Vehicle braking control method and device

Abstract

This specification provides a vehicle braking control method and apparatus. The vehicle braking control method includes: acquiring vehicle operating parameters of a running vehicle and performing weighted fusion processing to generate a dynamic operating condition index; acquiring the real-time driving signal of the current driver at a preset time window and importing it into a preset driving intention prediction model to generate a driving intention type; determining the braking force demand level of each wheel cylinder based on the dynamic operating condition index and the driving intention type; adjusting the brake fluid volume of each wheel cylinder based on the braking force demand level to adjust the vehicle's braking condition, and correcting the brake fluid volume of each wheel cylinder in real time based on the acquired feedback signal. This application can achieve precise matching between braking pressure distribution and vehicle dynamic characteristics, effectively balance the front and rear axle braking torque distribution under cornering braking conditions, shorten the response time from recognizing driving intention to executing pressure adjustment, and improve the coordinated control capability of the braking system in sudden avoidance scenarios.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of vehicle braking control technology, and particularly to vehicle braking control methods and devices. Background Technology

[0002] A vehicle braking system is a series of specialized devices on a car used to apply a certain force to certain parts of the vehicle, thereby forcibly braking it to a certain extent. The braking system can force a moving car to decelerate or even stop as required by the driver; it can keep a stopped car stable under various road conditions; and it can maintain a stable speed for a car traveling downhill. With scientific advancements, intelligent technologies are also being applied to vehicle braking systems.

[0003] Currently, intelligent control of vehicle braking methods often relies on a single signal for judgment, resulting in low judgment accuracy and poor practicality. Summary of the Invention

[0004] In view of this, 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 address technical deficiencies in the prior art.

[0005] According to a first aspect of the embodiments of this specification, a vehicle braking control method is provided, comprising:

[0006] The vehicle operating parameters of the vehicle in operation are obtained and weighted and fused to generate a dynamic operating condition index;

[0007] The system acquires the real-time driving signals of the current driver during a preset time window and imports them into a preset driving intention prediction model to generate driving intention types. The real-time driving signals include steering wheel angle signals and pedal travel signals.

[0008] Based on the dynamic operating condition index and driving intention type, the braking force demand level of each wheel cylinder is determined. The braking force demand level includes a low demand level that represents maintaining the current braking, a medium demand level that represents increasing the front wheel distribution ratio, and a high demand level that represents distributing the maximum pressure.

[0009] The brake fluid volume of each wheel cylinder is adjusted based on the braking force demand level to regulate the vehicle's braking performance, and the brake fluid volume of each wheel cylinder is corrected in real time based on the acquired feedback signals.

[0010] In some embodiments, vehicle operating parameters of a vehicle in operation are acquired and weighted fusion processing is performed to generate a dynamic operating condition index, including:

[0011] Acquire vehicle operating parameters of the vehicle in operation, including vehicle speed signal, load signal, road friction signal, co-axle wheel speed difference data and road texture recognition data;

[0012] The vehicle speed signal is processed by noise filtering to obtain a smooth vehicle speed;

[0013] The load signal is normalized to generate a dynamic load;

[0014] Feature extraction is performed on road friction signals, and combined with coaxial wheel speed difference data and road texture recognition data, a comprehensive friction index is generated through fuzzy logic.

[0015] The smooth vehicle speed, dynamic load parameters, and comprehensive friction index are weighted and fused based on the current weighted indicators. The weighted indicators are dynamically adjusted according to the vehicle operation mode, which includes stable mode, critical mode, and dangerous mode.

[0016] In some embodiments, the step of generating a driving intent type includes:

[0017] The steering wheel angle signal and pedal travel signal are obtained from the preset time sliding window, and the rate of change is calculated to obtain the steering trend characteristics and emergency operation characteristics respectively;

[0018] The turning trend characteristics are compared with a preset turning threshold, and the emergency operation characteristics are compared with a preset emergency threshold to obtain the comparison results.

[0019] The current driver's driving intention type is determined by matching the comparison results with a preset driving intention comparison table. The driving intention type includes smooth driving mode, predictive operation mode, and emergency operation mode.

[0020] In some embodiments, the brake fluid volume of each wheel cylinder is adjusted in real time based on the acquired feedback signal, including:

[0021] The pressure deviation value of each wheel cylinder is calculated based on the obtained feedback signal;

[0022] Temperature compensation processing is performed on the oil temperature of each wheel cylinder based on the ambient temperature to generate a viscosity correction coefficient;

[0023] The pressure deviation value and viscosity correction coefficient are imported into the preset calculation model to generate the valve opening adjustment index for each cylinder.

[0024] The valve opening of each wheel cylinder is adjusted according to the valve opening adjustment index to correct the brake fluid volume of each wheel cylinder in real time.

[0025] In some embodiments, calculating the pressure deviation value of each wheel cylinder based on the acquired feedback signal includes:

[0026] Obtain feedback signals from each wheel cylinder;

[0027] The real-time pressure of each wheel cylinder is determined based on the feedback signal;

[0028] 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.

[0029] In some embodiments, the above method further includes:

[0030] When an abnormal signal is detected in the sensor, the acquisition of the sensor's transmitted signal is interrupted.

[0031] Obtain redundant data sources corresponding to sensors with abnormal signals. The redundant data sources are calculated from signals transmitted by other sensors.

[0032] In some embodiments, the above method further includes:

[0033] Cache vehicle operating parameters within a preset time window;

[0034] When the data transmission interruption duration exceeds the limit, calculations are performed based on the cached vehicle operating parameters.

[0035] According to a second aspect of the embodiments of this specification, a vehicle braking control device is provided, comprising:

[0036] The first generation module is configured to acquire the vehicle operating parameters of the running vehicle and perform weighted fusion processing to generate a dynamic operating condition index.

[0037] The second generation module is configured to acquire the real-time driving signals of the current driver in the preset time window and import them into the preset driving intention prediction model to generate driving intention types. The real-time driving signals include steering wheel angle signals and pedal travel signals.

[0038] The determination module is configured to determine the braking force demand level of each wheel cylinder based on the dynamic operating condition index and driving intention type. The braking force demand level includes a low demand level that represents maintaining the current braking, a medium demand level that represents increasing the front wheel distribution ratio, and a high demand level that represents distributing the maximum pressure.

[0039] The adjustment module is configured to adjust the amount of brake fluid in each wheel cylinder based on the braking force demand level in order to adjust the vehicle's braking condition, and to correct the amount of brake fluid in each wheel cylinder in real time based on the acquired feedback signal.

[0040] In some embodiments, vehicle operating parameters of a vehicle in operation are acquired and weighted fusion processing is performed to generate a dynamic operating condition index, including:

[0041] Acquire vehicle operating parameters of the vehicle in operation, including vehicle speed signal, load signal, road friction signal, co-axle wheel speed difference data and road texture recognition data;

[0042] The vehicle speed signal is processed by noise filtering to obtain a smooth vehicle speed;

[0043] The load signal is normalized to generate a dynamic load;

[0044] Feature extraction is performed on road friction signals, and combined with coaxial wheel speed difference data and road texture recognition data, a comprehensive friction index is generated through fuzzy logic.

[0045] The smooth vehicle speed, dynamic load parameters, and comprehensive friction index are weighted and fused based on the current weighted indicators. The weighted indicators are dynamically adjusted according to the vehicle operation mode, which includes stable mode, critical mode, and dangerous mode.

[0046] In some embodiments, the step of generating a driving intent type includes:

[0047] The steering wheel angle signal and pedal travel signal are obtained from the preset time sliding window, and the rate of change is calculated to obtain the steering trend characteristics and emergency operation characteristics respectively;

[0048] The turning trend characteristics are compared with a preset turning threshold, and the emergency operation characteristics are compared with a preset emergency threshold to obtain the comparison results.

[0049] The current driver's driving intention type is determined by matching the comparison results with a preset driving intention comparison table. The driving intention type includes smooth driving mode, predictive operation mode, and emergency operation mode.

[0050] In some embodiments, the brake fluid volume of each wheel cylinder is adjusted in real time based on the acquired feedback signal, including:

[0051] The pressure deviation value of each wheel cylinder is calculated based on the obtained feedback signal;

[0052] Temperature compensation processing is performed on the oil temperature of each wheel cylinder based on the ambient temperature to generate a viscosity correction coefficient;

[0053] The pressure deviation value and viscosity correction coefficient are imported into the preset calculation model to generate the valve opening adjustment index for each cylinder.

[0054] The valve opening of each wheel cylinder is adjusted according to the valve opening adjustment index to correct the brake fluid volume of each wheel cylinder in real time.

[0055] In some embodiments, calculating the pressure deviation value of each wheel cylinder based on the acquired feedback signal includes:

[0056] Obtain feedback signals from each wheel cylinder;

[0057] The real-time pressure of each wheel cylinder is determined based on the feedback signal;

[0058] 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.

[0059] In some embodiments, the above-described apparatus further includes a first anomaly handling module, configured to: when an anomaly is detected in the signal transmitted by a sensor, interrupt the acquisition of the transmission signal of the sensor with the anomaly; acquire a redundant data source corresponding to the sensor with the anomaly, wherein the redundant data source is calculated from the signals transmitted by other sensors.

[0060] In some embodiments, the above-described apparatus further includes a second exception handling module, configured to: cache vehicle operating parameters within a preset time window; and perform calculations based on the cached vehicle operating parameters when a data transmission interruption duration is detected to exceed the limit.

[0061] According to a fourth aspect of the embodiments of this specification, a vehicle is provided, characterized in that the vehicle is provided with a control center and a hydraulic braking module AHB, the hydraulic braking module AHB including a brake controller unit ECU, a booster unit PSU, and a hydraulic control unit PCU, wherein...

[0062] The control center is used to execute the steps of the vehicle braking control method as claimed in the preceding claims;

[0063] The brake controller unit (ECU) is connected to the booster unit (PSU) and the hydraulic control unit (PCU) respectively. It is used to receive control commands sent by the upper-level control center and generate a first command and a second command according to the control commands to control the booster unit (PSU) and the hydraulic control unit (PCU) to perform pressure control. The first command is sent to the booster unit (PSU) and the second command is sent to the booster unit (PSU).

[0064] The booster unit (PSU) is used to output hydraulic oil at a certain pressure to the hydraulic control unit (PCU) according to the first received instruction.

[0065] The hydraulic control unit (PCU) is used to adjust, control, and distribute the output pressure according to the received second command, so as to ensure that different working pressures are achieved under different working conditions.

[0066] According to a fourth aspect of the embodiments of this specification, a computing device is provided, comprising:

[0067] Memory and processor;

[0068] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the above-described vehicle braking control method.

[0069] According to a fifth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the vehicle braking control method described above.

[0070] According to a sixth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described vehicle braking control method.

[0071] At least one embodiment in this specification obtains vehicle operating parameters of a running vehicle and performs weighted fusion processing to generate a dynamic operating condition index; obtains the real-time driving signal of the current driver at a preset time window and imports it into a preset driving intention prediction model to generate a driving intention type, wherein the real-time driving signal includes steering wheel angle signal and pedal travel signal; determines the braking force demand level of each wheel cylinder based on the dynamic operating condition index and driving intention type, wherein 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 the maximum distribution pressure; adjusts the brake fluid volume of each wheel cylinder based on the braking force demand level to adjust the vehicle's braking situation, and corrects the brake fluid volume of each wheel cylinder in real time based on the obtained feedback signal. This application can achieve precise matching between braking pressure distribution and vehicle dynamic characteristics, effectively balancing the front and rear axle braking torque distribution under cornering braking conditions. During emergency braking on low-friction surfaces, premature wheel lock-up is avoided by dynamically adjusting the pressure increase gradient. It also shortens the response time from recognizing driving intentions to executing pressure adjustments, and improves the coordinated control capability of the braking system in sudden avoidance scenarios. Attached Figure Description

[0072] Figure 1 This is a flowchart of some embodiments of a vehicle braking control method provided in this specification;

[0073] Figure 2 These are flowcharts of other embodiments of a vehicle braking control method provided in some embodiments of this specification;

[0074] Figure 3 This is a simplified structural diagram of a vehicle braking control device provided in some embodiments of this specification;

[0075] Figure 4 This is a structural block diagram of a computing device provided in some embodiments of this specification. Detailed Implementation

[0076] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0077] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the one or more embodiments of this specification. The singular forms “a” and “the” as 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” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items. The modifications “a” and “a plurality” as used in this disclosure are illustrative and not restrictive, and those skilled in the art will understand that they should be understood as “one or more” unless the context clearly indicates otherwise.

[0078] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0079] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0080] AHB: Accumulator-based Electro-hydraulic Braking Module;

[0081] ECU: Electronic Control Unit, brake control unit;

[0082] PSU: Pressure Supply Unit;

[0083] PCU: Pressure Control Unit, hydraulic control unit.

[0084] In existing technologies, traditional vehicle braking adjustment methods typically use fixed thresholds or single parameters for braking force distribution, making it difficult to adapt to complex and changing driving conditions. Especially in scenarios such as emergency lane changes and slippery roads, existing systems cannot effectively coordinate the relationship between the vehicle's dynamic load and the driver's operating intentions, easily leading to uneven braking pressure distribution and affecting vehicle stability. For example, when a vehicle brakes on a curve, traditional methods may ignore the impact of steering trends on wheel cylinder pressure distribution, resulting in a mismatch between braking torque distribution and the vehicle's actual motion state.

[0085] To address these issues, researchers noted that existing braking systems suffer from dynamic response lag and insufficient adaptability to different operating conditions. In-depth analysis revealed that relying solely on wheel speed differences or pedal signals is insufficient to accurately reflect the vehicle's actual braking needs. By studying vehicle dynamics models, they discovered that the coupling relationship between integrated dynamic load and road friction coefficient can more accurately characterize braking conditions. Furthermore, they found that the changing trend of driver operation signals is a better predictor of braking intent than absolute values. Based on these findings, they proposed a method to fuse multi-dimensional vehicle parameters with driving behavior characteristics, constructing a dual judgment mechanism of dynamic condition index and driving intent type, and subsequently establishing a graded braking force distribution strategy.

[0086] Therefore, this application proposes to generate a dynamic operating condition index by weighted fusion of acquired vehicle operating parameters. The dynamic operating condition index includes dynamic load level and comprehensive friction level. Simultaneously, real-time driver signals are acquired and driving intention type is predicted. Based on the dynamic operating condition index and driving intention type, the braking force demand level for each wheel cylinder is determined. Finally, the brake fluid volume is adjusted and corrected in real time based on the demand level.

[0087] See Figure 1 , Figure 1 A flowchart of a vehicle braking control method according to some embodiments of this specification is shown, specifically including the following steps.

[0088] Step 101: Obtain the vehicle operating parameters of the vehicle in operation and perform weighted fusion processing to generate a dynamic operating condition index.

[0089] Specifically, vehicle operating parameters refer to a multi-dimensional set of data reflecting the vehicle's motion state, which can be signals or data collected by devices such as vehicle speed sensors, load sensors, and wheel speed sensors.

[0090] The dynamic operating condition index is a quantitative indicator that characterizes the real-time motion state of a vehicle and provides a dynamic benchmark for braking force distribution.

[0091] Step 102: Obtain the real-time driving signal of the current driver in the preset time window and import it into the preset driving intention prediction model to generate the driving intention type. The real-time driving signal includes the steering wheel angle signal and the pedal travel signal.

[0092] Driving intent type refers to the classification and identification of driver's operational behavior.

[0093] As a specific example, driving intent types can include the following three categories:

[0094] D1 (Smooth Driving): can indicate that the rate of change of throttle / brake is <10% / s.

[0095] D2 (Predictive Operation): This indicates that the steering wheel angle remains greater than 15°.

[0096] D3 (Emergency Operation): This indicates that the brake pedal travel instantaneously reaches more than 80%.

[0097] Step 103: Determine the braking force demand level for each wheel cylinder based on the dynamic operating condition index and driving intention type. The braking force demand level includes a low demand level that represents maintaining the current braking, a medium demand level that represents increasing the front wheel distribution ratio, and a high demand level that represents distributing the maximum pressure.

[0098] In some optional implementations, the braking force requirement level for each wheel cylinder can be determined based on dynamic operating condition indices, driving intention types, and a preset braking force prediction model. This braking force prediction model can be any type of computational model capable of outputting classification results, and can be configured as needed.

[0099] Braking force demand level refers to the priority of pressure distribution for different wheel cylinders. Specifically, it can be achieved by establishing a three-level distribution strategy of low, medium and high, which correspond to three pressure control modes: maintenance, adjustment and maximization, respectively.

[0100] As a specific example, braking force demand levels can include the following three categories:

[0101] C1: Low demand, which can mean that the current allocation can be maintained.

[0102] C2: Medium demand, which can represent an increase of 10% in braking force distribution to the front wheels.

[0103] C3: High demand, which can indicate the maximum pressure distribution across the entire wheel, for example, prioritizing the braking demand of the wheel cylinder on the side with the higher coefficient of friction.

[0104] Step 104: Adjust the brake fluid volume of each wheel cylinder based on the braking force demand level to regulate the vehicle's braking performance, and correct the brake fluid volume of each wheel cylinder in real time based on the acquired feedback signal.

[0105] Specifically, after preprocessing, vehicle operating parameters are used to generate a dynamic operating condition index that comprehensively reflects the vehicle load distribution and road friction conditions through a dynamic weight allocation algorithm. Simultaneously, real-time collected steering wheel angle and pedal travel signals are processed using rate-of-change calculations and input into a trained classification model to identify driving intention types. When both steering trend and emergency braking characteristics are detected simultaneously, the system automatically prioritizes front wheel braking force distribution. The pressure increase gradient is adjusted based on the friction level in the dynamic operating condition index, employing a gradual pressure increase strategy on low-friction surfaces. During braking, 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 an allowable range.

[0106] Compared to existing technologies, traditional methods rely on preset fixed distribution ratios and cannot dynamically adjust pressure distribution according to the actual movement of the vehicle. This solution introduces a dynamic operating condition index, enabling the braking system to sense changes in vehicle load transfer and road surface adhesion conditions in real time. Compared to anti-lock braking strategies that solely rely on wheel speed differences, this method can predict potential slip risks in the early stages of braking and adjust pressure distribution in advance. Furthermore, existing technologies exhibit a lag in judging driver intentions, while this solution, by analyzing the changing trends of operating signals, can identify emergency braking needs before the pedal depth reaches a threshold.

[0107] Through the above technical solution, this application can achieve precise matching between braking pressure distribution and vehicle dynamic characteristics, effectively balancing the braking torque distribution between the front and rear axles under cornering braking conditions. During emergency braking on low-traction surfaces, premature wheel lock-up is avoided by dynamically adjusting the pressure increase gradient. Simultaneously, the response time from recognizing driving intentions to executing pressure adjustments is shortened, improving the coordinated control capability of the braking system in sudden avoidance scenarios.

[0108] In some embodiments, this application further proposes a vehicle braking control method, which acquires vehicle operating parameters of a vehicle in operation and performs weighted fusion processing to generate a dynamic operating condition index. The method includes acquiring vehicle operating parameters, which include vehicle speed signal, load signal, road friction signal, co-axle wheel speed difference data, and road texture recognition data; performing noise filtering 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 friction signal and combining the co-axle wheel speed difference data and road texture recognition data to generate a comprehensive friction index through fuzzy logic; and performing weighted fusion processing on the smoothed vehicle speed, dynamic load parameters, and comprehensive friction index based on the current weighted index, which is dynamically adjusted according to the vehicle operating mode, including a stable mode, a critical mode, and a dangerous mode.

[0109] The noise filtering process involves using digital filters to remove high-frequency interference components from the vehicle speed signal. Specifically, a Butterworth low-pass filter can be used to filter out signal jitter caused by wheel vibration or sensor noise by setting a cutoff frequency. Normalization maps the raw values ​​of the load signal to a preset range. This can be achieved using a linear normalization algorithm, converting load signals with different dimensions into dimensionless values ​​with a unified dimension for subsequent weighted calculations. Fuzzy logic generation of the comprehensive friction index involves performing fuzzy inference operations on the feature values ​​of the road friction signal with the coaxial wheel speed difference and road texture data. This can be achieved using a fuzzy rule base for membership calculation, transforming discrete sensor data into continuous comprehensive friction level parameters. Dynamic adjustment of the weighted index involves selecting different weight allocation strategies based on the vehicle's operating mode. For example, a balanced weight allocation is used in stable mode, while the weight ratio of the comprehensive friction index is increased in hazardous mode.

[0110] Specifically, after vehicle operating parameters are collected by multi-source sensors, the vehicle speed signal is first low-pass filtered to eliminate high-frequency noise interference. For example, a Butterworth filter is used to filter out noise components above 30Hz, retaining the effective vehicle speed signal of 0-10Hz. The load signal is normalized and converted into dynamic load parameters in the range of 0-1, facilitating weighted superposition calculations with other parameters. The road friction signal obtains the friction coefficient variation trend through time-domain feature extraction, combined with tire slippage characteristics reflected by coaxial wheel speed difference data, and road texture recognition data collected by visual sensors, and outputs a comprehensive friction index through a fuzzy logic system. The three core parameters—smooth vehicle speed, dynamic load, and comprehensive friction index—are fused and calculated according to the weighting coefficients corresponding to the current vehicle operating mode. For example, in hazardous mode, the comprehensive friction index is assigned a weighting coefficient of 0.6, while in stable mode, it is assigned only a weighting coefficient of 0.3, ultimately generating a condition index characterizing the vehicle's dynamic operating state.

[0111] Compared to existing technologies, traditional methods often use fixed weighting coefficients when calculating operating condition indices, failing to dynamically adjust parameter weights based on the vehicle's actual operating state. For example, when a vehicle enters an emergency braking state, existing technologies still allocate the comprehensive friction index according to conventional weights, leading to a lag in braking control decisions. This application, however, introduces a dynamic weighting adjustment mechanism based on operating modes, effectively improving the accuracy of parameter fusion under complex operating conditions.

[0112] Through the above technical solution, this application can accurately identify the dynamic operating characteristics of vehicles under different operating modes. For example, in hazardous modes, it strengthens the calculation weight of the comprehensive friction index, enabling the braking control system to detect changes in road adhesion in advance. This dynamic weighted fusion mechanism solves the problem of insufficient parameter fusion accuracy in emergency conditions of traditional methods, providing a more accurate basis for subsequent braking force distribution.

[0113] In some embodiments, this application further proposes a step for generating a driving intention type, including: acquiring steering wheel angle signals and pedal travel signals in a preset time window, and calculating the rate of change to obtain steering trend features and emergency operation features respectively; comparing the steering trend features with a preset steering threshold, and simultaneously comparing the emergency operation features with a preset emergency threshold to obtain a comparison result; matching the comparison result with a preset driving intention comparison table to determine the current driver's driving intention type, wherein the driving intention type includes a smooth driving mode, a predictive operation mode, and an emergency operation mode.

[0114] Among them, the steering trend feature refers to the rate of change of the steering wheel angle signal within a preset time window. Specifically, it can be achieved by using a differential algorithm to calculate the difference in steering wheel angle between adjacent moments, and then dividing it by the time interval to obtain the rate of angle change, which is used to characterize the continuity or abruptness of the driver's steering intention.

[0115] Among them, the emergency operation feature refers to the rate of change of the pedal travel signal within a preset time window. Specifically, it can be achieved by calculating the acceleration change using the second derivative of the pedal travel within the sliding window, which is used to identify the urgency of the pedal operation.

[0116] The driving intention comparison table refers to a logical mapping table that includes a combination of steering trend features and emergency operation feature thresholds. Specifically, it can be stored in the form of a two-dimensional matrix to store the driving intention types corresponding to different threshold ranges. 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.

[0117] Specifically, the steering wheel angle signal and pedal travel signal are collected in real time by onboard sensors. The preset time window can refer to a time interval before the current moment; for example, the interval can be selected from 0.5 seconds to 2 seconds before the current moment. The steering trend characteristic can be calculated using the first derivative of the steering wheel angle within the sliding window. When the absolute value of this derivative exceeds a preset steering threshold, it indicates an active steering intention. The emergency operation characteristic is calculated by the absolute value of the pedal travel signal increment per unit time. If it continuously exceeds the preset emergency threshold, it is determined to be an emergency braking demand. By comparing both with the preset threshold and combining the logical mapping relationship of the driving intention comparison table, three typical scenarios—smooth driving, predictive steering to avoid obstacles, or emergency braking—can be accurately distinguished. For example, when the steering trend characteristic reaches the threshold but the emergency operation characteristic does not reach the threshold, the predictive operation mode is triggered, and the front wheel braking force distribution is adjusted in advance.

[0118] Compared to existing technologies, current solutions typically rely solely on the steering wheel angle or pedal signal at the current moment to determine intent, failing to capture changes in operational trends. This solution, however, extracts signal change rate features through a preset time sliding window, enabling early identification of the evolving driving intent. For example, when the steering wheel angle begins to increase but has not yet reached a dangerous threshold, the rate of change detects the steering trend, allowing for pre-distribution of braking force before the actual steering action occurs.

[0119] Through the above technical solution, this application can identify dynamic changes in driving intention earlier during braking control, avoiding lag in braking force distribution caused by signal processing delays. For example, when a steering trend characteristic is detected to exceed a threshold, the system can increase the front wheel braking force distribution ratio in advance to prevent understeer; when an emergency operation characteristic continues to exceed the threshold, the maximum pressure distribution mode is immediately triggered to shorten the emergency braking response time.

[0120] In some embodiments, this application further proposes to correct the brake fluid volume of each wheel cylinder in real time based on the acquired feedback signal, including calculating the pressure deviation value of each wheel cylinder based on the acquired 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, importing the pressure deviation value and the viscosity correction coefficient into a preset calculation model; and adjusting 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.

[0121] The pressure deviation value refers to the difference between the real-time pressure of the wheel cylinder and the target braking pressure. Specifically, it can be achieved by subtracting the target pressure from the real-time pressure measured by a pressure sensor. The target pressure is determined based on the target calculated value corresponding to the braking force requirement level. Temperature compensation processing refers to predicting oil viscosity changes by measuring ambient temperature. This can be achieved by collecting ambient temperature data using a temperature sensor and applying a preset viscosity-temperature correspondence table or compensation algorithm. The viscosity correction coefficient is a quantitative parameter used to correct for differences in oil flowability caused by temperature changes. It can be generated using a lookup table method or a polynomial fitting model. The valve opening adjustment index is an indicator used to control the opening degree of the solenoid valve. It can be calculated using a proportional-integral-derivative control algorithm combined with the pressure deviation and viscosity correction coefficient.

[0122] Specifically, in the process of correcting the brake fluid volume, real-time pressure signals are first collected using wheel cylinder pressure sensors, and the difference between these signals and the target braking pressure is calculated to generate a pressure deviation value. Simultaneously, ambient temperature data is acquired using temperature sensors, and a viscosity correction coefficient is generated using a pre-set temperature compensation algorithm. The pressure deviation value and viscosity correction coefficient are input into a calculation model, which dynamically generates valve opening adjustment commands for each wheel cylinder based on the fluid flow characteristics and valve control principles. After the adjustment commands are transmitted to the solenoid valve actuator, the flow rate of the brake fluid entering the wheel cylinder is precisely adjusted by changing the valve opening, thereby achieving closed-loop control of the braking pressure.

[0123] Compared to existing technologies, traditional braking control methods typically rely solely on pressure deviation for valve control, neglecting the changes in flow characteristics caused by variations in oil viscosity with temperature. This leads to potential regulation lag or overshoot in extreme temperature environments. This solution introduces a temperature compensation mechanism to simultaneously correct oil viscosity parameters when calculating valve opening, enabling the control model to adapt to ambient temperature changes and improving the accuracy and stability of pressure regulation.

[0124] Through the above technical solution, this application effectively solves the problem of insufficient control precision in traditional brake fluid. By coordinating the control of pressure deviation value and viscosity correction coefficient, the fluid flow rate can be accurately controlled under different ambient temperature conditions, ensuring that the wheel cylinder pressure quickly converges to the target value, thereby improving the response speed and control precision of the braking system.

[0125] In some embodiments, this application further proposes a method for calculating the pressure deviation value of each wheel cylinder based on the acquired feedback signal, including acquiring the feedback signal of each wheel cylinder; determining the real-time pressure of each wheel cylinder based on the feedback signal; and calculating the difference between each real-time pressure and the target braking pressure, wherein the target braking pressure is the target calculated value corresponding to the braking force demand level.

[0126] The feedback signal refers to the hydraulic parameters collected by the sensors installed inside the wheel cylinder. Specifically, it can be achieved by using a pressure sensor to monitor the pressure changes inside the wheel cylinder in real time, which is used to reflect the actual braking execution status.

[0127] Among them, real-time pressure refers to the current oil pressure value of the wheel cylinder obtained by the sensor. Specifically, it can be achieved by periodically sampling the sensor output signal, and generating a numerical value after analog-to-digital conversion, which is used to quantify the current actual braking force.

[0128] The target braking pressure refers to the expected pressure value preset according to the braking force demand level. Specifically, the value 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 operating parameters to provide a benchmark reference for pressure deviation calculation.

[0129] The difference refers to the arithmetic difference between the real-time pressure and the target pressure. Specifically, it can be achieved by comparing and calculating the two values ​​in real time using subtraction, which is used to quantify the current pressure adjustment requirements.

[0130] Specifically, during braking control, the pressure sensors built into the wheel cylinders continuously collect hydraulic pressure data and transmit it to the control unit. The control unit filters the data and generates real-time pressure values ​​for each wheel cylinder. Simultaneously, based on the current braking force requirement, the corresponding target pressure value is retrieved from a preset mapping table. The difference between the real-time pressure and the target pressure is calculated through subtraction, and this difference serves as the basis for subsequent hydraulic fluid volume adjustment. For example, when the target pressure is 10 MPa and the real-time pressure is 8 MPa, a positive deviation of 2 MPa is calculated, triggering a valve opening increase command to replenish the hydraulic fluid.

[0131] Compared to existing technologies, traditional methods typically rely on preset fixed pressure thresholds for adjustment, failing to consider the impact of dynamic changes in braking force demand levels on the target pressure, resulting in inaccurate deviation calculation benchmarks. This solution, however, dynamically matches the braking force demand level with the target pressure, enabling deviation calculations to reflect actual operating conditions in real time, thereby improving the response accuracy of pressure regulation.

[0132] Through the above technical solution, this application can accurately quantify the difference between the actual pressure and the target pressure of each wheel cylinder, providing a precise adjustment basis for subsequent oil volume compensation, and avoiding the problem of reduced braking performance or unbalanced braking force distribution caused by the accumulation of pressure deviation.

[0133] In some embodiments, this application further proposes that when an abnormal signal is detected in the sensor transmission, the acquisition of the transmission signal of the sensor with the abnormal signal is interrupted; and a redundant data source corresponding to the sensor with the abnormal signal is acquired, wherein the redundant data source is the signal transmitted by other sensors calculated.

[0134] Signal anomaly refers to the sensor output signal value exceeding a preset reasonable range or not conforming to expected variation patterns. This can be achieved through threshold comparison or time-domain feature analysis. For example, an anomaly is determined when the accelerometer output value exceeds three times the gravitational acceleration. This feature is used to prevent erroneous signals from interfering with braking control.

[0135] Redundant data sources refer to alternative data generated by algorithms from raw data from other sensors. This can be achieved through data fusion or state observers. For example, when wheel speed sensors fail, equivalent wheel speed data can be estimated using steering angle and yaw rate signals via a Kalman filter. This feature is used to maintain the continuous operation of the braking control system in the event of sensor failure.

[0136] Specifically, when the onboard electronic control unit detects an anomaly in the signal transmitted by a sensor during periodic self-tests, it immediately cuts off the signal input channel of that sensor to prevent erroneous data from flowing into subsequent processing stages. Subsequently, the system activates a pre-set redundant calculation module, retrieves data from other sensors associated with the function of that sensor, and generates an equivalent measurement value through a preset compensation algorithm. For example, in the event of a brake pressure sensor failure, an alternative pressure estimate can be generated by reverse calculation using the wheel cylinder valve opening signal and the oil pump speed signal for brake force distribution control. Thus, the brake control system can maintain basic functionality even when a critical sensor suddenly fails, preventing overall control interruption due to the failure of a single sensor.

[0137] Compared to existing technologies, traditional braking control methods typically rely on a single sensor for key parameter acquisition. When the sensor experiences intermittent failures, the system cannot promptly identify abnormal data or switch to a backup signal source, easily leading to incorrect braking pressure distribution or response delays. This solution, however, introduces a dynamic redundant signal generation mechanism, enabling signal source switching within milliseconds after a sensor failure, ensuring the continuity and stability of the braking control loop.

[0138] Through the above technical solution, this application can quickly activate the backup data source when the vehicle sensor suddenly malfunctions, effectively avoid the problem of brake pressure misadjustment caused by signal distortion, improve the fault tolerance and safety redundancy of the vehicle braking system under complex working conditions, and reduce the configuration requirements of hardware redundant sensors.

[0139] In some embodiments, this application further proposes caching vehicle operating parameters within a preset time window; when the data transmission interruption duration is detected to exceed the limit, calculations are performed based on the cached vehicle operating parameters.

[0140] The preset time window refers to a pre-defined time range used to store vehicle operating parameters. Specifically, it can be implemented using a circular buffer, which continuously records the latest parameters by cyclically overwriting old data, so as to retain valid information within a limited storage space.

[0141] Among them, the data transmission interruption duration exceeding the limit refers to the duration of signal loss caused by sensor or communication link failure exceeding a preset threshold. Specifically, the signal interruption duration can be monitored by a timer, and when it exceeds the threshold, an emergency handling mechanism is triggered to ensure that the braking control logic can still maintain operation under abnormal conditions.

[0142] Specifically, during vehicle operation, real-time collected vehicle operating parameters are continuously stored in a buffer corresponding to a preset time window. When the system detects a data transmission interruption and the interruption duration exceeds a set threshold, it automatically switches to buffer mode and calls the vehicle operating parameters stored in the buffer to perform subsequent calculations, such as generating dynamic operating condition indices and determining braking force demand levels. In this way, even during communication link anomalies, the system can still maintain braking control functions based on historical data, avoiding control failure due to signal loss.

[0143] Compared to existing technologies, traditional braking control methods typically rely on real-time transmitted vehicle operating parameters. When data transmission is interrupted, effective information cannot be obtained, leading to control logic interruptions or the use of default parameters, potentially causing decreased braking performance or safety hazards. This solution, however, retains historical data through a caching mechanism, enabling dynamic adjustments based on valid information even in abnormal situations, thus improving system fault tolerance and operational continuity.

[0144] Through the above technical solution, this application can maintain braking control function when the communication link is abnormal, avoiding control failure or delay due to signal interruption, and ensuring the braking stability of the vehicle under complex operating conditions. At the same time, the caching mechanism can reduce the complete dependence on real-time data and improve the robustness of the system under signal fluctuation or short-term interruption scenarios.

[0145] The following is in conjunction with the appendix Figure 2 The diagram illustrates a process flow of a vehicle braking control method provided in other embodiments of this specification, specifically including the following steps.

[0146] Step 201: Obtain the vehicle operating parameters of the vehicle in operation and perform weighted fusion processing to generate a dynamic operating condition index;

[0147] Step 202: Obtain the steering wheel angle signal and pedal travel signal in the preset time sliding window, and calculate the rate of change to obtain the steering trend characteristics and emergency operation characteristics respectively;

[0148] Step 203: Compare the steering trend characteristics with the preset steering threshold, and at the same time compare the emergency operation characteristics with the preset emergency threshold to obtain the comparison results;

[0149] Step 204: Match the comparison results with the preset driving intention comparison table to determine the current driver's driving intention type, which includes smooth driving mode, predictive operation mode and emergency operation mode.

[0150] Step 205: Determine the braking force requirement level for each wheel cylinder based on the dynamic operating condition index and driving intention type;

[0151] Step 206: Adjust the amount of brake fluid in each wheel cylinder based on the braking force requirement level to regulate the vehicle's braking performance.

[0152] Step 207: Calculate the pressure deviation value of each wheel cylinder based on the acquired feedback signal;

[0153] 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;

[0154] Step 209: Import the pressure deviation value and viscosity correction coefficient into the preset calculation model to generate the valve opening adjustment index for each cylinder.

[0155] Step 210: 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.

[0156] In some embodiments, steps 201-210 are related to Figure 1 The specific implementation of the corresponding steps in those embodiments and the resulting technical effects can be found in the following references. Figure 1 The steps involved will not be elaborated upon here.

[0157] Corresponding to the above method embodiments, this specification also provides embodiments of a vehicle braking control device. Figure 3 A schematic diagram of the structure of a vehicle braking control device provided in some embodiments of this specification is shown. For example... Figure 3 As shown, the device includes:

[0158] The first generation module 301 is configured to acquire the vehicle operating parameters of the running vehicle and perform weighted fusion processing to generate a dynamic operating condition index.

[0159] The second generation module 302 is configured to acquire the real-time driving signal of the current driver in the preset time window and import it into the preset driving intention prediction model to generate the driving intention type. The real-time driving signal includes the steering wheel angle signal and the pedal travel signal.

[0160] The determination module 303 is configured to determine the braking force demand level of each wheel cylinder based on the dynamic operating condition index and driving intention type. The braking force demand level includes a low demand level that represents maintaining the current braking, a medium demand level that represents increasing the front wheel distribution ratio, and a high demand level that represents distributing the maximum pressure.

[0161] The adjustment module 304 is configured to adjust the amount of brake fluid in each wheel cylinder based on the braking force demand level in order to adjust the braking condition of the vehicle, and to correct the amount of brake fluid in each wheel cylinder in real time based on the acquired feedback signal.

[0162] In some embodiments, vehicle operating parameters of a vehicle in operation are acquired and weighted fusion processing is performed to generate a dynamic operating condition index, including:

[0163] Acquire vehicle operating parameters of the vehicle in operation, including vehicle speed signal, load signal, road friction signal, co-axle wheel speed difference data and road texture recognition data;

[0164] The vehicle speed signal is processed by noise filtering to obtain a smooth vehicle speed;

[0165] The load signal is normalized to generate a dynamic load;

[0166] Feature extraction is performed on road friction signals, and combined with coaxial wheel speed difference data and road texture recognition data, a comprehensive friction index is generated through fuzzy logic.

[0167] The smooth vehicle speed, dynamic load parameters, and comprehensive friction index are weighted and fused based on the current weighted indicators. The weighted indicators are dynamically adjusted according to the vehicle operation mode, which includes stable mode, critical mode, and dangerous mode.

[0168] In some embodiments, the step of generating a driving intent type includes:

[0169] The steering wheel angle signal and pedal travel signal are obtained from the preset time sliding window, and the rate of change is calculated to obtain the steering trend characteristics and emergency operation characteristics respectively;

[0170] The turning trend characteristics are compared with a preset turning threshold, and the emergency operation characteristics are compared with a preset emergency threshold to obtain the comparison results.

[0171] The current driver's driving intention type is determined by matching the comparison results with a preset driving intention comparison table. The driving intention type includes smooth driving mode, predictive operation mode, and emergency operation mode.

[0172] In some embodiments, the brake fluid volume of each wheel cylinder is adjusted in real time based on the acquired feedback signal, including:

[0173] The pressure deviation value of each wheel cylinder is calculated based on the obtained feedback signal;

[0174] Temperature compensation processing is performed on the oil temperature of each wheel cylinder based on the ambient temperature to generate a viscosity correction coefficient;

[0175] The pressure deviation value and viscosity correction coefficient are imported into the preset calculation model to generate the valve opening adjustment index for each cylinder.

[0176] The valve opening of each wheel cylinder is adjusted according to the valve opening adjustment index to correct the brake fluid volume of each wheel cylinder in real time.

[0177] In some embodiments, calculating the pressure deviation value of each wheel cylinder based on the acquired feedback signal includes:

[0178] Obtain feedback signals from each wheel cylinder;

[0179] The real-time pressure of each wheel cylinder is determined based on the feedback signal;

[0180] 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.

[0181] In some embodiments, the above-described apparatus further includes a first anomaly handling module, configured to: when an anomaly is detected in the signal transmitted by a sensor, interrupt the acquisition of the transmission signal of the sensor with the anomaly; acquire a redundant data source corresponding to the sensor with the anomaly, wherein the redundant data source is calculated from the signals transmitted by other sensors.

[0182] In some embodiments, the above-described apparatus further includes a second exception handling module, configured to: cache vehicle operating parameters within a preset time window; and perform calculations based on the cached vehicle operating parameters when a data transmission interruption duration is detected to exceed the limit.

[0183] The above is a schematic scheme 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-described vehicle braking control method belong to the same concept. For details not described in detail in the technical solution of the vehicle braking control device, please refer to the description of the technical solution of the above-described vehicle braking control method.

[0184] In some embodiments, the present invention also provides a vehicle, characterized in that the vehicle is equipped with a control center and a hydraulic braking module AHB, the hydraulic braking module AHB being equipped with a brake controller unit ECU, a booster unit PSU, and a hydraulic control unit PCU, wherein...

[0185] The control center is used to execute the steps of the vehicle braking control method as claimed in the preceding claims;

[0186] The brake controller unit (ECU) is connected to the booster unit (PSU) and the hydraulic control unit (PCU) respectively. It is used to receive control commands sent by the upper-level control center and generate a first command and a second command according to the control commands to control the booster unit (PSU) and the hydraulic control unit (PCU) to perform pressure control. The first command is sent to the booster unit (PSU) and the second command is sent to the booster unit (PSU).

[0187] The booster unit (PSU) is used to output hydraulic oil at a certain pressure to the hydraulic control unit (PCU) according to the first received instruction.

[0188] The hydraulic control unit (PCU) is used to adjust, control, and distribute the output pressure according to the received second command, so as to ensure that different working pressures are achieved under different working conditions.

[0189] Figure 4 A structural block diagram of a computing device 400 according to some embodiments of this 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 via a bus 403, and a database 405 is used to store data.

[0190] The computing device 400 also includes an access device 404 that enables the computing device 400 to communicate via one or more networks 406. Examples of such networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations 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 (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0191] In one embodiment of this specification, the aforementioned components of the computing device 400 and Figure 4 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 4 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0192] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 400 can also be a mobile or stationary server.

[0193] The processor 402 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the aforementioned vehicle braking control method. The above is an illustrative scheme 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 aforementioned vehicle braking control method belong to the same concept; details not described in detail in the technical solution of the computing device can be found in the description of the technical solution of the aforementioned vehicle braking control method.

[0194] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the vehicle braking control method described above.

[0195] The above is an illustrative scheme 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 vehicle braking control method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the vehicle braking control method described above.

[0196] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described vehicle braking control method.

[0197] The above is an illustrative scheme 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-described vehicle braking control method belong to the same concept. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the above-described vehicle braking control method.

[0198] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0199] Computer instructions include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in computer-readable media can be appropriately added to or removed according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0200] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0201] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0202] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the embodiments described in this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A vehicle braking control method, characterized in that, include: The process involves acquiring vehicle operating parameters of a vehicle in operation and performing weighted fusion processing to generate a dynamic operating condition index. This includes: acquiring vehicle operating parameters, which include vehicle speed signal, load signal, road friction signal, co-axle wheel speed difference data, and road texture recognition data; performing noise filtering on the vehicle speed signal to obtain a smoothed speed; normalizing the load signal to generate a dynamic load; extracting features from the road friction signal and combining it with the co-axle wheel speed difference data and road texture recognition data to generate a comprehensive friction index using fuzzy logic; and performing weighted fusion processing on the smoothed speed, the dynamic load parameters, and the comprehensive friction index based on a current weighted index, which is dynamically adjusted according to the vehicle operating mode, including stable mode, critical mode, and dangerous mode. The system acquires the real-time driving signals of the current driver within a preset time window and imports them into a preset driving intention prediction model to generate a driving intention type. The real-time driving signals include steering wheel angle signals and pedal travel signals. The steps for generating the driving intention type include: acquiring the steering wheel angle signals and pedal travel signals within the preset time window, and calculating the rate of change to obtain steering trend features and emergency operation features respectively; comparing the steering trend features with a preset steering threshold, and simultaneously comparing the emergency operation features with a preset emergency threshold to obtain a comparison result; matching the comparison result with a preset driving intention comparison table to determine the current driver's driving intention type, wherein the driving intention type includes a smooth driving mode, a predictive operation mode, and an emergency operation mode. Based on the dynamic operating condition index and the driving intention type, the braking force demand level of each wheel cylinder is determined, wherein the braking force demand level includes a low demand level that represents maintaining the current braking, a medium demand level that represents increasing the front wheel distribution ratio, and a high demand level that represents distributing the maximum pressure. The brake fluid volume of each wheel cylinder is adjusted based on the braking force demand level to regulate the vehicle's braking performance. The brake fluid volume of each wheel cylinder is then corrected in real-time based on acquired feedback signals. This real-time correction of the brake fluid volume of each wheel cylinder based on the acquired feedback signals includes: calculating the pressure deviation value of each wheel cylinder based on the acquired feedback signals; performing temperature compensation processing on the fluid temperature of each wheel cylinder based on ambient temperature to generate a viscosity correction coefficient; importing 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; and adjusting the valve opening of each wheel cylinder based on the valve opening adjustment index to correct the brake fluid volume of each wheel cylinder in real-time.

2. The method according to claim 1, characterized in that, The pressure deviation values ​​of each wheel cylinder are calculated based on the acquired feedback signals, including: Obtain feedback signals from each wheel cylinder; The real-time pressure of each wheel cylinder is determined based on 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.

3. The method according to claim 1, characterized in that, Also includes: When an abnormal signal is detected in the sensor, the acquisition of the sensor's transmitted signal is interrupted. Obtain redundant data sources corresponding to sensors with abnormal signals, wherein the redundant data sources are calculated from signals transmitted by other sensors.

4. The method according to claim 1, characterized in that, Also includes: Cache vehicle operating parameters within a preset time window; When the data transmission interruption duration exceeds the limit, calculations are performed based on the cached vehicle operating parameters.

5. A vehicle braking control device, characterized in that, include: The first generation module is configured to acquire vehicle operating parameters of a running vehicle and perform weighted fusion processing to generate a dynamic operating condition index. The acquisition of vehicle operating parameters and the weighted fusion processing to generate the dynamic operating condition index includes: acquiring vehicle operating parameters of the running vehicle, which include vehicle speed signal, load signal, road friction signal, co-axle wheel speed difference data, and road texture recognition data; performing noise filtering 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 friction signal and combining it with the co-axle wheel speed difference data and road texture recognition data to generate a comprehensive friction index through fuzzy logic; and performing weighted fusion processing on the smoothed vehicle speed, the dynamic load parameters, and the comprehensive friction index based on a current weighted index, which is dynamically adjusted according to the vehicle operating mode, including a stable mode, a critical mode, and a dangerous mode. The second generation module is configured to acquire the real-time driving signals of the current driver within a preset time window and import them into a preset driving intention prediction model to generate a driving intention type. The real-time driving signals include steering wheel angle signals and pedal travel signals. The steps for generating the driving intention type include: acquiring the steering wheel angle signals and pedal travel signals within the preset time window, and calculating the rate of change to obtain steering trend features and emergency operation features respectively; comparing the steering trend features with a preset steering threshold, and simultaneously comparing the emergency operation features with a preset emergency threshold to obtain a comparison result; matching the comparison result with a preset driving intention comparison table to determine the current driver's driving intention type, wherein the driving intention type includes a smooth driving mode, a predictive operation mode, and an emergency operation mode. The determination module is configured to determine the braking force demand level of each wheel cylinder based on the dynamic operating condition index and the driving intention type, wherein the braking force demand level includes a low demand level that represents maintaining the current braking, a medium demand level that represents increasing the front wheel distribution ratio, and a high demand level that represents distributing the maximum pressure. The adjustment module is configured to adjust the brake fluid volume of each wheel cylinder based on the braking force demand level to regulate the braking condition of the vehicle, and to correct the brake fluid volume of each wheel cylinder in real time based on the acquired feedback signal. The real-time correction of the brake fluid volume of each wheel cylinder based on the acquired feedback signal includes: calculating the pressure deviation value of each wheel cylinder based on the acquired feedback signal; performing temperature compensation processing on the oil temperature of each wheel cylinder based on the ambient temperature to generate a viscosity correction coefficient; importing 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; and adjusting 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.

6. A vehicle, characterized in that, The vehicle is equipped with a control center and a hydraulic braking module (AHB). The hydraulic braking module (AHB) includes a brake controller unit (ECU), a booster unit (PSU), and a hydraulic control unit (PCU). The control center is used to execute the steps of the vehicle braking control method according to any one of claims 1 to 4; The brake controller unit (ECU) is connected to the booster unit (PSU) and the hydraulic control unit (PCU) respectively. It is used to receive control commands sent by the upper-level control center and generate a first command and a second command according to the control commands to control the booster unit (PSU) and the hydraulic control unit (PCU) to perform pressure control. The first command is sent to the booster unit (PSU) and the second command is sent to the booster unit (PSU). The booster unit (PSU) is used to output hydraulic oil at a certain pressure to the hydraulic control unit (PCU) according to the first received instruction. The hydraulic control unit (PCU) is used to adjust, control, and distribute the output pressure according to the received second command, so as to ensure that different working pressures are achieved under different working conditions.

7. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed by a processor, they implement the steps of the vehicle braking control method according to any one of claims 1 to 4.

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