Vehicle dynamic stability cooperative control method and system based on intelligent driving and vehicle

Abstract

The invention provides a vehicle dynamic stability cooperative control method and system based on intelligent driving and a vehicle, and the method comprises the steps: transferring the core logic of vehicle dynamic control VDC and traction control TCS to an intelligent driving domain controller ADDC in an intelligent driving function activation state; the ADDC adjusts a yaw velocity threshold value, a lateral acceleration threshold value and trigger delay time based on at least one of the vehicle speed, the road adhesion coefficient, the obstacle avoidance emergency degree and the trajectory deviation degree; a target yaw moment is calculated according to a target path and a real-time vehicle state, is decomposed into a four-wheel braking force instruction, and is sent to an integrated braking control system OneBox through a CANFD bus; the OneBox shields a local VDC / TCS operation module when there is no abnormality, and executes an ADDC instruction; and when the yaw velocity, the trajectory deviation and the steering wheel torque meet preset conditions, the ADDC gradually reduces the braking force command until the braking force command returns to zero. According to the method, the trajectory tracking precision and the driving stability are improved, and the VDC false triggering rate and the intelligent driving unexpected exit risk are remarkably reduced.

Description

Technical Field

[0001] This application relates to the cross-technical field of intelligent driving and vehicle chassis control, and in particular to a method, system and vehicle for coordinated control of vehicle dynamic stability based on intelligent driving. Background Technology

[0002] In existing intelligent driving systems, Vehicle Yaw Stability Control (VDC) and Traction Control System (TCS) are typically integrated into the braking control system (such as OneBox), operating independently of the intelligent driving domain controller. When VDC / TCS intervention is triggered under high-dynamic conditions, the system forcibly exits the intelligent driving mode, resulting in insufficient driver takeover window and increasing the risk of loss of control. Furthermore, traditional VDC uses a fixed activation threshold, which cannot adaptively adjust according to driving scenarios, making it prone to premature intervention in demanding conditions such as emergency obstacle avoidance, compromising trajectory tracking accuracy. In addition, VDC only passively regulates based on the vehicle's own state, without coordination with path planning, and the braking force distribution lacks optimization for tire utilization, leading to conservative control or frequent false triggers, affecting the continuity and control performance of the intelligent driving system. Summary of the Invention

[0003] The purpose of this application is to provide a vehicle dynamic stability cooperative control method, system and vehicle based on intelligent driving, so as to alleviate the above-mentioned technical problems existing in the prior art.

[0004] In a first aspect, the present invention provides a vehicle dynamic stability cooperative control method based on intelligent driving, wherein, when the intelligent driving function is activated, the core control logic of vehicle dynamic control and traction control is migrated to the intelligent driving domain controller for execution; including: The intelligent driving domain controller adjusts the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold based on at least one of the following parameters: vehicle speed, road surface adhesion coefficient, obstacle avoidance urgency, and the degree of trajectory deviation between the actual driving trajectory and the planned path. The intelligent driving domain controller calculates the target yaw moment based on the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold. It generates braking force commands based on the target yaw moment and periodically sends the braking force commands to the integrated braking control system through a high-speed communication interface. Upon receiving the braking force command, the integrated braking control system, in the absence of system malfunctions, disables its original vehicle dynamic control and traction control calculation modules and executes the braking force command from the intelligent driving domain controller. When the vehicle's yaw rate, the degree of trajectory deviation, and the torque applied to the steering device meet preset conditions, the intelligent driving domain controller gradually reduces the amplitude of the braking force command output to the external actuators until it stops outputting.

[0005] In an optional implementation, the intelligent driving domain controller adjusts the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold based on at least one of the following parameters: vehicle speed, road surface adhesion coefficient, obstacle avoidance urgency, and trajectory deviation between the actual driving trajectory and the planned path. This includes: Determine the target yaw rate threshold based on the current vehicle speed and road surface adhesion coefficient; The urgency of obstacle avoidance is determined based on the relative velocity and relative distance to the obstacle, and the target lateral acceleration threshold is determined based on the urgency of obstacle avoidance. The deviation between the actual driving trajectory and the planned path is obtained in real time. If the deviation exceeds a preset distance threshold and the duration exceeds a preset time threshold, the delayed intervention time will be extended.

[0006] In an optional implementation, the intelligent driving domain controller calculates the target yaw moment based on a yaw rate threshold, a lateral acceleration threshold, and a trigger delay time after the yaw rate continuously exceeds the threshold, and generates a braking force command based on the target yaw moment, including: The intelligent driving domain controller calculates the target yaw moment based on the target yaw moment and the actual vehicle yaw moment, using the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold as constraints. Based on the target yaw moment, the four-wheel braking force is distributed, and a braking force command corresponding to each wheel is generated.

[0007] In an optional implementation, upon receiving the braking force command, the integrated braking control system, in the absence of system anomalies, disables its original vehicle dynamic control and traction control calculation modules and executes the braking force command from the intelligent driving domain controller, including: The integrated braking control system receives braking force commands for all four wheels from the intelligent driving domain controller. In the absence of system anomalies, disable the output path of the local vehicle dynamic control and traction control calculation module to shield its original vehicle dynamic control and traction control calculation module. The received braking force command is converted into a braking pressure control signal, which drives the four-wheel braking actuator to operate.

[0008] In an optional implementation, when the vehicle yaw rate, the degree of trajectory deviation, and the torque applied to the steering device meet preset conditions, the intelligent driving domain controller gradually reduces the amplitude of the braking force command output to the external actuator until it stops outputting, including: The intelligent driving domain controller determines whether the following conditions are met simultaneously: The vehicle's yaw rate is lower than a first preset threshold and remains below a first preset time; The deviation between the actual driving position and the planned path is less than a second preset threshold and continues for a second preset time; The torque applied to the steering device exceeds a third preset threshold and remains so for a third preset time; After all conditions are met, the amplitude of the braking force command for each wheel is gradually reduced according to the preset attenuation law; Within a preset time period, the braking force command amplitude of each wheel is reduced to zero.

[0009] In an optional implementation, the method further includes: The intelligent driving domain controller receives feedback from the integrated braking control system on the actual wheel braking force, wheel speed and vehicle yaw rate. The actual yaw moment is determined based on the wheel speed, the wheel rotation speed, and the vehicle body yaw rate. Calculate the torque difference between the actual yaw moment and the target yaw moment; If the absolute value of the torque difference is greater than the preset deviation threshold, then update the tire lateral stiffness parameter or the predicted control time domain length in the control model.

[0010] In an optional implementation, the method further includes: The integrated braking control system monitors the hydraulic pressure of the braking system, wheel-end sensor signals, and the communication status between controllers. When abnormal hydraulic pressure, loss of wheel-end sensor signal, or communication interruption is detected, the input of braking force command from the intelligent driving domain controller is disabled. Start the local vehicle dynamics control and traction control calculation module; Send system degradation status information to the intelligent driving domain controller.

[0011] In a second aspect, the present invention provides an intelligent driving cooperative control system for implementing the method as described in any of the foregoing embodiments, comprising: The intelligent driving domain controller is equipped with a stability boundary prediction module, a trigger condition adjustment module, a target yaw moment generation and allocation module, a command sending module, and an exit determination and execution module. The integrated braking control system is equipped with an arbitration processing module, which, upon receiving a braking force command from the intelligent driving domain controller, disables the original vehicle dynamic control and traction control calculation modules within the system in the absence of system anomalies, and executes the braking force command. The multi-source sensing unit includes a vehicle speed detection module, an inertial measurement module, a steering input detection module, and an environmental perception module; The four-wheel brake actuator is used to respond to the brake pressure command output by the integrated brake control system.

[0012] In an optional implementation, the intelligent driving domain controller is connected to the integrated braking control system via a high-bandwidth vehicle communication bus, which periodically transmits braking force commands, system status information, and fault alarm information.

[0013] Thirdly, the present invention provides a vehicle equipped with the intelligent driving cooperative control system described in the foregoing embodiments.

[0014] This application provides a vehicle dynamic stability collaborative control method, system, and vehicle based on intelligent driving. It migrates the core control logic of VDC / TCS to the intelligent driving domain controller, achieving deep integration of stability control and the intelligent driving system, avoiding intervention conflicts caused by the separation of control authority. By dynamically adjusting the yaw rate threshold and lateral acceleration threshold based on vehicle speed, road adhesion coefficient, obstacle avoidance urgency, and trajectory deviation, and extending the trigger delay time, the system's tolerance to high-dynamic operations is improved, reducing unnecessary intervention. The calculation of the target yaw moment and the distribution of four-wheel braking force are uniformly completed by the intelligent driving domain controller. Combined with tire utilization optimization, control accuracy and response efficiency are improved. The integrated braking control system shields local logic and prioritizes the execution of external commands, ensuring the continuity of intelligent driving control. Finally, a smooth exit is achieved through multi-condition collaborative judgment, avoiding abrupt interruptions. Overall, this method significantly improves the trajectory tracking capability and driving stability of intelligent driving under complex conditions, effectively reducing the VDC false trigger rate and the risk of unexpected exit. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a vehicle dynamic stability cooperative control method based on intelligent driving, provided for an embodiment of this application; Figure 2 A detailed flowchart of a dynamic stability cooperative control method led by intelligent driving provided in an embodiment of this application; Figure 3 This is a schematic diagram of an intelligent driving cooperative control system provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0020] This application provides a vehicle dynamic stability collaborative control method based on intelligent driving. When the intelligent driving function is activated, the core control logic of vehicle dynamic control and traction control is migrated to the intelligent driving domain controller for execution. That is, the stability control algorithm module, originally integrated into the integrated braking control system (such as OneBox), is moved from the lower-level execution unit to the central decision-making layer of the intelligent driving domain controller (ADDC), achieving centralized and collaborative control functions. Specifically, when the driver activates L2 or higher level intelligent driving functions, after receiving the "intelligent driving mode activated" signal, the ADDC immediately loads and runs the VDC / TCS core control program. This program includes functional sub-modules such as stability parameter pre-calculation, dynamic threshold adjustment, target yaw moment calculation, and four-wheel braking force distribution. At this time, the original independent VDC / TCS operation logic within OneBox enters a suspended state, but continues to upload sensor data such as wheel speed, yaw rate, and acceleration for the ADDC to use. All judgments and control commands regarding vehicle stability are uniformly generated by ADDC based on the fusion results of multi-source perception information, realizing the transformation of the control architecture from distributed passive intervention to centralized active closed-loop regulation, and providing a functional integration foundation for multi-system collaborative decision-making and gradual transfer of control.

[0021] See Figure 1 As shown, the method mainly includes the following steps: S110, the intelligent driving domain controller, adjusts the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold based on at least one of the following parameters: vehicle speed, road surface adhesion coefficient, obstacle avoidance urgency, and the degree of trajectory deviation between the actual driving trajectory and the planned path.

[0022] Traditional systems use fixed thresholds, which can easily lead to premature intervention or frequent false triggers during high-speed lane changes or obstacle avoidance on slippery surfaces. This application addresses this by moving the control threshold adjustment mechanisms, such as the yaw rate threshold, lateral acceleration threshold, and continuous yaw rate exceeding the threshold, to the intelligent driving domain controller. This allows for dynamic modification of the judgment conditions for the vehicle stability control system to initiate intervention based on the current driving scenario, achieving linkage with the intelligent driving task status.

[0023] In practice, ADDC (Advanced Driver Response Control) appropriately increases the activation thresholds for yaw rate and lateral acceleration at high speeds based on real-time vehicle speed information. Combined with the estimated road adhesion coefficient, it raises the thresholds on low-adhesion surfaces (such as ice, snow, and slippery surfaces) to prevent intervention from being triggered by slight tire slippage. It judges the urgency of obstacle avoidance based on obstacle distance and relative speed, further relaxing restrictions in emergency situations. Simultaneously, it monitors the lateral deviation between the actual trajectory and the planned path, allowing for greater dynamic response space when the deviation is significant and persists for a certain period. All these parameters are matched against a pre-calibrated scene-threshold mapping table to achieve adaptive adjustment of activation conditions.

[0024] The S120 intelligent driving domain controller calculates the target yaw moment based on the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold. It generates braking force commands based on the target yaw moment and periodically sends the braking force commands to the integrated braking control system through a high-speed communication interface.

[0025] In one implementation, to correct the deviation between the vehicle's actual motion posture and the desired trajectory, a required corrective yaw moment, i.e., a target yaw moment, can be calculated and generated by the intelligent driving domain controller, based on constraints such as a yaw rate threshold, a lateral acceleration threshold, and a trigger delay time after the yaw rate continuously exceeds the threshold. After calculating the target yaw moment, it is distributed to all four wheels. Based on the vehicle dynamics model, the total yaw moment can be rationally distributed to each wheel to achieve optimal stability control.

[0026] In practice, ADDC integrates path planning results with vehicle motion states provided by IMU and wheel speed sensors, and uses predictive control algorithms to calculate the target yaw moment required to minimize yaw rate error. Subsequently, a nonlinear distribution strategy based on tire utilization optimization is employed, comprehensively considering factors such as vertical load and slippage state of each wheel, to convert the target moment into independent braking force commands for the four wheels (front, rear, left, and right). These commands are sent to the OneBox via the CAN FD bus at 10ms intervals to ensure real-time and continuous control.

[0027] The S130 integrated braking control system, upon receiving a braking force command, disables its original vehicle dynamic control and traction control calculation modules in the absence of system malfunctions, and executes the braking force command from the intelligent driving domain controller.

[0028] By disabling the original VDC / TCS calculation modules, during intelligent driving operation, OneBox no longer independently determines whether stability intervention is needed based on locally collected data, preventing dual control conflicts. Specifically, after receiving four-wheel braking force commands from ADDC, the integrated braking control system OneBox prioritizes parsing external commands. Provided there are no system faults such as abnormal braking pressure, lost wheel speed signals, or communication interruptions, it proactively disables the control logic output of the local VDC and TCS, fully executing the braking force commands from ADDC, and feeding back the "takeover" status to the domain controller, forming a closed-loop monitoring system. This mechanism ensures the intelligent driving system's control over vehicle stability, avoiding unexpected exits caused by rapid local responses in traditional modes.

[0029] S140, when the vehicle's yaw rate, trajectory deviation, and torque on the steering device meet preset conditions, the intelligent driving domain controller gradually reduces the amplitude of the braking force command output to the external actuators until it stops outputting.

[0030] In one specific implementation, ADDC continuously monitors whether the vehicle's yaw rate is below a preset stability threshold and remains stable. Simultaneously, it determines whether the lateral deviation between the actual driving trajectory and the planned path converges to within the allowable tolerance range, and detects whether the torque applied by the steering device reaches the identifiable takeover threshold and remains so for a certain duration, serving as a comprehensive basis for determining the driver's takeover intention. When all three conditions are met, the system initiates the exit procedure, gradually reducing the four-wheel braking force command from its current value to zero at a linear slope, completing the transfer of control. Afterward, OneBox restores its local VDC / TCS function and enters normal monitoring mode.

[0031] For ease of understanding, the following provides a detailed description of the vehicle dynamic stability cooperative control method based on intelligent driving provided in the embodiments of this application.

[0032] The aforementioned intelligent driving domain controller adjusts the yaw rate threshold, lateral acceleration threshold, and trigger delay time after the yaw rate continuously exceeds the threshold based on at least one of the following parameters: vehicle speed, road surface adhesion coefficient, obstacle avoidance urgency, and trajectory deviation between the actual driving trajectory and the planned path. In specific implementation, this may include the following steps 1.1 to 1.3: Step 1.1: Determine the target yaw rate threshold based on the current vehicle speed and road surface adhesion coefficient.

[0033] Traditional systems use fixed thresholds, which can easily lead to false triggering or insufficient response under high-speed or low-adhesion conditions. In this step, "determining the target yaw rate threshold based on the current vehicle speed and road surface adhesion coefficient" refers to dynamically setting the threshold for VDC system intervention based on the vehicle's operating conditions. By correlating the threshold setting with the actual driving environment, control adaptability is improved.

[0034] In practice, ADDC acquires vehicle speed information from wheel speed sensors in real time and estimates the current road adhesion coefficient μ by combining longitudinal acceleration and tire slip ratio. At high vehicle speeds, the yaw rate threshold is appropriately increased to accommodate greater dynamic maneuverability; on low-adhesion surfaces (such as wet, slippery, or icy surfaces), the restrictions are further relaxed to avoid premature braking intervention due to minor tire slippage. Finally, a pre-calibrated three-dimensional mapping table of "vehicle speed-adhesion coefficient-yaw rate threshold" is retrieved, and a target threshold adapted to the current operating conditions is output.

[0035] Step 1.2: Determine the urgency of obstacle avoidance based on the relative velocity and relative distance of the obstacle, and determine the target lateral acceleration threshold based on the urgency of obstacle avoidance.

[0036] The obstacle avoidance urgency level characterizes the risk level of the intelligent driving system's obstacle avoidance actions and is a key basis for determining whether higher lateral dynamic behaviors are permitted. In practice, ADDC obtains the relative distance and relative speed of obstacles ahead through the perception module, and combines the relative approach rate and predicted time of collision (TTC) to classify obstacle avoidance scenarios into three urgency levels: "low," "medium," and "high." When the urgency level is determined to be high, the system automatically increases the lateral acceleration activation threshold, for example, from the usual 0.4g to 0.65g, so that the vehicle can make full use of the tire's limit capacity during emergency lane changes, avoiding the interruption of obstacle avoidance actions due to premature VDC activation, thereby ensuring the integrity of the obstacle avoidance task and the accuracy of trajectory tracking.

[0037] Step 1.3: Real-time acquisition of the deviation between the actual driving trajectory and the planned path. If the deviation exceeds a preset distance threshold and the duration exceeds a preset time threshold, the delayed intervention time will be extended.

[0038] The degree of trajectory deviation is used to characterize the lateral deviation between the vehicle's current motion state and the expected path. Extending the delayed intervention time means delaying the response of the VDC system when a significant and continuous trajectory deviation is detected, giving the intelligent driving system more opportunities for autonomous correction. In specific implementation, ADDC continuously calculates the lateral error between the actual position and the planned path. When this error exceeds a set distance threshold (e.g., 0.3m) and continues to exceed a set time threshold (e.g., 1s), it is determined that the system is in an active correction process rather than an unstable state. Subsequently, the trigger delay time of VDC is dynamically extended, for example from the standard 200ms to 500-800ms, to prevent unnecessary stability intervention caused by brief deviations and improve the system's fault tolerance and control continuity.

[0039] The three steps described above together constitute the core implementation method of the dynamic activation threshold adjustment mechanism, enabling multi-dimensional adaptive adjustment of the yaw rate threshold, lateral acceleration threshold, and trigger delay time. By integrating key factors such as vehicle speed, road conditions, task urgency, and trajectory consistency, the system can intelligently tolerate highly dynamic operations while ensuring safety, reducing unnecessary interventions and improving control flexibility and trajectory tracking capabilities in complex scenarios.

[0040] Furthermore, the aforementioned intelligent driving domain controller calculates the target yaw moment based on the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold. Based on the target yaw moment, it generates a braking force command. In specific implementation, this may include the following steps 2.1 and 2.2: Step 2.1: The intelligent driving domain controller calculates the target yaw moment based on the target yaw moment and the actual vehicle yaw moment, using the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold as constraints.

[0041] In one implementation, the calculated yaw rate threshold, lateral acceleration threshold, and trigger delay time after the yaw rate continuously exceeds the threshold are first used as constraints. The target yaw moment is calculated with the objective function being the minimization of the target yaw rate and the actual vehicle yaw rate. Specifically, it can be calculated using PID control based on the deviation edPsi between the target yaw rate and the actual yaw rate. Mz_target = Kp*edPsi + Ki*(integral of edPsi) + Kd*(differential of edPsi) Where Mz_target is the target yaw moment; edPsi is the deviation between the target yaw angular velocity and the actual yaw angular velocity; Kp is the proportional coefficient; ki is the integral coefficient; and Kd is the differential coefficient.

[0042] Step 2.2: Perform four-wheel braking force distribution processing based on the target yaw moment, and generate braking force commands corresponding to each wheel.

[0043] First, when distributing the torque, the target yaw moment can be allocated to each wheel using the following calculation method: Mz_target = Σ(Fi × li) Where Mz_target is the target yaw moment; Fi is the longitudinal braking force of the i-th wheel (front left, front right, rear left, rear right), including the front left braking force F_fl, the front right braking force F_fr, the rear left braking force F_rl, and the rear right braking force F_rr; li is the lateral lever arm (a constant, determined by the vehicle parameters) from the i-th wheel to the vehicle's center of gravity.

[0044] Furthermore, upon receiving a braking force command, the aforementioned integrated braking control system, assuming no system malfunctions, disables its original vehicle dynamic control and traction control calculation modules and executes the braking force command from the intelligent driving domain controller. In specific implementation, this may include the following steps 3.1 to 3.3: Step 3.1: The integrated braking control system receives braking force commands for the four wheels from the intelligent driving domain controller.

[0045] The OneBox receives braking force commands from all four wheels, meaning it obtains target braking force information for each wheel independently set by the Intelligent Driving Domain Controller (ADDC) via a high-speed communication bus.

[0046] In one example, the ADDC sends data frames containing the target braking forces for the four wheels (left front, right front, left rear, and right rear) to the OneBox via the CAN FD bus (communication identifier ID 0x1F8) at 10ms intervals. The OneBox's communication module listens to this message channel in real time, parses the required braking force for each wheel, and transmits the data to the internal arbitration and execution unit as input for subsequent braking actions.

[0047] Step 3.2: Disable the output path of the local vehicle dynamic control and traction control calculation module in the absence of system abnormalities, so as to shield the original vehicle dynamic control and traction control calculation module inside it.

[0048] Disabling the local VDC / TCS output path means, under the premise of confirming normal system operation, actively cutting off the control authority of the original stability control algorithm inside OneBox over the actuators to prevent conflicts between local logic and external commands. In specific implementation, after OneBox receives an ADDC command, its internal arbitration module first performs fault diagnosis to determine if there are system anomalies such as wheel speed sensor failure, abnormal hydraulic pressure, or communication interruption. If no anomaly is detected, it immediately sets to the "takeover" state, shutting down the output switches of the local VDC and TCS control loops, preventing them from generating independent intervention commands. This achieves shielding of the original control logic and ensures the uniqueness and dominance of the ADDC commands.

[0049] Step 3.3: Convert the received braking force command into a braking pressure control signal to drive the four-wheel braking actuator unit to operate.

[0050] Converting the signal to brake pressure control refers to generating a control quantity that can drive the electronic control unit to precisely adjust the hydraulic pressure based on the target braking force value, the current wheel cylinder characteristics, and the braking system gain model. In practice, OneBox's brake execution module maps the target braking force of each wheel to the corresponding wheel cylinder target pressure and adjusts the braking pressure of each wheel through a proportional valve control strategy, enabling the actual braking force to quickly track the command value. The pressure build-up process is regulated by closed-loop feedback and fine-tuned in conjunction with wheel speed changes to ensure accurate output of braking force across all four wheels, ultimately achieving the desired yaw moment control effect.

[0051] The three steps described above collectively achieve the functional reconfiguration and command priority execution mechanism of the integrated braking control system in intelligent driving mode. By receiving external braking force commands, disabling local control outputs, and converting commands into actual braking actions, the unified scheduling authority of the intelligent driving domain controller over vehicle stability control is ensured. Overall, this method effectively avoids intervention conflicts and unexpected exits caused by decentralized control in traditional architectures, improves system coordination and control continuity, and enhances the stable execution capability of high-level intelligent driving in complex dynamic scenarios.

[0052] Furthermore, when the vehicle's yaw rate, trajectory deviation, and steering torque meet preset conditions, the intelligent driving domain controller gradually reduces the amplitude of the braking force command output to the external actuators until it stops outputting. In specific implementation, the intelligent driving domain controller determines whether the following conditions are met simultaneously: 1. The vehicle yaw rate is below a first preset threshold and remains below it for a first preset time. Vehicle yaw rate characterizes the dynamic intensity of the vehicle body's rotation around its vertical axis and is a crucial indicator of vehicle stability. Being below the first preset threshold and remaining below it for a first preset time means the system needs to confirm that the vehicle has returned to a stable operating state, rather than achieving instantaneous stabilization. In practice, the ADDC receives yaw rate data from the inertial measurement unit (IMU) in real time. When it detects that the value is below a preset stabilization threshold (e.g., 0.15 rad / s) and remains in this state for more than a first preset time (e.g., 2 seconds), it determines that the vehicle attitude has stabilized and meets the basic conditions for exiting intervention.

[0053] 2. The deviation between the actual driving position and the planned path is less than a second preset threshold and remains so for a second preset time. The deviation is used to characterize the lateral error between the vehicle's current driving trajectory and the target path, thereby assessing whether path tracking performance has recovered. In one example, ADDC integrates high-precision positioning and perception information, continuously calculates the lateral deviation, and when the deviation converges to within the allowable tolerance range (e.g., 0.1m) and remains so for more than the second preset time (e.g., 2 seconds), the trajectory control is considered to have returned to normal, meeting the spatial consistency requirement for exiting control intervention.

[0054] 3. The torque applied to the steering system exceeds a third preset threshold and remains so for a third preset time. The torque applied to the steering system, i.e., the input torque applied by the driver through the steering wheel, is used to identify the driver's intention to take over control. Exceeding the third preset threshold and remaining so for a third preset time is to avoid misinterpreting minor disturbances as takeover behavior. In one specific embodiment, the ADDC collects the steering wheel torque sensor signal. When the detected torque exceeds the identifiable threshold (e.g., 2 N·m) and remains so for more than the third preset time (e.g., 3 seconds), it is confirmed that the driver has actively intervened in the control and possesses takeover capability, and the system can safely release control.

[0055] After all conditions are met, the amplitude of the braking force command for each wheel is gradually reduced according to a preset attenuation law, reducing it to zero within a preset time period. The preset attenuation law refers to gradually reducing the braking force output using a linear or smooth curve to avoid sudden braking actions that could cause ride discomfort or sudden vehicle swaying. The preset time period is also the transition window for the transfer of control (e.g., 300ms). In practice, ADDC initiates a gradual release process, linearly reducing the target braking force command for each of the four wheels from its current value to zero according to a time ratio, and simultaneously sending a "control release" signal to OneBox. During this process, the vehicle status is continuously monitored, and the process is paused and exited if any abnormal fluctuations occur. After the zeroing is completed, the local VDC / TCS function automatically recovers and enters normal monitoring mode.

[0056] The aforementioned exit mechanism uses multi-dimensional state joint determination to ensure that the exit process is initiated only when the vehicle is dynamically stable, the trajectory has returned, and the driver is ready to take over. It also employs a gradual command decay strategy to achieve a smooth transition. Overall, this method effectively avoids abrupt exits or control oscillations caused by single-condition triggers in traditional systems, improving the safety, comfort, and human-machine collaboration level of the intelligent driving system at the end of the task.

[0057] In an optional implementation, the method further includes steps 4.1 to 4.4: Step 4.1: The intelligent driving domain controller receives feedback from the integrated braking control system on the actual wheel braking force, wheel speed, and vehicle yaw rate.

[0058] Receiving feedback information refers to the Intelligent Driving Domain Controller (ADDC) acquiring actual response data from the execution end via a high-speed communication link to achieve closed-loop control monitoring. Specifically, after executing the four-wheel braking force command from the ADDC, OneBox periodically (e.g., every 10ms) transmits the actual wheel braking force, wheel speed, and actual yaw rate of the vehicle body obtained from its internal sensors or estimation modules back to the ADDC via the CAN FD bus. This process constructs a complete communication loop from command issuance to execution feedback, providing a data foundation for subsequent control accuracy evaluation and model correction.

[0059] Step 4.2: Determine the actual yaw moment based on wheel speed, wheel rotation speed and vehicle body yaw rate.

[0060] In one implementation, the corrective yaw moment, i.e. the actual yaw moment, can be derived from the actual motion state of the vehicle based on the actual braking force distribution.

[0061] Mz=(Fy,FL+Fy,FR) lf (Fy,RL+Fy,RR) lr+(Fx,FL Fx,FR) ly / 2+(Fx,RL Fx,RR) ly / 2.

[0062] Where Fy: tire lateral force; Fx: tire longitudinal force; FL\FR\RL\RR: represent the left front, right front, left rear, and right rear wheels respectively; Lf: distance from the center of gravity to the front axle; lr: distance from the center of gravity to the rear axle; Ly: left and right track width.

[0063] ADDC calculates the slip difference between the left and right wheels by feeding back the front and rear axle wheel speed differences, combines this with tire mechanical characteristics to make a preliminary estimate of the lateral force contribution of each wheel, and integrates the measured trend of the vehicle's yaw rate change to calculate the actual yaw moment generated by the entire vehicle through a dynamic inverse model. This process does not rely on ideal model assumptions but is based on reconstruction of the actual response, which can accurately reflect the control execution effect.

[0064] Step 4.3: Calculate the torque difference between the actual yaw moment and the target yaw moment.

[0065] The torque difference is used to quantify the tracking accuracy of the control system and is a key criterion for determining whether model adjustments are needed. In practice, ADDC compares the actual yaw moment calculated in the previous step with the target yaw moment generated by its own planning cycle by cycle to obtain the deviation between the two. This torque difference reflects the control error caused by factors such as changes in road conditions, tire aging, or model mismatch, and serves as the trigger input for the online correction mechanism.

[0066] Step 4.4: If the absolute value of the torque difference is greater than the preset deviation threshold, then update the tire lateral stiffness parameter or the predicted control time domain length in the control model.

[0067] Updating control model parameters refers to adaptively adjusting the core model for calculating the yaw moment of the supporting target when a significant control deviation is detected. Specifically, when the absolute value of the torque difference exceeds a preset deviation threshold (e.g., 15%) over multiple consecutive cycles, the system initiates an online correction mechanism: if the deviation manifests as response lag or insufficient gain, the tire lateral stiffness parameters are dynamically adjusted to match the current adhesion state; if it manifests as severe deviation from the predicted trajectory, the prediction time domain length of the model predictive control (MPC) is appropriately shortened or extended to improve short-term response capability or long-term stability. This process requires no manual intervention and can continuously optimize control performance during actual driving.

[0068] The four steps described above constitute a complete dynamic closed-loop correction and error compensation mechanism. By collecting execution feedback in real time, reconstructing the actual yaw moment, identifying control deviations, and adjusting key model parameters online, adaptive tracking of tire-road interaction characteristics and continuous optimization of the control model are achieved. Overall, this method effectively improves the robustness and control accuracy of the system under different operating conditions, reduces trajectory drift and stability degradation caused by model mismatch, and enhances the autonomous adjustment capability and engineering practicality of the intelligent driving domain control system in complex environments.

[0069] Furthermore, the method also includes steps 5.1 to 5.4: Step 5.1: The integrated braking control system monitors the hydraulic pressure of the braking system, wheel-end sensor signals, and the communication status between controllers.

[0070] Monitoring system status refers to OneBox's real-time health diagnostics of its critical functional modules and external interaction links to identify abnormal operating conditions that may affect control safety. Specifically, OneBox continuously collects master cylinder hydraulic pressure, wheel cylinder pressure, and wheel speed sensor output signals through its built-in diagnostic unit, and monitors whether communication messages between the CAN FD bus (e.g., ID 0x1F8) and the Intelligent Driving Domain Controller (ADDC) are received normally and whether the cycle is stable. When pressure establishment fails, wheel speed signals are interrupted, or valid command frames are not received for multiple consecutive cycles, a system-level risk is identified, triggering the fault response process.

[0071] Step 5.2: When abnormal hydraulic pressure, loss of wheel end sensor signal, or communication interruption is detected, disable the input of braking force command from the intelligent driving domain controller.

[0072] Disabling external command input means cutting off the response path to braking force commands issued by the ADDC when an execution or sensing failure is confirmed, preventing safety hazards caused by erroneous data or out-of-control commands. In specific implementation, once any of the above anomalies is confirmed (such as hydraulic pressure drop exceeding a threshold, no speed feedback from a wheel, or communication interruption lasting more than 200ms), OneBox immediately enters safety protection mode, shutting down the function channel for receiving and executing external braking force commands, ensuring that subsequent control behavior does not rely on unreliable input.

[0073] Step 5.3: Start the local vehicle dynamic control and traction control calculation module.

[0074] Activating the local VDC / TCS computing module means restoring OneBox's original independent stability control capabilities, enabling autonomous intervention in the event of a loss of upper-level collaborative control. Specifically, OneBox activates its internally pre-built VDC and TCS control algorithms. Based on locally acquired signals such as wheel speed, yaw rate, and acceleration, it determines whether anti-slip control or yaw stabilization intervention is needed according to traditional fixed-threshold logic, and autonomously generates corresponding braking force adjustment commands to ensure the vehicle retains basic dynamic stability in emergency situations.

[0075] Step 5.4: Send system degradation status information to the intelligent driving domain controller.

[0076] Sending system degradation status information refers to reporting the current control switch event and fault type to ADDC so that the intelligent driving system can adjust its operating strategy in a timely manner. In practice, OneBox sends a "system degradation" or "control takeover" status flag to ADDC through a preset diagnostic message, notifying it that it has exited the cooperative control mode. After receiving this information, ADDC can trigger an intelligent driving function degradation or exit prompt, reminding the driver to take over, thus forming a complete fault response closed loop.

[0077] The above four steps together constitute a fault emergency handling mechanism oriented towards functional safety. By monitoring the critical status of the system in real time, external command dependencies are promptly cut off when execution, sensing, or communication anomalies are detected, local stability control is restored, and degraded status is proactively reported, achieving a seamless switch from collaborative control to independent operation.

[0078] This application also provides a specific implementation method, see [link to specific implementation method]. Figure 2 As shown, it mainly includes the following parts: 1. System Startup and Intelligent Driving Activation When the driver activates L2+ or higher level intelligent driving functions (such as highway assist or automatic lane change), the ADDC (Intelligent Driving Domain Controller) sends a "Intelligent Driving Mode Activation" signal to the OneBox controller, triggering the system to enter the cooperative control state. At this time, the original VDC / TCS independent control logic inside the OneBox is automatically put into a "suspended" state, but sensor data continues to be collected and uploaded to the ADDC.

[0079] 2. Multi-source sensing and state fusion ADDC receives real-time data streams from onboard sensors, including: vehicle speed (from wheel speed sensors), yaw rate and lateral acceleration (from IMU), tire slip ratio (calculated from wheel speed difference), road adhesion coefficient (estimated based on longitudinal acceleration and tire slip ratio), and path planning results (from high-precision map + perception module). Through Kalman filtering and multi-sensor fusion algorithms, it generates high-precision real-time estimates of the vehicle's dynamic state.

[0080] 3. Dynamic threshold adaptive adjustment ADDC has a built-in scene-adaptive threshold database. Based on the current vehicle speed, road surface adhesion coefficient (μ), obstacle avoidance urgency (dynamically assessed by the relative speed and distance to obstacles), and trajectory deviation (lateral error between the desired path and the actual path), it automatically looks up and matches the data, dynamically adjusting the VDC / TCS trigger threshold. For example: • When the vehicle speed is >80 km / h and μ < 0.4, the yaw rate threshold increases from 0.8 rad / s to 1.3 rad / s; • When the obstacle avoidance urgency level is "high", the lateral acceleration threshold increases from 0.4g to 0.65g; • When the trajectory deviation is >0.3m and lasts for 1s, the upper limit of the slip ratio tolerance is relaxed from 15% to 25%.

[0081] 4. Calculation and distribution of target yaw moment The VDC function, moved up to ADDC, minimizes the error between the target yaw rate and the actual vehicle yaw rate, outputs the optimal target yaw moment Mz, and decomposes it into the target braking force of the four wheels, achieving efficient and asymmetric stability control.

[0082] 5. OneBox Arbitration and Orders Priority The OneBox controller receives the four-wheel target braking force command (cycle 10ms) issued by ADDC. Its arbitration module has a higher priority than the local VDC / TCS calculation unit. If there is no braking system fault (such as abnormal pressure or wheel speed sensor failure), the arbitration module immediately disables the local VDC / TCS control output, forcibly executes the ADDC command, and feeds back the "takeover" status to ADDC.

[0083] 6. Dynamic closed-loop correction and error compensation ADDC continuously receives actual braking force, wheel speed, and yaw rate data from OneBox and compares them with target values ​​in real time. If the actual Mz deviates from the target Mz by more than ±15%, the system automatically triggers an online model correction mechanism to adjust the tire side stiffness parameter or prediction time domain length of the MPC, improving control accuracy. This process requires no manual calibration and is continuously optimized during actual driving.

[0084] 7. System smooth exit mechanism When the vehicle completes obstacle avoidance, restores its trajectory to the planned path, and meets the following three conditions: • Yaw rate ≤ 0.15 rad / s (lasting ≥ 2 seconds); • The lateral displacement error of the vehicle is ≤ 0.1m (lasting ≥2 seconds); •Driver's steering wheel torque ≥ 2 N•m (lasting ≥ 3 seconds); ADDC start-up and exit process: Gradually and linearly reduce the braking force of all four wheels, reducing it to zero within 300ms, and send a "Release Control" signal to OneBox. At this time, the local VDC / TCS logic automatically reverts to normal mode.

[0085] 8. Fail-safe mechanisms If OneBox detects an abnormality in the braking system (such as a drop in hydraulic pressure or loss of wheel speed signal), or if the CAN FD bus communication is interrupted for more than 200ms, the arbitration module immediately switches to the local VDC / TCS default control strategy and sends a "system degradation" alarm to ADDC to ensure driving safety.

[0086] This application also provides an intelligent driving cooperative control system that implements any of the methods described in the foregoing embodiments. See [link to relevant documentation]. Figure 3 As shown, the system includes: an intelligent driving domain controller (ADDC), an integrated braking control system (OneBox), a multi-source sensing unit, and a four-wheel braking actuator. The modules complete the vehicle dynamic stability control task through functional division and communication interaction.

[0087] The intelligent driving domain controller is deployed at the central layer of the vehicle's electronic and electrical architecture to integrate perception, planning, and chassis control decisions. This controller is equipped with multiple functional modules: a stability boundary prediction module receives vehicle speed, road adhesion coefficient estimation results, path planning trajectory, and tire model parameters, calculates the maximum tolerable yaw rate, lateral acceleration, and tire slip ratio under the current operating conditions, and generates a predictive output regarding the intervention capability of the integrated braking control system; a trigger condition adjustment module, based on the input vehicle speed information, the relative distance and relative speed of obstacles provided by the environmental perception module, the vehicle state fed back by the inertial measurement module, and the lateral deviation between the actual driving trajectory and the planned path, looks up a pre-calibrated scene-threshold mapping table and dynamically generates yaw rate thresholds, lateral acceleration thresholds, and delayed intervention time parameters; and a target yaw moment generation module... The allocation module calculates the required target yaw moment based on path tracking error and vehicle dynamics. Combining the vertical load and slip state of each wheel, it uses a nonlinear allocation strategy to decompose the moment into braking force commands for the four wheels: left front, right front, left rear, and right rear. The command sending module packages the above four-wheel braking force commands into a fixed-format data frame and periodically sends it to the integrated braking control system via a high-bandwidth vehicle communication bus. The exit judgment and execution module acquires the vehicle yaw rate, trajectory deviation, and steering wheel torque signal in real time. When all three meet the preset stable operation and takeover intention judgment conditions, it initiates a gradual decay process of the braking force command amplitude and resets the commands of each wheel to zero within a set time period.

[0088] The integrated braking control system is equipped with an arbitration processing module. Upon receiving braking force commands from the intelligent driving domain controller, and assuming no system anomalies, it disables the original vehicle dynamics control and traction control calculation modules and executes the braking force commands. The integrated braking control system is an electro-hydraulic braking device with a built-in arbitration processing module. This module receives four-wheel braking force command data frames from the intelligent driving domain controller. Assuming no braking system fault, it closes the output paths of the local vehicle dynamics control (VDC) and traction control (TCS) calculation modules, preventing local control logic from affecting the actuators. Simultaneously, it converts the received external braking force commands into corresponding wheel cylinder pressure control targets, driving the four-wheel braking actuators. When an abnormal hydraulic pressure is detected, a wheel sensor signal is lost, or communication interruption with the ADDC persists for more than a specified duration, the arbitration processing module switches to local control mode and activates the internal VDC / TCS algorithm for independent intervention.

[0089] The multi-source sensing unit includes: a vehicle speed detection module, consisting of wheel speed sensors for four wheels, used to collect the rotation frequency of each wheel and calculate the linear velocity at the wheel end; an inertial measurement module, installed near the vehicle's center of gravity, used to output motion parameters such as yaw rate and lateral acceleration in real time; a steering input detection module, integrated into the steering column, used to detect the steering wheel torque applied by the driver; and an environmental perception module, consisting of a forward-facing millimeter-wave radar, a monocular / dual-lens camera, and a high-precision positioning system, used to identify the position and relative motion of obstacles ahead, as well as the vehicle's precise pose on the map.

[0090] The four-wheel brake actuator is used to respond to the brake pressure commands output by the integrated brake control system. The four-wheel brake actuator is an electro-hydraulic actuator integrated inside the OneBox, which includes a proportional valve, accumulator and pump assembly. It can independently adjust the brake pressure of each wheel according to the control signal generated by the arbitration processing module to achieve differential braking force output.

[0091] In an optional implementation, the intelligent driving domain controller and the integrated braking control system are connected via a high-bandwidth vehicle communication bus. This high-bandwidth vehicle communication bus periodically transmits braking force commands, system status information, and fault alarm information. The intelligent driving domain controller and the integrated braking control system are connected via a high-bandwidth vehicle communication bus using the CANFD protocol, with a communication cycle of 10ms. The transmitted content includes four-wheel braking force commands, actual braking force and wheel speed data fed back from the OneBox, system operating mode flags, and fault diagnosis codes.

[0092] The components of the above system interact and collaborate in accordance with their defined functional responsibilities to complete a closed-loop control process from path planning to stable execution.

[0093] This application also provides a vehicle equipped with the intelligent driving cooperative control system described above. The vehicle is a passenger car or commercial vehicle with L2 or higher level intelligent driving functions. Its electronic and electrical architecture adopts a domain-centralized design, including an intelligent driving domain controller (ADDC), an integrated braking control system (OneBox), multi-source sensing units, four-wheel braking actuators, and a human-machine interaction system. The modules of the intelligent driving cooperative control system are physically integrated with the vehicle communication network via hardwired connections. The ADDC is installed in the central control compartment and establishes a high real-time communication link with the OneBox via a CAN FD bus, with a communication cycle of 10ms, used to transmit four-wheel braking force commands, system status feedback, and fault alarm information.

[0094] Millimeter-wave radar and vision sensors are arranged at the front and sides of the vehicle, and a high-precision positioning device (such as GNSS / IMU integrated navigation) is installed on the top or behind the windshield, forming an environmental perception module; an inertial measurement module is located in the center of gravity area of ​​the vehicle body, and all four wheels are equipped with high-precision wheel speed sensors; the steering wheel and steering column integrate a torque detection unit. The above devices together form a multi-source sensing unit and upload the data to ADDC in real time.

[0095] The OneBox is integrated into the engine compartment or chassis area, housing a hydraulic adjustment unit and electronically controlled valve assembly. It connects to the brake calipers of all four wheels, forming a four-wheel braking actuator. Internally, it runs local VDC and TCS control logic and includes an arbitration processing module for switching control modes under different operating conditions.

[0096] When the driver activates advanced driver assistance functions such as intelligent cruise control, automatic lane change, or emergency obstacle avoidance, the system enters a cooperative control state. Based on the received sensor data and path planning results, ADDC performs stability boundary prediction, dynamic threshold adjustment, target yaw moment calculation and distribution, and issues four-wheel braking force commands. Under fault-free conditions, OneBox disables local computation output and executes external commands; in abnormal situations, it autonomously takes over control and reports a degraded status. The control process continues until the exit conditions are met, at which point ADDC gradually releases the braking force commands, completing the transfer of control.

[0097] Through the aforementioned system configuration, the vehicle achieves a deep integration of intelligent driving functions and chassis stability control, supporting continuous control and safe exit mechanisms in highly dynamic scenarios.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A collaborative control method for vehicle dynamic stability based on intelligent driving, characterized in that, When the intelligent driving function is activated, the core control logic of vehicle dynamic control and traction control is migrated to the intelligent driving domain controller for execution; including: The intelligent driving domain controller adjusts the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold based on at least one of the following parameters: vehicle speed, road surface adhesion coefficient, obstacle avoidance urgency, and the degree of trajectory deviation between the actual driving trajectory and the planned path. The intelligent driving domain controller calculates the target yaw moment based on the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold. It generates braking force commands based on the target yaw moment and periodically sends the braking force commands to the integrated braking control system through a high-speed communication interface. Upon receiving the braking force command, the integrated braking control system, in the absence of system malfunctions, disables its original vehicle dynamic control and traction control calculation modules and executes the braking force command from the intelligent driving domain controller. When the vehicle's yaw rate, the degree of trajectory deviation, and the torque applied to the steering device meet preset conditions, the intelligent driving domain controller gradually reduces the amplitude of the braking force command output to the external actuators until it stops outputting.

2. The vehicle dynamic stability cooperative control method based on intelligent driving according to claim 1, characterized in that, The intelligent driving domain controller adjusts the yaw rate threshold, lateral acceleration threshold, and trigger delay time after the yaw rate continuously exceeds the threshold based on at least one of the following parameters: vehicle speed, road surface adhesion coefficient, obstacle avoidance urgency, and trajectory deviation between the actual driving trajectory and the planned path. This includes: Determine the target yaw rate threshold based on the current vehicle speed and road surface adhesion coefficient; The urgency of obstacle avoidance is determined based on the relative velocity and relative distance to the obstacle, and the target lateral acceleration threshold is determined based on the urgency of obstacle avoidance. The deviation between the actual driving trajectory and the planned path is obtained in real time. If the deviation exceeds a preset distance threshold and the duration exceeds a preset time threshold, the delayed intervention time will be extended.

3. The vehicle dynamic stability cooperative control method based on intelligent driving according to claim 1, characterized in that, The intelligent driving domain controller calculates the target yaw moment based on a yaw rate threshold, a lateral acceleration threshold, and a trigger delay time after the yaw rate continuously exceeds the threshold. Based on the target yaw moment, it generates braking force commands, including: The intelligent driving domain controller calculates the target yaw moment based on the target yaw moment and the actual vehicle yaw moment, using the yaw rate threshold, the lateral acceleration threshold, and the trigger delay time after the yaw rate continuously exceeds the threshold as constraints. Based on the target yaw moment, the four-wheel braking force is distributed, and a braking force command corresponding to each wheel is generated.

4. The vehicle dynamic stability cooperative control method based on intelligent driving according to claim 1, characterized in that, Upon receiving the braking force command, the integrated braking control system, assuming no system malfunctions, disables its original vehicle dynamic control and traction control calculation modules and executes the braking force command from the intelligent driving domain controller, including: The integrated braking control system receives braking force commands for all four wheels from the intelligent driving domain controller. In the absence of system anomalies, disable the output path of the local vehicle dynamics control and traction control calculation module to shield its original vehicle dynamics control and traction control calculation module. The received braking force command is converted into a braking pressure control signal, which drives the four-wheel braking actuator to operate.

5. The vehicle dynamic stability cooperative control method based on intelligent driving according to claim 1, characterized in that, When the vehicle's yaw rate, the degree of trajectory deviation, and the torque applied to the steering device meet preset conditions, the intelligent driving domain controller gradually reduces the amplitude of the braking force command output to the external actuators until it stops outputting, including: The intelligent driving domain controller determines whether the following conditions are met simultaneously: The vehicle's yaw rate is lower than a first preset threshold and remains below a first preset time; The deviation between the actual driving position and the planned path is less than a second preset threshold and continues for a second preset time; The torque applied to the steering device exceeds a third preset threshold and remains so for a third preset time; After all conditions are met, the amplitude of the braking force command for each wheel is gradually reduced according to the preset attenuation law; Within a preset time period, the braking force command amplitude of each wheel is reduced to zero.

6. The vehicle dynamic stability cooperative control method based on intelligent driving according to claim 1, characterized in that, The method further includes: The intelligent driving domain controller receives feedback from the integrated braking control system on the actual wheel braking force, wheel speed and vehicle yaw rate. The actual yaw moment is determined based on the wheel speed, the wheel rotation speed, and the vehicle body yaw rate. Calculate the torque difference between the actual yaw moment and the target yaw moment; If the absolute value of the torque difference is greater than the preset deviation threshold, then update the tire lateral stiffness parameter or the predicted control time domain length in the control model.

7. The vehicle dynamic stability cooperative control method based on intelligent driving according to claim 1, characterized in that, The method further includes: The integrated braking control system monitors the hydraulic pressure of the braking system, wheel-end sensor signals, and the communication status between controllers. When abnormal hydraulic pressure, loss of wheel-end sensor signal, or communication interruption is detected, the input of braking force command from the intelligent driving domain controller is disabled. Start the local vehicle dynamics control and traction control calculation module; Send system degradation status information to the intelligent driving domain controller.

8. An intelligent driving cooperative control system implementing the method as described in any one of claims 1 to 7, characterized in that, include: The intelligent driving domain controller is equipped with a stability boundary prediction module, a trigger condition adjustment module, a target yaw moment generation and allocation module, a command sending module, and an exit determination and execution module. The integrated braking control system is equipped with an arbitration processing module, which, upon receiving a braking force command from the intelligent driving domain controller, disables the original vehicle dynamic control and traction control calculation modules within the system in the absence of system anomalies, and executes the braking force command. The multi-source sensing unit includes a vehicle speed detection module, an inertial measurement module, a steering input detection module, and an environmental perception module; The four-wheel brake actuator is used to respond to the brake pressure command output by the integrated brake control system.

9. The system according to claim 8, characterized in that, The intelligent driving domain controller and the integrated braking control system are connected via a high-bandwidth vehicle communication bus, which periodically transmits braking force commands, system status information, and fault alarm information.

10. A vehicle, characterized in that, The vehicle is equipped with the intelligent driving cooperative control system as described in claim 8 or 9.

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