Vehicle braking control method and device

Abstract

The embodiment of the invention provides a vehicle braking control method and device.The vehicle braking control method comprises the steps that vehicle operation parameters of a running vehicle are obtained and subjected to weighted fusion processing, and a dynamic working condition index is generated; acquiring a real-time driving signal of a current driver in a sliding window at preset time and importing the real-time driving signal into a preset driving intention prediction model to generate a driving intention type; according to the dynamic working condition index and the driving intention type, the braking force demand level of each wheel cylinder is determined; and the brake oil quantity of each wheel cylinder is adjusted based on the brake force demand level so as to adjust the brake condition of the vehicle, and the brake oil quantity of each wheel cylinder is corrected in real time according to the obtained feedback signal. According to the method, accurate matching of brake pressure distribution and vehicle dynamic characteristics can be achieved, front and rear shaft brake torque distribution is effectively balanced under the curve brake working condition, the response time from driving intention recognition to pressure adjustment execution is shortened, and the coordination control capacity of a brake system is improved under the sudden avoidance scene.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of vehicle braking control, and particularly to a vehicle braking control method and device. Background Art

[0002] The vehicle braking system is a series of special devices on the vehicle that apply a certain force to some parts of the vehicle to forcefully brake it to a certain extent. The braking system can force a moving vehicle to decelerate or even stop according to the driver's requirements; keep a parked vehicle stationary stably under various road conditions; and keep the speed of a downhill moving vehicle stable. With the development of science, intelligence has also been applied to the vehicle braking system.

[0003] Currently, the intelligent control of the braking method for vehicles often only relies on a single signal for judgment, resulting in low judgment accuracy and poor practicability. Summary of the Invention

[0004] In view of this, the embodiments of this specification provide a vehicle braking control method. One or more embodiments of this specification also relate to a vehicle braking control device, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects existing in the prior art.

[0005] According to the first aspect of the embodiments of this specification, a vehicle braking control method is provided, including: Obtain the vehicle operation parameters of a running vehicle and perform weighted fusion processing to generate a dynamic working condition index; Obtain the real-time driving signal of the current driver within a preset time sliding window and import it into a preset driving intention prediction model to generate a driving intention type, where the real-time driving signal includes a steering wheel angle signal and a pedal travel signal; Determine the braking force demand level of each wheel cylinder according to the dynamic working condition index and the driving intention type, where the braking force demand level includes a low demand level representing maintaining the current braking, a medium demand level representing increasing the front wheel distribution ratio, and a high demand level representing distributing the maximum pressure; Adjust the braking oil volume of each wheel cylinder based on the braking force demand level to adjust the braking condition of the vehicle, and real-time correct the braking oil volume of each wheel cylinder according to the obtained feedback signal.

[0006] In some embodiments, obtaining the vehicle operation parameters of a running vehicle and performing weighted fusion processing to generate a dynamic working condition index includes: Obtain the vehicle operation parameters of a running vehicle, where the vehicle operation parameters include a vehicle speed signal, a load signal, a road surface friction signal, a coaxial wheel speed difference data, and a road surface texture recognition data; Perform noise filtering processing on the vehicle speed signal to obtain a smoothed vehicle speed; Normalize the load signal to generate a dynamic load; Extract features from the road surface friction signal, combine the coaxial wheel speed difference data and the road surface texture recognition data, and generate a comprehensive friction index through fuzzy logic; Perform weighted fusion processing on the smoothed vehicle speed, dynamic load parameters, and comprehensive friction index based on the current weighting index, and the weighting index is dynamically adjusted according to the vehicle operation mode, where the vehicle operation mode includes a stable mode, a critical model, and a dangerous mode.

[0007] In some embodiments, the steps of generating the driving intention type include: Obtain the steering wheel angle signal and the pedal travel signal in a preset time sliding window, and calculate the change rates respectively to obtain the steering trend feature and the emergency operation feature; Compare the steering trend feature with a preset steering threshold, and at the same time compare the emergency operation feature with a preset emergency threshold to obtain a comparison result; Match according to the comparison result and a preset driving intention comparison table to determine the current driver's driving intention type, where the driving intention type includes a steady driving mode, a predictive operation mode, and an emergency operation mode.

[0008] In some embodiments, according to the obtained feedback signal, the brake fluid volume of each wheel cylinder is corrected in real time, including: Calculate the pressure deviation value of each wheel cylinder according to the obtained feedback signal; Perform temperature compensation processing on the oil temperature of each wheel cylinder according to the ambient temperature to generate a viscosity correction coefficient; Import the pressure deviation value and the viscosity correction coefficient into a preset calculation model to generate a valve opening adjustment index for each wheel cylinder; Adjust the valve opening of each wheel cylinder according to the valve opening adjustment index to correct the brake fluid volume of each wheel cylinder in real time.

[0009] In some embodiments, calculating the pressure deviation value of each wheel cylinder according to the obtained feedback signal includes: Obtain the feedback signal of each wheel cylinder; Determine the real-time pressure of each wheel cylinder according to the feedback signal; Calculate the difference between each real-time pressure and the target braking pressure, where the target braking pressure is the target calculated value corresponding to the braking force demand level.

[0010] In some embodiments, the above method further includes: When it is detected that the signal transmitted by the sensor is abnormal, interrupt the transmission signal of the sensor with abnormal signal acquisition; Obtain a redundant data source corresponding to the sensor with abnormal signal, where the redundant data source is obtained by calculating the signals transmitted by other sensors.

[0011] In some embodiments, the above method further includes: Caching the vehicle operation parameters within a preset time window; When it is detected that the duration of data transmission interruption exceeds the limit, performing calculations based on the cached vehicle operation parameters.

[0012] According to the second aspect of the embodiments of the present specification, a vehicle braking control device is provided, including: A first generation module configured to obtain the vehicle operation parameters of a running vehicle and perform weighted fusion processing to generate a dynamic working condition index; A second generation module configured to obtain the real-time driving signals of the current driver within a preset time sliding window and import them into a preset driving intention prediction model to generate a driving intention type, where the real-time driving signals include a steering wheel angle signal and a pedal stroke signal; A determination module configured to determine the braking force demand levels of each wheel cylinder according to the dynamic working condition index and the driving intention type, where the braking force demand levels include a low demand level representing maintaining the current braking, a medium demand level representing increasing the front wheel distribution ratio, and a high demand level representing distributing the maximum pressure; An adjustment module configured to adjust the braking fluid volume of each wheel cylinder based on the braking force demand level to adjust the braking condition of the vehicle, and to correct the braking fluid volume of each wheel cylinder in real time according to the obtained feedback signal.

[0013] In some embodiments, obtaining the vehicle operation parameters of a running vehicle and performing weighted fusion processing to generate a dynamic working condition index includes: Obtaining the vehicle operation parameters of a running vehicle, where the vehicle operation parameters include a vehicle speed signal, a load signal, a road surface friction signal, a coaxial wheel speed difference data, and a road surface texture recognition data; Performing noise filtering processing on the vehicle speed signal to obtain a smoothed vehicle speed; Performing normalization processing on the load signal to generate a dynamic load; Performing feature extraction on the road surface friction signal, combining the coaxial wheel speed difference data and the road surface texture recognition data, and generating a comprehensive friction index through fuzzy logic; Performing weighted fusion processing on the smoothed vehicle speed, the dynamic load parameter, and the comprehensive friction index based on the current weighted index, where the weighted index is dynamically adjusted according to the vehicle operation mode, and the vehicle operation mode includes a stable mode, a critical model, and a dangerous mode.

[0014] In some embodiments, the steps of generating a driving intention type include: Obtaining the steering wheel angle signal and the pedal stroke signal in a preset time sliding window, and respectively calculating the change rates to obtain a steering trend feature and an emergency operation feature; Compare the steering trend feature with a preset steering threshold, and at the same time compare the emergency operation feature with a preset emergency threshold to obtain a comparison result; Match according to the comparison result and a preset driving intention comparison table to determine the current driver's driving intention type, where the driving intention type includes a smooth driving mode, a predictive operation mode, and an emergency operation mode.

[0015] In some embodiments, the brake fluid volume of each wheel cylinder is corrected in real time according to the acquired feedback signal, including: Calculate the pressure deviation value of each wheel cylinder according to the acquired feedback signal; Perform temperature compensation processing on the oil temperature of each wheel cylinder according to the ambient temperature to generate a viscosity correction coefficient; Import the pressure deviation value and the viscosity correction coefficient into a preset calculation model to generate a valve opening adjustment index for each wheel cylinder; Adjust the valve opening of each wheel cylinder according to the valve opening adjustment index to correct the brake fluid volume of each wheel cylinder in real time.

[0016] In some embodiments, calculating the pressure deviation value of each wheel cylinder according to the acquired feedback signal includes: Obtain the feedback signal of each wheel cylinder; Determine the real-time pressure of each wheel cylinder according to the feedback signal; Calculate the difference between each real-time pressure and the target braking pressure, where the target braking pressure is the target calculated value corresponding to the braking force demand level.

[0017] In some embodiments, the above device further includes a first exception handling module, configured to: when detecting an abnormal signal transmitted by a sensor, interrupt the transmission signal of the sensor with the abnormal signal; obtain a redundant data source corresponding to the sensor with the abnormal signal, where the redundant data source is obtained by calculating the signals transmitted by other sensors.

[0018] In some embodiments, the above device further includes a second exception handling module, configured to: cache the vehicle operation parameters within a preset time window; when detecting that the duration of the data transmission interruption exceeds the limit, perform calculations based on the cached vehicle operation parameters.

[0019] According to the fourth aspect of the embodiments of the present specification, a vehicle is provided, characterized in that the vehicle is provided with a control center and a hydraulic braking module AHB, and the hydraulic braking module AHB is provided with a brake controller unit ECU, a supercharging unit PSU, and a hydraulic control unit PCU, where The control center is used to execute the steps of the vehicle braking control method in the foregoing claims; The brake control unit ECU is respectively connected to the supercharging unit PSU and the hydraulic control unit PCU, and is used to receive the control instructions sent by the superior control center, and generate a first instruction and a second instruction according to the control instructions to control the supercharging unit PSU and the hydraulic control unit PCU to perform pressure control. Among them, the first instruction is sent to the supercharging unit PSU, and the second instruction is sent to the supercharging unit PSU; The supercharging unit PSU is used to output hydraulic oil with a certain pressure to the hydraulic control unit PCU according to the received first instruction; The hydraulic control unit PCU is used to adjust, control, and distribute the output pressure according to the received second instruction to ensure different working pressures under different working conditions.

[0020] According to the fourth aspect of the embodiments of the present specification, a computing device is provided, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above vehicle braking control method are implemented.

[0021] According to the fifth aspect of the embodiments of the present specification, a computer-readable storage medium is provided, which stores computer-executable instructions. When the instructions are executed by the processor, the steps of the above vehicle braking control method are implemented.

[0022] According to the sixth aspect of the embodiments of the present specification, a computer program is provided. When the computer program is executed on a computer, the computer is made to execute the steps of the above vehicle braking control method.

[0023] In at least one embodiment of the present specification, the vehicle operation parameters of a running vehicle are acquired and weighted fusion processing is performed to generate a dynamic working condition index; the real-time driving signals of the current driver in a preset time window are acquired and imported into a preset driving intention prediction model to generate a driving intention type, where the real-time driving signals include a steering wheel angle signal and a pedal stroke signal; according to the dynamic working condition index and the driving intention type, the braking force demand level of each wheel cylinder is determined, where the braking force demand level includes a low demand level representing maintaining the current braking, a medium demand level representing increasing the front wheel distribution ratio, and a high demand level representing distributing the maximum pressure; based on the braking force demand level, the braking fluid volume of each wheel cylinder is adjusted to adjust the braking condition of the vehicle, and the braking fluid volume of each wheel cylinder is corrected in real time according to the acquired feedback signal. The present application can achieve an accurate match between the braking pressure distribution and the vehicle dynamic characteristics, and effectively balance the front and rear axle braking torque distribution under the corner braking condition. When braking emergently on a low-adhesion road surface, the wheel premature locking is avoided by dynamically adjusting the pressure increase gradient. At the same time, the response time from identifying the driving intention to executing the pressure adjustment is shortened, and the coordinated control ability of the braking system is improved in the sudden avoidance scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart of some embodiments of a vehicle braking control method provided by some embodiments of the present specification; Figure 2 is a flowchart of other embodiments of a vehicle braking control method provided by some embodiments of the present specification; Figure 3 is a schematic diagram of a simple structure of a vehicle braking control device provided by some embodiments of the present specification; Figure 4 is a block diagram of a computing device provided by some embodiments of the present specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Many specific details are set forth in the following description in order to provide a thorough understanding of the present specification. However, the present specification can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of the present specification. Therefore, the present specification is not limited by the specific implementations disclosed below.

[0026] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a" and "the" used in one or more embodiments of this specification 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 in one or more embodiments of this specification refers to and encompasses any and all possible combinations of one or more of the associated listed items. The modifiers "a" and "plural" mentioned in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless clearly indicated otherwise in the context, it should be understood as "one or more".

[0027] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0028] First, the noun terms related to one or more embodiments of this specification are explained.

[0029] AHB: Accumulator-based Electro-hydraulic Braking Module, an energy storage type electro-hydraulic braking module; ECU: Electronic Control Unit, a braking control unit; PSU: Pressure Supply Unit, a pressure boosting unit; PCU: Pressure Control Unit, a hydraulic control unit.

[0030] In the prior art, traditional vehicle braking adjustment methods usually use fixed thresholds or single parameters for braking force distribution, making it difficult to adapt to complex and changeable driving conditions. Especially in scenarios such as emergency lane changes and slippery roads, the existing systems cannot effectively coordinate the relationship between vehicle dynamic loads and the driver's operation intention, easily resulting in uneven braking pressure distribution and affecting vehicle stability. For example, when the vehicle brakes on a curve, the traditional method may ignore the influence of the steering trend on the wheel cylinder pressure distribution, causing the braking torque distribution to not match the actual motion state of the vehicle.

[0031] To solve the above problems, the R & D personnel noticed that the existing braking system has defects in dynamic response lag and insufficient working condition adaptability. Through in-depth analysis, it was found that relying solely on wheel speed difference or pedal signal is difficult to accurately reflect the actual braking demand of the vehicle. By studying the vehicle dynamics model, it was found that the coupling relationship of comprehensive dynamic load and road surface friction coefficient can more accurately characterize the braking condition. At the same time, it was found that the change trend of the driver's operation signal can better predict the braking intention than the absolute value. Based on these findings, it is proposed to conduct a fusion analysis of multi-dimensional vehicle parameters and driving behavior characteristics, construct a dual judgment mechanism of dynamic working condition index and driving intention type, and then establish a hierarchical braking force distribution strategy.

[0032] Therefore, this application proposes to generate a dynamic working condition index by obtaining vehicle operation parameters and performing weighted fusion. The dynamic working condition index includes a dynamic load level and a comprehensive friction level. At the same time, obtain the driver's real-time driving signal and predict the type of driving intention, determine the braking force demand level of each wheel cylinder according to the dynamic working condition index and the type of driving intention, and finally adjust the brake fluid volume based on the demand level and correct it in real time.

[0033] See Figure 1 , Figure 1 shows a flowchart of a vehicle braking control method provided according to some embodiments of this specification, specifically including the following steps.

[0034] Step 101: Obtain the vehicle operation parameters of the running vehicle and perform weighted fusion processing to generate a dynamic working condition index.

[0035] Specifically, the vehicle operation parameters refer to a multi-dimensional data set reflecting the vehicle's motion state, and can specifically be signals or data collected by devices such as a vehicle speed sensor, a load sensor, and a wheel speed sensor.

[0036] The dynamic working condition index refers to a quantitative index characterizing the real-time motion state of the vehicle, providing a dynamic benchmark for braking force distribution.

[0037] Step 102: Obtain the real-time driving signal of the current driver in a preset time sliding window and import it into a preset driving intention prediction model to generate a driving intention type, where the real-time driving signal includes a steering wheel angle signal and a pedal travel signal.

[0038] The driving intention type refers to the classification and recognition of the driver's operation behavior.

[0039] As a specific example, the driving intention type can include the following three categories: D1 (steady driving): It can indicate that the throttle / brake change rate < 10% / s.

[0040] D2 (predictive operation): It can indicate that the steering wheel angle continuously > 15°.

[0041] D3 (Emergency operation): It can indicate that the instantaneous brake pedal stroke reaches more than 80%.

[0042] Step 103: Determine the braking force demand levels of each wheel cylinder according to the dynamic working condition index and the type of driving intention. Among them, the braking force demand levels include a low demand level representing maintaining the current braking, a medium demand level representing increasing the front-wheel distribution ratio, and a high demand level representing distributing the maximum pressure.

[0043] In some optional implementation manners, the braking force demand levels of each wheel cylinder can be determined according to the dynamic working condition index, the type of driving intention, and a preset braking force prediction model. The braking force prediction model can be various calculation models capable of outputting classification results and can be set according to needs.

[0044] The braking force demand level refers to the pressure distribution priority for different wheel cylinders. Specifically, a three-level distribution strategy of low, medium, and high can be established, corresponding to three pressure regulation modes of maintaining, adjusting, and maximizing respectively.

[0045] As a specific example, the braking force demand levels can include the following three categories: C1: Low demand, which can indicate that maintaining the current distribution is sufficient.

[0046] C2: Medium demand, which can indicate increasing the braking force of the front-wheel distribution ratio by 10%.

[0047] C3: High demand, which can indicate full-wheel maximum pressure distribution. For example, it can preferentially meet the braking demand of the wheel cylinder on the side with a high friction coefficient.

[0048] Step 104: Adjust the brake fluid volume of each wheel cylinder based on the braking force demand level to adjust the braking condition of the vehicle, and dynamically correct the brake fluid volume of each wheel cylinder according to the obtained feedback signal.

[0049] Specifically, after the vehicle operation parameters are preprocessed, a dynamic working condition index comprehensively reflecting the vehicle load distribution and road surface friction condition is generated through a dynamic weight distribution algorithm. At the same time, the steering wheel angle signal and the pedal stroke signal collected in real time are input into the trained classification model to identify the type of driving intention after the change rate calculation. When it is detected that the steering trend feature and the emergency braking feature exist simultaneously, the system automatically raises the front-wheel braking force distribution priority. The pressure increase gradient is adjusted according to the friction level in the dynamic working condition index, and a progressive pressure increase strategy is adopted on the low-friction road surface. During the braking execution process, the pressure feedback values of each wheel cylinder are monitored in real time, and the valve opening is dynamically corrected through a closed-loop control algorithm to ensure that the deviation between the actual pressure and the target value is controlled within the allowable range.

[0050] Compared with the prior art, traditional methods rely on preset fixed distribution ratios and cannot dynamically adjust the pressure distribution according to the actual motion state of the vehicle. This solution enables the braking system to perceive changes in vehicle load transfer and road adhesion conditions in real time by introducing a dynamic operating condition index. Compared with the anti-lock braking strategy that solely relies on wheel speed differences, this method can predict potential slip risks at the initial stage of braking and adjust the pressure distribution in advance. In addition, the prior art has a lag in judging the driver's operation intention, while this solution can identify the emergency braking demand before the pedal depth reaches the threshold by analyzing the change trend of the operation signal.

[0051] Through the above technical solution, this application can achieve an accurate match between the braking pressure distribution and the dynamic characteristics of the vehicle, and effectively balance the braking torque distribution between the front and rear axles under cornering braking conditions. When braking emergently on a low-adhesion road surface, the pressure increase gradient is dynamically adjusted to avoid premature wheel lock-up. At the same time, the response time from identifying the driving intention to executing the pressure adjustment is shortened, and the coordinated control ability of the braking system is improved in sudden avoidance scenarios.

[0052] In some embodiments, this application further proposes a vehicle braking control method that obtains the vehicle operation parameters of the running vehicle and performs weighted fusion processing to generate a dynamic operating condition index, including obtaining the vehicle operation parameters of the running vehicle. The vehicle operation parameters include vehicle speed signal, load signal, road surface friction signal, coaxial wheel speed difference data, and road surface texture recognition data; performing noise filtering processing on the vehicle speed signal to obtain a smoothed vehicle speed; performing normalization processing on the load signal to generate a dynamic load; extracting features from the road surface friction signal, and generating a comprehensive friction index through fuzzy logic in combination with the coaxial wheel speed difference data and the road surface texture recognition data; performing weighted fusion processing on the smoothed vehicle speed, dynamic load parameter, and comprehensive friction index based on the current weighting index, and the weighting index is dynamically adjusted according to the vehicle operation mode. The vehicle operation mode includes a stable mode, a critical mode, and a dangerous mode.

[0053] Among them, the noise filtering processing refers to using a digital filter to remove the high-frequency interference components in the vehicle speed signal, which can be specifically implemented by using a Butterworth low-pass filter, and filtering out the signal jitter caused by wheel vibration or sensor noise by setting the cut-off frequency. The normalization processing refers to mapping the original value of the load signal to a preset interval, which can be specifically implemented by using a linear normalization algorithm to convert the load signals with different dimensions into dimensionless numerical values with the same dimension for subsequent weighted calculation. Generating a comprehensive friction index through fuzzy logic refers to performing fuzzy inference operations on the characteristic values of the road surface friction signal, the coaxial wheel speed difference, and the road surface texture data, which can be specifically implemented by using a fuzzy rule base for membership degree calculation to convert the discrete sensor data into continuous comprehensive friction level parameters. Dynamically adjusting the weighting index refers to selecting different weight distribution strategies according to the vehicle operation mode. For example, an equal weight distribution is adopted in the stable mode, and the weight ratio of the comprehensive friction index is increased in the dangerous mode.

[0054] Specifically, after the vehicle operation parameters are collected by multi-source sensors, first, a low-pass filter is applied to the vehicle speed signal to eliminate high-frequency noise interference. For example, a Butterworth filter is used to filter out noise components above 30 Hz, and the effective vehicle speed signal in the range of 0 - 10 Hz is retained. The load signal is normalized and then converted into a dynamic load parameter in the range of 0 - 1, which is convenient for weighted superposition operations with other parameters. The road surface friction signal obtains the change trend of the friction coefficient through time-domain feature extraction. Combining the tire slip characteristics reflected by the coaxial wheel speed difference data and the road surface texture recognition data collected by the vision sensor, a comprehensive friction index is output through a fuzzy logic system. The three core parameters, namely the smoothed vehicle speed, the dynamic load, and the comprehensive friction index, are fused and calculated according to the weighted coefficients corresponding to the current vehicle operation mode. For example, in the dangerous mode, a weight coefficient of 0.6 is assigned to the comprehensive friction index, and only a weight coefficient of 0.3 is assigned in the stable mode. Finally, a condition index representing the dynamic operation state of the vehicle is generated.

[0055] Compared with the prior art, traditional methods often use fixed weight coefficients when calculating the condition index and cannot dynamically adjust the parameter weights according to the actual operation state of the vehicle. For example, when the vehicle enters the emergency braking state, the prior art still assigns the comprehensive friction index according to the conventional weight, resulting in a lag in braking control decisions. However, this application can effectively improve the parameter fusion accuracy under complex conditions by introducing a dynamic weight adjustment mechanism based on the operation mode.

[0056] Through the above technical solutions, this application can accurately identify the dynamic condition characteristics of the vehicle under different operation modes. For example, in the dangerous mode, the calculation weight of the comprehensive friction index is strengthened, enabling the braking control system to perceive the change trend of the road surface adhesion in advance. This dynamic weighted fusion mechanism solves the problem of insufficient parameter fusion accuracy of traditional methods under emergency conditions and provides a more accurate condition judgment basis for subsequent braking force distribution.

[0057] In some embodiments, this application further proposes that the steps for generating the type of driving intention include: obtaining the steering wheel angle signal and the pedal travel signal in a preset time sliding window, and respectively calculating the change rates to obtain the steering trend feature and the emergency operation feature; comparing the steering trend feature with a preset steering threshold, and at the same time comparing the emergency operation feature with a preset emergency threshold to obtain a comparison result; matching according to the comparison result and a preset driving intention comparison table to determine the current driver's driving intention type, where the driving intention type includes a steady driving mode, a predictive operation mode, and an emergency operation mode.

[0058] Among them, the steering trend feature refers to the change rate of the steering wheel angle signal within a preset time sliding window. Specifically, it can be implemented by calculating the difference in the steering wheel angle at adjacent moments using a differential algorithm and then dividing by the time interval to obtain the angular change rate, which is used to characterize the persistence or mutation of the driver's steering intention.

[0059] Among them, the emergency operation feature refers to the change rate of the pedal travel signal within a preset time sliding window. Specifically, it can be implemented by calculating the acceleration change amount using the second derivative of the pedal travel within the sliding window, which is used to identify the urgency of the pedal operation.

[0060] Among them, the driving intention comparison table refers to a logical mapping table that includes combinations of thresholds for the steering trend feature and the emergency operation feature. Specifically, it can be stored in the form of a two-dimensional matrix to store the driving intention types corresponding to different threshold intervals. For example, when the steering trend exceeds the first threshold and the emergency operation is lower than the second threshold, it is mapped to a predictive operation mode.

[0061] Specifically, the steering wheel angle signal and the pedal travel signal are collected in real time through in-vehicle sensors. The preset time sliding window can refer to a certain time interval before the current moment. For example, the interval from 0.5 seconds to 2 seconds before the current moment can be selected. The calculation of the steering trend feature can use the first derivative of the steering wheel angle within the sliding window. When the absolute value of this derivative exceeds the preset steering threshold, it indicates the existence of an active steering intention. The emergency operation feature is calculated by the absolute value of the increment of the pedal travel signal per unit time. If it continuously exceeds the preset emergency threshold, it is determined that there is an emergency braking demand. By comparing the two with the preset thresholds and combining the logical mapping relationship of the driving intention comparison table, three typical scenarios of steady driving, predictive steering to avoid obstacles, or emergency braking can be accurately distinguished. For example, when the steering trend feature reaches the threshold while the emergency operation feature does not reach the threshold, the predictive operation mode is triggered to adjust the front wheel braking force distribution in advance.

[0062] Compared with the prior art, the existing solutions usually only judge the intention based on the steering wheel angle or pedal signal at the current moment and cannot capture the change of the operation trend. However, this solution extracts the signal change rate feature through a preset time sliding window, and can identify the evolution process of the driving intention in advance. For example, when the steering wheel angle starts to increase but does not reach the dangerous threshold, the steering trend has been detected through the change rate, so that the braking force pre-distribution is started before the actual steering action of the vehicle occurs.

[0063] Through the above technical solutions, this application can identify the dynamic changes of the driving intention earlier in the braking control process and avoid the lag of the braking force distribution caused by signal processing delay. For example, when it is detected that the steering trend feature exceeds the threshold, the system can increase the front wheel braking force distribution ratio in advance to prevent understeering; when the emergency operation feature continuously exceeds the threshold, the maximum pressure distribution mode is immediately triggered to shorten the emergency braking response time.

[0064] In some embodiments, the present application further proposes to correct the brake oil volume of each wheel cylinder in real time according to the obtained feedback signal, including calculating the pressure deviation value of each wheel cylinder according to the obtained feedback signal, performing temperature compensation processing on the oil temperature of each wheel cylinder according to the ambient temperature to generate a viscosity correction coefficient, and importing the pressure deviation value and the viscosity correction coefficient into a preset calculation model; adjusting the valve opening of each wheel cylinder according to the valve opening adjustment index to correct the brake oil volume of each wheel cylinder in real time.

[0065] Among them, the pressure deviation value refers to the difference between the real-time pressure of the wheel cylinder and the target brake pressure. Specifically, it can be achieved by measuring the real-time pressure with a pressure sensor and then subtracting it from the target pressure. The target pressure is determined according to the target calculated value corresponding to the braking force demand level. Temperature compensation processing refers to predicting the change in oil viscosity by measuring the ambient temperature. Specifically, it can be achieved by using a temperature sensor to collect the ambient temperature and then using a preset viscosity-temperature correspondence table or compensation algorithm. The viscosity correction coefficient is a quantitative parameter used to correct the fluidity difference caused by temperature changes in the oil. Specifically, it can be generated by a table lookup method or a polynomial fitting model. The valve opening adjustment index is an indicator used to control the degree of opening of the solenoid valve. Specifically, it can be calculated and generated by a proportional-integral-differential control algorithm combined with the pressure deviation and the viscosity correction coefficient.

[0066] Specifically, in the process of correcting the amount of brake fluid, the real-time pressure signal is first collected through the wheel cylinder pressure sensor, and the difference between it and the target brake pressure is calculated to generate a pressure deviation value. At the same time, the ambient temperature data is obtained through the temperature sensor, and the viscosity correction coefficient is generated in combination with the preset temperature compensation algorithm. The pressure deviation value and the viscosity correction coefficient are input into the calculation model, which dynamically generates the valve opening adjustment instructions corresponding to each wheel cylinder based on the oil flow characteristics and valve control rules. After the adjustment instruction is transmitted to the solenoid valve actuator, the oil flow entering the wheel cylinder is accurately adjusted by changing the valve opening, thereby realizing closed-loop control of the brake pressure.

[0067] Compared with the existing technology, the traditional brake control method usually only controls the valve based on the pressure deviation, without considering the change of flow characteristics caused by the change of oil viscosity with temperature, which is prone to regulation lag or overshoot in extreme temperature environment. This solution introduces a temperature compensation mechanism to synchronously correct the oil viscosity parameters when calculating the valve opening, so that the control model can adapt to the changes in ambient temperature and improve the accuracy and stability of pressure regulation.

[0068] Through the above technical solution, this application effectively solves the problem of insufficient control precision of traditional brake oil. Through the coordinated control of pressure deviation value and viscosity correction coefficient, the oil flow can be accurately controlled under different ambient temperature conditions, ensuring that the wheel cylinder pressure quickly converges to the target value, and improving the response speed and control precision of the brake system.

[0069] In some embodiments, the present application further provides a method for calculating the pressure deviation value of each wheel cylinder according to the acquired feedback signal, including acquiring the feedback signal of each wheel cylinder; determining the real-time pressure of each wheel cylinder according to the feedback signal; calculating the difference between each real-time pressure and the target braking pressure, where the target braking pressure is the target calculated value corresponding to the braking force demand level.

[0070] Wherein, the feedback signal refers to the hydraulic parameters collected by the sensors arranged in the wheel cylinder, and specifically, the internal pressure change of the wheel cylinder can be monitored in real time by using a pressure sensor to reflect the actual braking execution state.

[0071] Wherein, the real-time pressure refers to the current oil pressure value of the wheel cylinder obtained by the sensor, and specifically, the output signal of the sensor can be collected by using a periodic sampling method and converted into a numerical quantity after analog-to-digital conversion processing to quantify the current actual braking force.

[0072] Wherein, the target braking pressure refers to the expected pressure value preset according to the braking force demand level. Specifically, the numerical range can be determined by querying the preset pressure level mapping table, and the target value can be dynamically adjusted in combination with the current vehicle operation parameters to provide a reference for the pressure deviation calculation.

[0073] Wherein, the difference refers to the arithmetic difference between the real-time pressure and the target pressure, and specifically, the subtraction operation can be used to perform real-time comparison and calculation on the two numerical values to quantify the current pressure adjustment demand.

[0074] Specifically, during the braking control process, the pressure sensor built in the wheel cylinder continuously collects the oil pressure data and transmits it to the control unit. The control unit filters the data and generates the real-time pressure value of each wheel cylinder. At the same time, according to the current braking force demand level, the corresponding target pressure value is retrieved from the preset mapping table. The difference between the real-time pressure and the target pressure is obtained through subtraction operation, and this difference will be used as the basis for subsequent oil volume adjustment. For example, when the target pressure is 10 MPa and the real-time pressure is 8 MPa, a positive deviation value of 2 MPa is calculated, triggering a command to increase the valve opening to supplement the oil volume.

[0075] Compared with the prior art, the traditional method usually relies on preset fixed pressure thresholds for adjustment, without considering the influence of the dynamic change of the braking force demand level on the target pressure, resulting in inaccurate deviation calculation benchmarks. However, this solution enables the deviation calculation to reflect the actual working condition requirements in real time by dynamically matching the braking force demand level with the target pressure, thereby improving the response accuracy of pressure adjustment.

[0076] Through the above technical solution, the present application can accurately quantify the difference between the actual pressure and the target pressure of each wheel cylinder, providing an accurate adjustment basis for subsequent oil volume compensation, and avoiding problems such as a decrease in braking performance or imbalance in braking force distribution caused by the accumulation of pressure deviation.

[0077] In some embodiments, the present application further proposes that when it is detected that the signal transmitted by the sensor is abnormal, the transmission signal of the sensor with abnormal signal is interrupted; a redundant data source corresponding to the sensor with abnormal signal is obtained, where the redundant data source is obtained by calculating the signals transmitted by other sensors.

[0078] Among them, an abnormal signal means that the signal value output by the sensor exceeds a preset reasonable range or does not conform to the expected change rule, and it can be specifically realized by threshold comparison or time-domain feature analysis. For example, when the output value of the acceleration sensor exceeds 3 times the gravitational acceleration, it is determined to be abnormal. This feature is used to avoid interference of incorrect signals on braking control.

[0079] The redundant data source refers to alternative data generated by calculating the original data of other sensors through an algorithm, and it can be specifically realized by data fusion or a state observer. For example, when the wheel speed sensor fails, the equivalent wheel speed data is estimated by using the steering angle signal and the yaw rate signal through a Kalman filter. This feature is used to maintain the continuous operation ability of the braking control system when the sensor fails.

[0080] Specifically, when the vehicle-mounted electronic control unit discovers that the signal transmitted by a certain sensor is abnormal through periodic self-check, it immediately cuts off the signal input channel of the sensor to prevent incorrect data from flowing into the subsequent processing link. Subsequently, the system starts a preset redundant calculation module, retrieves the data of other sensors associated with the function of the sensor, and generates an equivalent measurement value through a preset compensation algorithm. For example, in the case of the failure of the brake pressure sensor, the alternative pressure estimate value can be generated by inversely calculating the wheel cylinder valve opening signal and the oil pump speed signal for braking force distribution control. Thus, the braking control system can maintain the basic function when a key sensor suddenly fails, and avoid the overall control interruption caused by the failure of a single sensor.

[0081] Compared with the prior art, traditional braking control methods usually only rely on a single sensor to collect key parameters. When the sensor has an occasional failure, the system cannot identify abnormal data in time or switch to an alternative signal source, which is likely to cause incorrect braking pressure distribution or response delay. However, this solution can complete the signal source switching within hundreds of milliseconds after the sensor fails by introducing a dynamic redundant signal generation mechanism, ensuring the continuity and stability of the braking control loop.

[0082] Through the above technical solutions, the present application can quickly enable a backup data source when on-vehicle sensors suddenly malfunction, effectively avoid the problem of misadjustment of braking pressure caused by signal distortion, improve the fault tolerance and safety redundancy of the vehicle braking system under complex working conditions, and at the same time reduce the configuration requirements for hardware redundant sensors.

[0083] In some embodiments, the present application further proposes to cache the vehicle operation parameters within a preset time window; when it is detected that the duration of data transmission interruption exceeds the limit, calculations are performed based on the cached vehicle operation parameters.

[0084] Among them, the preset time window refers to a preset time range for storing vehicle operation parameters, which can be specifically implemented by a circular buffer. By continuously recording the latest parameters in a cyclic manner to overwrite old data, valid information can be retained within a limited storage space.

[0085] Among them, the situation where the duration of data transmission interruption exceeds the limit means that the duration of signal loss caused by the failure of the sensor or communication link exceeds a preset threshold. Specifically, a timer can be used to monitor the signal interruption duration, and when the threshold is exceeded, an emergency handling mechanism is triggered to ensure that the braking control logic can still maintain operation under abnormal conditions.

[0086] Specifically, during the operation of the vehicle, the vehicle operation parameters collected in real time are continuously stored in the buffer corresponding to the preset time window. When the system detects a data transmission interruption and the interruption duration exceeds the set threshold, it automatically switches to the cache mode and calls the vehicle operation parameters stored in the buffer to perform subsequent calculations, such as key steps like generating a dynamic working condition index and determining the braking force demand level. In this way, during the abnormal communication link period, the system can still maintain the braking control function based on historical data, avoiding control failure caused by signal loss.

[0087] Compared with the prior art, traditional braking control methods usually rely on the vehicle operation parameters transmitted in real time. When the data transmission is interrupted, no effective information can be obtained, resulting in the interruption of the control logic or the operation with default parameters, which may lead to a decline in braking performance or potential safety hazards. However, this solution retains historical data through a caching mechanism and can still perform dynamic adjustment based on valid information under abnormal conditions, improving the fault tolerance and operation continuity of the system.

[0088] Through the above technical solutions, the present application can maintain the braking control function when the communication link is abnormal, avoid control failure or delay caused by signal interruption, and ensure the braking stability of the vehicle under complex working conditions. At the same time, the caching mechanism can reduce the complete dependence on real-time data and improve the robustness of the system in scenarios of signal fluctuations or short-term interruptions.

[0089] The following combination of annex Figure 2, showing a process flowchart of a vehicle braking control method provided by some other embodiments of this specification, specifically including the following steps.

[0090] Step 201: Obtain the vehicle operation parameters of the running vehicle and perform weighted fusion processing to generate a dynamic working condition index; Step 202: Obtain the steering wheel angle signal and the pedal travel signal in the preset time sliding window, and calculate the change rates respectively to obtain the steering trend feature and the emergency operation feature; Step 203: Compare the steering trend feature with a preset steering threshold, and at the same time compare the emergency operation feature with a preset emergency threshold to obtain a comparison result; Step 204: Match according to the comparison result and a preset driving intention comparison table to determine the current driver's driving intention type, where the driving intention type includes a smooth driving mode, a predictive operation mode, and an emergency operation mode.

[0091] Step 205: Determine the braking force demand level of each wheel cylinder according to the dynamic working condition index and the driving intention type; Step 206: Adjust the brake fluid volume of each wheel cylinder based on the braking force demand level to adjust the braking condition of the vehicle.

[0092] Step 207: Calculate the pressure deviation value of each wheel cylinder according to the obtained feedback signal; Step 208: Perform temperature compensation processing on the oil temperature of each wheel cylinder according to the ambient temperature to generate a viscosity correction coefficient; Step 209: Import the pressure deviation value and the viscosity correction coefficient into a preset calculation model to generate a valve opening adjustment index for each wheel cylinder.

[0093] Step 210: Adjust the valve opening of each wheel cylinder according to the valve opening adjustment index to real-time correct the brake fluid volume of each wheel cylinder.

[0094] In some embodiments, the specific implementation and the technical effects brought by the corresponding steps of steps 201-210 in Figure 1 the corresponding embodiments can refer to Figure 1 those steps in, and will not be elaborated here.

[0095] Corresponding to the above method embodiments, this specification also provides embodiments of a vehicle braking control device, Figure 3 showing a structural schematic diagram of a vehicle braking control device provided by some embodiments of this specification. As Figure 3 shown, the device includes: The first generation module 301, configured to obtain the vehicle operation parameters of the running vehicle and perform weighted fusion processing to generate a dynamic working condition index; The second generation module 302 is configured to obtain the real-time driving signals of the current driver within a preset time sliding window and import them into a preset driving intention prediction model to generate a driving intention type, where the real-time driving signals include a steering wheel angle signal and a pedal travel signal; The determination module 303 is configured to determine the braking force demand levels of each wheel cylinder according to the dynamic working condition index and the driving intention type, where the braking force demand levels include a low demand level indicating maintaining the current braking, a medium demand level indicating increasing the front wheel distribution ratio, and a high demand level indicating distributing the maximum pressure; The adjustment module 304 is configured to adjust the braking fluid volume of each wheel cylinder based on the braking force demand level to adjust the braking condition of the vehicle, and to correct the braking fluid volume of each wheel cylinder in real time according to the obtained feedback signal.

[0096] In some embodiments, obtaining the vehicle operation parameters of the running vehicle and performing weighted fusion processing to generate a dynamic working condition index includes: Obtaining the vehicle operation parameters of the running vehicle, where the vehicle operation parameters include a vehicle speed signal, a load signal, a road surface friction signal, a coaxial wheel speed difference data, and a road surface texture recognition data; Performing noise filtering processing on the vehicle speed signal to obtain a smoothed vehicle speed; Performing normalization processing on the load signal to generate a dynamic load; Performing feature extraction on the road surface friction signal, combining the coaxial wheel speed difference data and the road surface texture recognition data, and generating a comprehensive friction index through fuzzy logic; Performing weighted fusion processing on the smoothed vehicle speed, the dynamic load parameter, and the comprehensive friction index based on the current weighted index, where the weighted index is dynamically adjusted according to the vehicle operation mode, and the vehicle operation mode includes a stable mode, a critical model, and a dangerous mode.

[0097] In some embodiments, the steps of generating a driving intention type include: Obtaining the steering wheel angle signal and the pedal travel signal within the preset time sliding window, and respectively calculating the change rates to obtain a steering trend feature and an emergency operation feature; Comparing the steering trend feature with a preset steering threshold, and at the same time comparing the emergency operation feature with a preset emergency threshold to obtain a comparison result; Matching according to the comparison result and a preset driving intention comparison table to determine the current driver's driving intention type, where the driving intention type includes a smooth driving mode, a predictive operation mode, and an emergency operation mode.

[0098] In some embodiments, correcting the braking fluid volume of each wheel cylinder in real time according to the obtained feedback signal includes: Calculating the pressure deviation value of each wheel cylinder according to the obtained feedback signal; Perform temperature compensation processing on the hydraulic oil temperature of each wheel cylinder according to the ambient temperature to generate a viscosity correction coefficient; Import the pressure deviation value and the viscosity correction coefficient into a preset calculation model to generate a valve opening adjustment index for each wheel cylinder; Adjust the valve opening of each wheel cylinder according to the valve opening adjustment index to real-time correct the brake hydraulic oil volume of each wheel cylinder.

[0099] In some embodiments, calculating the pressure deviation value of each wheel cylinder according to the acquired feedback signal includes: Acquire the feedback signal of each wheel cylinder; Determine the real-time pressure of each wheel cylinder according to the feedback signal; Calculate the difference between each real-time pressure and the target braking pressure, where the target braking pressure is the target calculated value corresponding to the braking force demand level.

[0100] In some embodiments, the above device further includes a first exception handling module, configured to: when detecting an abnormal signal transmitted by a sensor, interrupt the transmission signal of the sensor with abnormal signal acquisition; acquire a redundant data source corresponding to the sensor with abnormal signal, where the redundant data source is obtained by calculating the signals transmitted by other sensors.

[0101] In some embodiments, the above device further includes a second exception handling module, configured to: cache the vehicle operation parameters within a preset time window; when detecting that the data transmission interruption duration exceeds the limit, perform calculations based on the cached vehicle operation parameters.

[0102] The above is a schematic solution of a vehicle braking control device according to this embodiment. It should be noted that the technical solution of this vehicle braking control device and the technical solution of the above vehicle braking control method belong to the same concept. For the details not described in the technical solution of the vehicle braking control device, reference can be made to the description of the technical solution of the above vehicle braking control method.

[0103] In some embodiments, the embodiments of the present invention further provide a vehicle, characterized in that the vehicle is provided with a control center and a hydraulic braking module AHB, and the hydraulic braking module AHB is provided with a brake controller unit ECU, a supercharging unit PSU, and a hydraulic control unit PCU, where The control center is used to execute the steps of the vehicle braking control method in the foregoing claims; The brake controller unit ECU is respectively connected to the supercharging unit PSU and the hydraulic control unit PCU, and is used to receive the control instructions sent by the superior control center, and generate a first instruction and a second instruction according to the control instructions to control the supercharging unit PSU and the hydraulic control unit PCU to perform pressure control, where the first instruction is sent to the supercharging unit PSU, and the second instruction is sent to the supercharging unit PSU; The pressurization unit PSU is used to output hydraulic oil with a certain pressure to the hydraulic control unit PCU according to the received first instruction; The hydraulic control unit PCU is used to adjust, control, and distribute the output pressure according to the received second instruction to ensure different working pressures under different working conditions.

[0104] Figure 4 The structural block diagram of a computing device 400 provided according to some embodiments of the present specification is shown. The components of the computing device 400 include but are not limited to a memory 401 and a processor 402. The processor 402 is connected to the memory 401 through a bus 403, and a database 405 is used to store data.

[0105] The computing device 400 further includes an access device 404, and the access device 404 enables the computing device 400 to communicate via one or more networks 406. Examples of these networks include the Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 404 may include one or more of any type of wired or wireless network interface (for example, a network interface card (NIC)), such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC).

[0106] In an embodiment of the present specification, the above components of the computing device 400 and Figure 4 other components not shown may also be connected to each other, for example, through a bus. It should be understood that Figure 4 the shown structural block diagram of the computing device is only for illustrative purposes and is not a limitation on the scope of the present specification. Those skilled in the art can add or replace other components as needed.

[0107] The computing device 400 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.) or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 400 can also be a mobile or stationary server.

[0108] Wherein, the processor 402 is configured to execute the following computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the above vehicle braking control method are implemented. The above is a schematic solution of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above vehicle braking control method belong to the same concept. For the details not described in detail in the technical solution of the computing device, reference can be made to the description of the technical solution of the above vehicle braking control method.

[0109] An embodiment of this specification also provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the above vehicle braking control method are implemented.

[0110] The above is a schematic solution of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the above vehicle braking control method belong to the same concept. For the details not described in detail in the technical solution of the storage medium, reference can be made to the description of the technical solution of the above vehicle braking control method.

[0111] An embodiment of this specification also provides a computer program, wherein when the computer program is executed on a computer, the computer is made to execute the steps of the above vehicle braking control method.

[0112] The above is a schematic solution of a computer program according to this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above vehicle braking control method belong to the same concept. For the details not described in detail in the technical solution of the computer program, reference can be made to the description of the technical solution of the above vehicle braking control method.

[0113] The above description has been made of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the accompanying drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0114] Computer instructions include computer program code, which may be in source code form, object code form, executable file, or some intermediate form, etc. A computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, removable hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0115] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of this specification are not limited by the described order of actions, because according to the embodiments of this specification, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this specification.

[0116] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The alternative embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can well understand and utilize this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A vehicle braking control method, characterized in that, Including: Obtain the vehicle operation parameters of the running vehicle and perform weighted fusion processing to generate a dynamic working condition index; Obtain the real-time driving signals of the current driver within a preset time sliding window and import them into a preset driving intention prediction model to generate a driving intention type, where the real-time driving signals include a steering wheel angle signal and a pedal travel signal; Determine the braking force demand levels of each wheel cylinder according to the dynamic working condition index and the driving intention type, where the braking force demand levels include a low demand level representing maintaining the current braking, a medium demand level representing increasing the front wheel distribution ratio, and a high demand level representing distributing the maximum pressure; Adjust the brake fluid volume of each wheel cylinder based on the braking force demand level to adjust the braking condition of the vehicle, and real-time correct the brake fluid volume of each wheel cylinder according to the obtained feedback signal.

2. The method according to claim 1, wherein The step of obtaining the vehicle operation parameters of the running vehicle and performing weighted fusion processing to generate a dynamic working condition index includes: Obtain the vehicle operation parameters of the running vehicle, where the vehicle operation parameters include a vehicle speed signal, a load signal, a road surface friction signal, a coaxial wheel speed difference data, and a road surface texture recognition data; Perform noise filtering processing on the vehicle speed signal to obtain a smooth vehicle speed; Perform normalization processing on the load signal to generate a dynamic load; Extract features from the road surface friction signal, combine the coaxial wheel speed difference data and the road surface texture recognition data, and generate a comprehensive friction index through fuzzy logic; Perform weighted fusion processing on the smooth vehicle speed, the dynamic load parameter, and the comprehensive friction index based on the current weighting index, and the weighting index is dynamically adjusted according to the vehicle operation mode, where the vehicle operation mode includes a stable mode, a critical model, and a dangerous mode.

3. The method according to claim 1, characterized in that, The steps of generating a driving intention type include: Obtain the steering wheel angle signal and the pedal travel signal in the preset time sliding window, and calculate the change rates respectively to obtain a steering trend feature and an emergency operation feature; Compare the steering trend feature with a preset steering threshold, and at the same time compare the emergency operation feature with a preset emergency threshold to obtain a comparison result; Match according to the comparison result and a preset driving intention comparison table to determine the current driver's driving intention type, where the driving intention type includes a smooth driving mode, a predictive operation mode, and an emergency operation mode.

4. The method according to claim 1, wherein The step of real-time correcting the brake fluid volume of each wheel cylinder according to the obtained feedback signal includes: Calculate the pressure deviation value of each wheel cylinder according to the obtained feedback signal; Perform temperature compensation processing on the oil temperature of each wheel cylinder according to the ambient temperature to generate a viscosity correction coefficient; Import the pressure deviation value and the viscosity correction coefficient into a preset calculation model to generate a valve opening adjustment index for each wheel cylinder; Adjust the valve opening of each wheel cylinder according to the valve opening adjustment index to real-time correct the brake fluid volume of each wheel cylinder.

5. The method according to claim 4, wherein Calculating the pressure deviation value of each wheel cylinder according to the obtained feedback signal includes: Obtain the feedback signal of each wheel cylinder; Determine the real-time pressure of each wheel cylinder according to the feedback signal; Calculate the difference between each real-time pressure and the target braking pressure, where the target braking pressure is the target calculated value corresponding to the braking force demand level.

6. The method according to claim 1, characterized in that, It further includes: When it is detected that the signal transmitted by the sensor is abnormal, interrupt the transmission signal of the sensor with abnormal signal acquisition; Obtain a redundant data source corresponding to the sensor with abnormal signal, where the redundant data source is obtained by calculating the signals transmitted by other sensors.

7. The method according to claim 1, wherein It further includes: Cache the vehicle operation parameters within a preset time window; When it is detected that the duration of data transmission interruption exceeds the limit, perform calculations based on the cached vehicle operation parameters.

8. A vehicle braking control device, characterized in that, It includes: A first generation module, configured to obtain the vehicle operation parameters of the running vehicle and perform weighted fusion processing to generate a dynamic working condition index; A second generation module, configured to obtain the real-time driving signals of the current driver within a preset time sliding window and import them into a preset driving intention prediction model to generate a driving intention type, where the real-time driving signals include a steering wheel angle signal and a pedal stroke signal; A determination module, configured to determine the braking force demand levels of each wheel cylinder according to the dynamic working condition index and the driving intention type, where the braking force demand levels include a low demand level representing maintaining the current braking, a medium demand level representing increasing the front-wheel distribution ratio, and a high demand level representing distributing the maximum pressure; An adjustment module, configured to adjust the brake fluid volume of each wheel cylinder based on the braking force demand level to adjust the braking condition of the vehicle, and to correct the brake fluid volume of each wheel cylinder in real time according to the acquired feedback signal.

9. A vehicle, characterized in that, The vehicle is provided with a control center and a hydraulic braking module AHB, and the hydraulic braking module AHB is provided with a brake controller unit ECU, a booster unit PSU, and a hydraulic control unit PCU, where The control center is used to execute the steps of the vehicle braking control method according to any one of claims 1 to 7; The brake controller unit ECU is respectively connected to the booster unit PSU and the hydraulic control unit PCU, and is used to receive the control instructions sent by the superior control center, and generate a first instruction and a second instruction according to the control instructions to control the booster unit PSU and the hydraulic control unit PCU to perform pressure control, where the first instruction is sent to the booster unit PSU, and the second instruction is sent to the booster unit PSU; The booster unit PSU is used to output hydraulic oil with a certain pressure to the hydraulic control unit PCU according to the received first instruction; The hydraulic control unit PCU is used to adjust, control, and distribute the output pressure according to the received second instruction to ensure different working pressures under different working conditions.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed by a processor, the steps of the vehicle braking control method according to any one of claims 1 to 7 are implemented.

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