slips completely and does not provide any forward force.

[0050] In vehicle control, the slip ratio is a key monitoring parameter because it is directly related to the vehicle's traction, braking effect, and handling stability. For example, an excessive slip ratio may cause the vehicle to lose control, especially on low-friction roads (roads that can provide lower friction, such as ice roads, wet slippery roads, etc.).

[0051] Currently, there are two slip ratio prediction methods. One is to calculate the slip ratio of the vehicle based on the vehicle speed and wheel speed. This method can accurately determine the slip ratio, but there is a problem of response lag when performing braking control based on this slip ratio; the other is to predict the slip ratio at the next moment through the current slip ratio and the slip ratio at the previous moment, and both the current slip ratio and the slip ratio at the previous moment are calculated and determined according to the vehicle speed and wheel speed at the corresponding moment. Although this method realizes the advance prediction of the vehicle slip ratio, for sudden road condition changes in front of the vehicle, such as the appearance of ice and snow or wet slippery roads ahead, the current slip ratio and the slip ratio at the previous moment cannot accurately predict the slip ratio at the next moment; when the slip ratio prediction is inaccurate, the braking intervention for the vehicle is also inaccurate.

[0052] In addition, the current method compares the determined slip ratio with a pre-set slip ratio threshold. When the slip ratio exceeds the preset value, braking intervention is performed on the vehicle; first, under different road conditions, the braking requirements of the vehicle are different, and the pre-set slip ratio threshold cannot adapt to all road conditions. When using this preset threshold for vehicle braking intervention, the accuracy of vehicle braking intervention cannot be guaranteed, and there are problems of premature braking or untimely braking.

[0053] Therefore, in order to perform reliable and stable braking control on the vehicle, the embodiment of the present application first predicts the slip ratio, then determines the slip risk level according to the slip ratio prediction value, and finally selects the corresponding braking strategy according to the slip risk level to achieve effective and stable braking of the vehicle on the basis of ensuring the normal operation of the vehicle and ensuring the safe operation of the vehicle.

[0054] A vehicle slip prediction and control method proposed by the embodiment of the present application is applied to a vehicle equipped with ABS and EHB, and the involved structure includes: a wheel speed sensor, a six-axis IMU, a steering angle sensor, a data fusion module, a slip prediction module, ABS, EHB, and a hierarchical braking control module;

[0055] Among them, the wheel speed sensor is used to obtain the wheel speed data of the vehicle;

[0056] The six-axis IMU is used to obtain vehicle body attitude data;

[0057] The steering angle sensor is used to obtain the steering wheel angle data;

[0058] The data fusion module is used to fuse the wheel speed data, vehicle body attitude data and steering wheel angle data, eliminate noise and errors, and obtain more accurate and comprehensive vehicle state information.

[0059] The slip prediction module is used to predict the vehicle slip ratio and target slip ratio at the next moment according to the data fused by the data fusion module, and obtain the predicted value of the vehicle slip ratio and the target slip ratio at the next moment.

[0060] The hierarchical braking control module is used to determine the vehicle slip risk level at the next moment according to the predicted value of the vehicle slip ratio and the target slip ratio at the next moment; and generate corresponding braking strategies according to the vehicle slip risk level.

[0061] The ABS is used to perform anti-lock braking on the vehicle;

[0062] The EHB is used to perform full hydraulic braking on the vehicle, and the EHB has a function of pre-filling the brake fluid.

[0063] The wheel speed sensors are symmetrically installed on four wheel hubs, and the wheel speed is measured in real time through the magnetoresistive effect. The signals are transmitted to the data fusion module through shielded twisted pair wires, supporting high-frequency sampling to capture dynamic changes; the six-axis IMU is fixed at the vehicle center of mass position, and is designed with epoxy resin sealing and vibration prevention to synchronously collect acceleration and angular velocity data, reflecting the inertial characteristics of vehicle movement. The steering angle sensor is integrated under the steering column, and obtains the driver's steering intention through non-contact detection technology, providing input for lateral stability control. Collect data from the wheel speed sensors, six-axis IMU and steering wheel angle sensor. The data of these sensors is the basis for slip ratio prediction and can reflect the vehicle's motion state and driving operation conditions. For example, the wheel speed sensors provide the rotational speed information of each vehicle tire, the six-axis IMU can obtain the vehicle's acceleration and angular velocity, and the steering wheel angle sensor records the driver's steering operation, etc.

[0064] As Figure 1 shown, a vehicle slip prediction and control method proposed in an embodiment of the present application includes:

[0065] Obtain the wheel speed time series data, vehicle body attitude time series data and steering wheel angle time series data of the vehicle;

[0066] Predict the vehicle slip ratio at the next moment according to the wheel speed time series data, vehicle body attitude time series data and steering wheel angle time series data, and obtain the predicted value of the vehicle slip ratio at the next moment;

[0067] Determine the slip risk level of the vehicle at the next moment according to the predicted value of the vehicle slip rate at the next moment;

[0068] When the slip risk level of the vehicle at the next moment is medium, pre-fill the brake fluid with a set pressure for the vehicle;

[0069] When the slip risk level of the vehicle at the next moment is high, perform full hydraulic braking and anti-lock braking on the vehicle.

[0070] In addition, when the slip risk level of the vehicle at the next moment is low, send an alarm message.

[0071] When there is a change in the road condition in front of the vehicle, the driver will control the vehicle attitude and steering wheel in advance according to the observed road condition changes. Therefore, the vehicle attitude and steering wheel angle can reflect the road condition in front of the vehicle to a certain extent. When predicting the vehicle slip rate at the next moment by integrating the wheel speed time series data, vehicle body attitude time series data and steering wheel angle time series data of the vehicle, an accurate prediction of the vehicle slip rate at the next moment can be achieved. Furthermore, according to the accurately predicted predicted value of the vehicle slip rate at the next moment, determine the slip risk level of the vehicle at the next moment; when the slip risk level is low, only send an alarm message to attract the driver's attention; when the slip risk level is medium, pre-fill the brake fluid with a set pressure for the vehicle, so that when the vehicle needs to brake, the driver operating the brake pedal can brake the vehicle stably, and when the vehicle does not need to brake, it does not affect the normal driving of the vehicle; when the slip risk level is high, perform full hydraulic braking and anti-lock braking on the vehicle to achieve timely braking of the vehicle, thus ensuring the safety of the vehicle.

[0072] In some embodiments, also predict the target slip rate of the vehicle at the next moment according to the wheel speed time series data, vehicle body attitude time series data and steering wheel angle time series data of the vehicle;

[0073] Determine the slip risk level of the vehicle at the next moment according to the degree to which the predicted value of the vehicle slip rate at the next moment exceeds the target slip rate.

[0074] Among them, the time series data refers to the data obtained in the continuous time period before and at the current moment.

[0075] The degree to which the predicted value of the vehicle slip rate at the next moment exceeds the target slip rate is equal to the difference obtained by subtracting the target slip rate from the predicted value of the vehicle slip rate at the next moment, divided by the target slip rate.

[0076] When this degree is less than or equal to the first set value, determine that the slip risk level of the vehicle at the next moment is low; when this degree is greater than the first set value and less than or equal to the second set value, determine that the slip risk level of the vehicle at the next moment is medium; when this degree is greater than the second set value, the slip risk level of the vehicle at the next moment is high.

[0077] Preferably, the first set value is equal to 30%, and the second set value is equal to 70%.

[0078] Based on the wheel speed time series data, body attitude time series data, and steering wheel angle time series data of the vehicle, an accurate prediction of the target slip ratio of the vehicle at the next moment is achieved; the target slip ratio can adapt to the requirements of the road surface where the vehicle is located at the next moment, and at this target slip ratio, the braking performance of the vehicle is optimal; using this target slip ratio, an accurate prediction of the vehicle slip risk level at the next moment can be achieved, and then the vehicle can be accurately controlled.

[0079] In some embodiments, the body attitude information includes body attitude time series data including lateral acceleration and angular velocity time series data, longitudinal acceleration and angular velocity time series data, and vertical acceleration and angular velocity time series data.

[0080] Through the body attitude information, the inertial characteristics of the vehicle's movement are reflected, and to a certain extent, the driver's control intention can be reflected.

[0081] In some embodiments, based on the wheel speed time series data, body attitude time series data, steering wheel angle time series data, and a trained slip ratio prediction model, the slip ratio of the vehicle at the next moment is predicted. Among them, the slip ratio prediction model takes the wheel speed time series data, body attitude time series data, and steering wheel angle time series data as inputs, and the slip ratio of the vehicle at the next moment as the output, and is obtained by constructing through a long short-term memory network.

[0082] Preferably, the slip ratio prediction model also synchronously outputs the target slip ratio of the vehicle at the next moment.

[0083] By constructing a slip ratio prediction model through a long short-term memory network (LSTM), and predicting the slip ratio and target slip ratio of the vehicle at the next moment through the slip ratio prediction model, the accuracy of the prediction is guaranteed, and the accuracy and rate of the prediction are improved.

[0084] The dimension of the input layer of the slip ratio prediction model is determined by the time window length and the number of features. The long short-term memory network uses two LSTM layers to capture the long-term dependencies in the input data. The LSTM layer controls the flow of information through a gating mechanism (forget gate, input gate, and output gate), and can effectively handle the long-term dependency problem in time series data. The first LSTM layer returns a sequence so that the subsequent LSTM layer can continue to process. The fully connected layer maps the output of the LSTM layer to a single output node, and uses a sigmoid activation function to output the predicted value and target slip ratio of the vehicle at the next moment, with a range between 0 and 1.

[0085] The embodiments of the present application significantly improve the safety and controllability of the vehicle under complex working conditions through multi-sensor fusion, dynamic prediction model and hierarchical braking control strategy: integrating wheel speed sensor, six-axis IMU and steering wheel angle sensor to realize multi-dimensional state perception, eliminating the information blind spot of a single sensor; the prediction model based on LSTM network and fuzzy logic provides early warning of slip risk, gaining response time for the braking system; three-level braking intervention mechanism and pre-filling technology shorten braking delay and improve braking force distribution accuracy, effectively solving the core problems of traditional braking system such as response lag, poor adaptability and insufficient coordinated control, and providing a more reliable technical solution for intelligent driving safety.

[0086] After constructing the slip rate prediction model, the embodiment of the present application also collects a large amount of training data. The training data is data marked with the vehicle slip rate and target slip rate at the next moment, including a large amount of training wheel speed time series data, vehicle body posture time series data and steering wheel angle time series data.

[0087] After acquiring training data, preprocess the data to remove outliers and missing values. Outliers may be unreasonable data caused by sensor failure or interference, such as wheel speed values that suddenly exceed the normal range. Missing values may be caused by data transmission issues and require appropriate processing methods, such as deleting corresponding records or interpolating and filling. Sensor data of different ranges are normalized to the same interval, typically [0, 1]. This prevents certain features from dominating model training due to their large value range. For example, wheel speed data may range from 0 to 200 km / h, while six-axis IMU data may have a smaller range. Normalization standardizes sensor data of different dimensions, unifies the feature space scale, and ensures model training stability. Normalization ensures that they have equal importance in model training.

[0088] The preprocessed training data is divided into a training set, a validation set, and a test set. The training set is used for model parameter learning, the validation set is used to adjust the model's hyperparameters during training, such as the learning rate and batch size, and the test set is used to evaluate the model's final performance. When the model performance meets the requirements, the model training is completed. When the model performance does not meet the requirements, it is necessary to adjust the model's hyperparameters, such as the number of neurons in the LSTM layer, the learning rate, the number of training rounds, etc., and then re-build and train the model until satisfactory results are obtained.

[0089] A common split is 70% for training, 15% for validation, and 15% for testing.

[0090] In some embodiments, when the slip risk level of the vehicle at the next moment is medium, the pressure of the pre-filled brake fluid is determined according to the degree to which the predicted value of the vehicle slip rate at the next moment exceeds the target slip rate, and this pressure is used as the set pressure to pre-fill the vehicle with brake fluid at the set pressure.

[0091] Among them, the process of determining the pressure of the pre-filled brake fluid is as follows:

[0092] Determine the degree to which the predicted value of the vehicle slip rate at the next moment exceeds the target slip rate, which is defined as the excess degree;

[0093] When the excess degree is equal to the first set value, the set pressure is 0; when the excess degree is the second set value, the set pressure is the maximum brake fluid pressure allowed by the electronic hydraulic braking system;

[0094] When the excess degree is between the first set value and the second set value, the set pressure changes linearly between 0 and the maximum brake fluid pressure with the excess degree.

[0095] By determining the pressure of the pre-filled brake fluid according to the degree to which the predicted value of the vehicle slip rate at the next moment exceeds the target slip rate, the control of the vehicle is further combined with the environment in which the vehicle is located. When the vehicle needs to brake, the driver controls the brake pedal, and the vehicle can be effectively braked in a timely manner.

[0096] In some embodiments, when it is determined that the slip risk level of the vehicle at the next moment is high, during the full hydraulic braking and anti-lock braking of the vehicle, the vehicle wheel speed and vehicle speed are obtained;

[0097] According to the vehicle wheel speed and vehicle speed, the actual value of the vehicle slip rate is determined;

[0098] According to the actual value of the vehicle slip rate and the target slip rate, determine the degree to which the actual value of the vehicle slip rate exceeds the target slip rate;

[0099] According to the degree to which the actual value of the vehicle slip rate exceeds the target slip rate, control the hydraulic braking force to reduce the degree to which the actual value of the vehicle slip rate exceeds the target slip rate to the target interval.

[0100] Preferably, the PID algorithm is used to control the hydraulic braking force, dynamically adjust the braking pressure, and the degree to which the actual value of the vehicle slip rate exceeds the target slip rate, and this target interval is 15% - 20%.

[0101] And during the full hydraulic braking and anti-lock braking of the vehicle, before the degree to which the actual value of the vehicle slip rate exceeds the target slip rate is reduced to the target interval, the driver is not allowed to take over to prevent the vehicle from having a braking risk when the driver controls the brake pedal force insufficiently.

[0102] Preferably, the hydraulic braking force of the EHB is adjusted by controlling the solenoid valve through PWM.

[0103] The wheel speed sensor obtains the vehicle wheel speed with a sampling period of 10 ms.

[0104] A vehicle slip prediction and control method proposed in an embodiment of the present application can accurately predict the vehicle slip ratio at the next moment according to the wheel speed time series data, the vehicle body attitude time series data, and the steering wheel angle time series data; then, according to the predicted value of the vehicle slip ratio at the next moment, determine the vehicle slip risk level at the next moment; when the vehicle slip risk level at the next moment is medium, pre-fill the vehicle with brake fluid of a set pressure to shorten the braking response time, so that when the driver steps on the brake pedal, the vehicle can be braked smoothly and effectively; when the vehicle slip risk level at the next moment is high, perform full hydraulic braking and anti-lock braking on the vehicle to ensure the braking effect of the vehicle; it can take into account the influence of the slip risk level on the vehicle braking effect, improve the braking control effect of the vehicle, and ensure the stability of the vehicle during braking. It effectively solves the core problems of the traditional braking system such as response lag, poor adaptability, and insufficient coordinated control, and provides a more reliable technical solution for intelligent driving safety.

[0105] An embodiment of the present application also provides a vehicle slip prediction and control system, including:

[0106] A data acquisition unit for acquiring the wheel speed time series data, the vehicle body attitude time series data, and the steering wheel angle time series data of the vehicle;

[0107] A slip ratio prediction unit for predicting the vehicle slip ratio at the next moment according to the wheel speed time series data, the vehicle body attitude time series data, and the steering wheel angle time series data, and obtaining the predicted value of the vehicle slip ratio at the next moment;

[0108] A slip risk level prediction unit for determining the vehicle slip risk level at the next moment according to the predicted value of the vehicle slip ratio at the next moment;

[0109] A slip control unit for pre-filling the vehicle with brake fluid of a set pressure when the vehicle slip risk level at the next moment is medium; and performing full hydraulic braking and anti-lock braking on the vehicle when the vehicle slip risk level at the next moment is high.

[0110] An embodiment of the present application also provides a computer device, which includes:

[0111] A processor suitable for executing a computer program;

[0112] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a vehicle slip prediction and control method provided by an embodiment of the present application.

[0113] An embodiment of the present application further provides a computer-readable storage medium that stores a computer program, and the computer program is suitable for being loaded and executed by a processor to implement a vehicle slip prediction and control method provided by an embodiment of the present application.

[0114] An embodiment of the present application further provides a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, it implements a vehicle slip prediction and control method provided by an embodiment of the present application.

[0115] The method disclosed in Embodiment 1 can be directly embodied as being executed by a hardware processor, or by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0116] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0117] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that based on the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. A vehicle slip prediction and control method, characterized in that, Including: Obtain the wheel speed time series data, vehicle body attitude time series data, and steering wheel angle time series data of the vehicle; Predict the vehicle slip ratio at the next moment based on the wheel speed time series data, vehicle body attitude time series data, and steering wheel angle time series data, and obtain the predicted value of the vehicle slip ratio at the next moment; Determine the slip risk level of the vehicle at the next moment according to the predicted value of the vehicle slip ratio at the next moment; When the slip risk level of the vehicle at the next moment is medium level, pre-fill the vehicle with brake fluid of a set pressure; When the slip risk level of the vehicle at the next moment is high level, perform full hydraulic braking and anti-lock braking on the vehicle.

2. The vehicle slip prediction and control method according to claim 1, characterized in that, Also predict the target slip ratio of the vehicle at the next moment based on the wheel speed time series data, vehicle body attitude time series data, and steering wheel angle time series data of the vehicle; Determine the slip risk level of the vehicle at the next moment according to the degree to which the predicted value of the vehicle slip ratio at the next moment exceeds the target slip ratio.

3. A vehicle slip prediction and control method according to claim 1, characterized in that, When the slip risk level of the vehicle at the next moment is medium level, determine the pressure of the pre-filled brake fluid according to the degree to which the predicted value of the vehicle slip ratio at the next moment exceeds the target slip ratio, and use this pressure as the set pressure.

4. The vehicle slip prediction and control method according to claim 1, wherein During the process of performing full hydraulic braking and anti-lock braking on the vehicle, obtain the vehicle wheel speed and vehicle speed; Determine the actual value of the vehicle slip ratio according to the vehicle wheel speed and vehicle speed; Determine the degree to which the actual value of the vehicle slip ratio exceeds the target slip ratio according to the actual value of the vehicle slip ratio and the target slip ratio; Control the hydraulic braking force according to the degree to which the actual value of the vehicle slip ratio exceeds the target slip ratio, and reduce the degree to which the actual value of the vehicle slip ratio exceeds the target slip ratio to the target range.

5. A vehicle slip prediction and control method according to claim 1, characterized in that, When the slip risk level of the vehicle at the next moment is low level, send an alarm message.

6. The vehicle slip prediction and control method according to claim 1, characterized in that Predict the vehicle slip ratio at the next moment according to the wheel speed time series data, vehicle body attitude time series data, steering wheel angle time series data, and the trained slip ratio prediction model, where the slip ratio prediction model takes the wheel speed time series data, vehicle body attitude time series data, and steering wheel angle time series data as inputs and the vehicle slip ratio at the next moment as the output, and is obtained by constructing a long short-term memory network.

7. A vehicle slip prediction and control system, characterized in that, Including: A data acquisition unit for obtaining the wheel speed time series data, vehicle body attitude time series data, and steering wheel angle time series data of the vehicle; A slip ratio prediction unit for predicting the vehicle slip ratio at the next moment based on the wheel speed time series data, vehicle body attitude time series data, and steering wheel angle time series data, and obtaining the predicted value of the vehicle slip ratio at the next moment; A slip risk level prediction unit for determining the slip risk level of the vehicle at the next moment according to the predicted value of the vehicle slip ratio at the next moment; A slip control unit for pre-filling the vehicle with brake fluid of a set pressure when the slip risk level of the vehicle at the next moment is medium level; and performing full hydraulic braking and anti-lock braking on the vehicle when the slip risk level of the vehicle at the next moment is high level.

8. An electronic device, characterized in that, The device includes: A processor suitable for executing a computer program; A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by the processor, it implements a vehicle slip prediction and control method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is adapted to be loaded and executed by a processor to perform a vehicle slip prediction and control method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by a processor, it implements a vehicle slip prediction and control method according to any one of claims 1-6.

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