Vehicle transverse stability coordination control system based on sensing fusion

Abstract

The invention relates to the technical field of vehicle engineering, and discloses a vehicle transverse stability coordination control system based on sensing fusion, which comprises a main controller, a sensing fusion coprocessor and a transverse control coprocessor. The main controller calls and sends a stability control instruction according to the driving state; the sensing fusion coprocessor schedules a transverse control instruction in multi-source information fusion; the transverse control coprocessor analyzes the parameters to obtain basic control signals, dynamically monitors transverse acceleration, yaw velocity and tire slip angles, generates and outputs compensation signals through mode adjustment such as dynamic coordination function bypass selection and global or local parameter dynamic coordination, and controls an actuator after being fused with the basic signals. The system integrates a data fusion module, a state evaluation module and a multi-strategy dynamic adjustment module, multi-source information accurate fusion and multi-parameter cooperative control are achieved, the transverse stability and driving safety of the vehicle under the complex working condition are improved, and the method is suitable for the field of vehicle engineering control.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle engineering, and particularly to a vehicle lateral stability coordinated control system based on sensor fusion. Background Art

[0002] With the rapid development of the automotive industry and the increasing demand for road transportation, the driving safety and stability of vehicles have become crucial technical issues. During vehicle driving, lateral stability is one of the key factors affecting vehicle safety. Vehicle lateral instability may lead to dangerous conditions such as sideslip and fishtailing, seriously threatening the lives of passengers and road traffic safety.

[0003] Traditional vehicle lateral stability control technologies mainly rely on single sensors or independent control systems, such as the Electronic Stability Program (ESP) based on wheel speed sensors. However, single sensors have problems such as incomplete information acquisition and insufficient reliability in complex working conditions, and it is difficult to accurately and real-time reflect the true driving state of the vehicle. At the same time, traditional control systems usually adopt fixed control strategies and cannot be flexibly adjusted according to the dynamic driving state of the vehicle and changes in the external environment. In the face of complex working conditions such as high-speed cornering, emergency obstacle avoidance, and slippery roads, the control effect is limited, and it is difficult to achieve precise control of vehicle lateral stability.

[0004] With the development of multi-sensor technology and intelligent control algorithms, the application of sensor fusion technology in the vehicle field has gradually received attention. Multi-sensor fusion can integrate multi-source information and provide a more comprehensive and accurate description of the vehicle state. However, there are still many technical challenges in how to effectively fuse multi-source information and how to achieve coordinated control of vehicle lateral stability based on the fused information. For example, the sampling periods of different sensors are different, resulting in timing deviations in multi-source signals and affecting the accuracy of information fusion. In complex working conditions, how to dynamically adjust the control strategy according to the real-time state of the vehicle to achieve coordinated control of multiple parameters such as lateral acceleration, yaw rate, and tire side slip angle to improve vehicle lateral stability is still a technical problem to be solved urgently.

[0005] In addition, in the process of generating and executing control commands of the existing vehicle lateral stability control system, there is a lack of effective cooperation between the main controller, the sensor fusion coprocessor, and the lateral control coprocessor. The main controller is difficult to flexibly retrieve the corresponding stability control commands according to the vehicle driving state. The sensor fusion coprocessor has insufficient scheduling ability for the lateral control coprocessor during the information fusion process. The lateral control coprocessor also cannot efficiently analyze control parameters and achieve dynamic coordinated monitoring and adjustment of multiple parameters, resulting in the response speed and control accuracy of the entire system being difficult to meet the requirements of modern vehicles for high safety and stability.

[0006] Therefore, it is of great practical significance and engineering application value to develop a vehicle lateral stability coordination control system that can effectively fuse multi-source sensor information, dynamically coordinate and monitor multiple control parameters, and enable efficient collaborative work among modules to improve the driving safety and stability of vehicles. Summary of the Invention

[0007] The purpose of the present invention is to provide a vehicle lateral stability coordination control system based on sensing fusion to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A vehicle lateral stability coordination control system based on sensing fusion, the system includes: A main controller, a sensing fusion co-processor, and a lateral control co-processor; The main controller is used to retrieve the corresponding stability control instruction according to the vehicle driving state and send the stability control instruction to the lateral control co-processor; The sensing fusion co-processor is used to schedule the lateral control instruction of the lateral control co-processor during the execution of the multi-source information fusion instruction; The lateral control co-processor is used to analyze the lateral control parameters in the stability control instruction to obtain the corresponding basic control signal; dynamically coordinate and monitor the lateral acceleration, yaw rate, and tire side slip angle, and select the corresponding coordination control mode for dynamic adjustment according to the monitoring results to obtain an output compensation signal, and combine the basic control signal to perform the control output to the actuator.

[0009] Preferably, the lateral control co-processor includes: A data fusion module, which is used to analyze multi-source sensor signals and convert the analyzed sensor signals into vehicle state parameters as the basic control signal; wherein, the sensor signals include steering angle signals, wheel speed signals, and inertial measurement signals; A state evaluation module, which is respectively deployed in the dynamic coordination module and the parameter correction module, and is used to respectively perform real-time evaluation on the dynamic coordination module and the parameter correction module to obtain a first evaluation result corresponding to the dynamic coordination module and a second evaluation result corresponding to the parameter correction module; A control decision module, which is used to select the corresponding coordination control mode for dynamic adjustment according to the first evaluation result and the second evaluation result through a configuration register to obtain an output compensation signal, and combine the basic control signal to perform the control output to the actuator.

[0010] Preferably, the main controller is further configured to retrieve a corresponding preset control parameter as a first target stability instruction from the data storage unit according to the first target stability task, and send the first target stability instruction to the lateral control coprocessor.

[0011] Preferably, the main controller is further configured to use the coordinated control algorithm based on multi-objective optimization as a second target stability instruction according to the second target stability task, and send the second target stability instruction to the lateral control coprocessor.

[0012] Preferably, the coordinated control mode includes: dynamic coordination function bypass, dynamic coordination of global parameters, and dynamic coordination of local parameters.

[0013] Preferably, the control decision module includes: A dynamic adjustment module, configured to select coordinated control strategies corresponding to the dynamic coordination module and the parameter correction module respectively according to the first evaluation result and the second evaluation result for dynamic adjustment to obtain the output compensation signal; wherein, the coordinated control strategies include a lateral torque distribution strategy, a yaw moment correction strategy, and a lateral force compensation strategy; A signal synthesis module, configured to perform a weighted fusion operation on the basic control signal and the output compensation signal to obtain a final control signal; An execution output module, configured to output the final control signal to the actuator.

[0014] Preferably, the dynamic adjustment module includes: A first correction module, configured to superimpose a correction signal within the current control cycle when it is detected that the lateral acceleration corresponding to the basic control signal exceeds a threshold, so as to obtain a corrected total control signal as a first target output compensation signal; A second correction module, configured to perform non-linear correction according to a side slip angle compensation table to obtain a second target output compensation signal.

[0015] Preferably, the first correction module includes: A steady-state coordination module, configured to determine whether the deviation between the current yaw rate and the target yaw rate exceeds an allowable range when it is detected that the vehicle is in steady-state steering. If it exceeds, the lateral torque distribution strategy is corrected once; if not, no correction is made; A transient coordination module, configured to determine whether the difference between the tire side slip angle and the preset side slip angle exceeds a safety threshold before the start of a single lane change action when it is detected that the vehicle is in transient lane change. If it exceeds, the yaw moment correction strategy is corrected once; if not, no correction is made; The extreme condition module is used to dynamically adjust the lateral force compensation strategy in combination with the tire slip ratio data when it is detected that the vehicle is on a low - adhesion road surface, and to make a primary correction to the compensation parameters when the slip ratio exceeds the critical value; The single - parameter adjustment module is used to locally correct the control strategy corresponding to the parameter within the control period when a single parameter is detected to be abnormal, and the remaining parameters maintain the original control strategy.

[0016] Preferably, the second correction module includes: The compensation configuration module is used to activate the sideslip angle compensation function for specific working conditions; wherein, the specific working conditions include high - speed cornering, emergency obstacle avoidance, and wet road surfaces, and each of the working conditions is configured with a sideslip angle compensation parameter register and a compensation action range register; The parameter recording module is used to record the control parameter change information of each compensation point under the specific working conditions in real time after the compensation function is activated; And the non - linear compensation module is used to perform a non - linear compensation according to the change information when the control target is achieved within the specified range of the specific working condition.

[0017] Preferably, the data fusion module further includes a time synchronization unit for aligning the sampling periods of different sensors to eliminate the timing deviation of multi - source signals.

[0018] Compared with the prior art, the beneficial effects of the present invention are: The vehicle lateral stability coordination control system based on sensor fusion provided by the present invention realizes the efficient fusion of multi - source sensor information and the precise control of vehicle lateral stability through the collaborative work of the main controller, the sensor fusion co - processor, and the lateral control co - processor, significantly improving the driving safety and stability of the vehicle under complex working conditions.

[0019] The main controller can flexibly retrieve the corresponding stability control instructions according to the vehicle driving state and send them to the lateral control co - processor. This design enables the system to quickly generate and transmit corresponding control instructions for different driving conditions and control tasks, improving the response speed and flexibility of the system. For example, retrieving the preset control parameters as the first target stability instruction according to the first target stability task, and using the coordinated control algorithm based on multi - objective optimization as the second target stability instruction according to the second target stability task, ensuring that the system can adopt appropriate control strategies in different scenarios.

[0020] When the sensing fusion coprocessor executes the multi-source information fusion instruction, it schedules the lateral control instruction of the lateral control coprocessor. The multi-source sensor signals (including steering angle signal, wheel speed signal, inertial measurement signal, etc.) are analyzed by the data fusion module and converted into vehicle state parameters as the basic control signals. At the same time, the time synchronization unit in the data fusion module aligns the sampling periods of different sensors, eliminates the timing deviation of the multi-source signals, improves the accuracy and reliability of information fusion, and provides a solid foundation for subsequent control decisions.

[0021] The design of the lateral control coprocessor is one of the core highlights of the present invention. Its state evaluation modules are respectively deployed in the dynamic coordination module and the parameter correction module to evaluate these two modules in real time and obtain the corresponding evaluation results, providing a basis for the control decision-making module to select the coordination control mode. According to the evaluation results, the control decision-making module selects the corresponding coordination control mode (including bypassing the dynamic coordination function, performing dynamic coordination of global parameters, and performing dynamic coordination of local parameters) through the configuration register for dynamic adjustment, and can flexibly respond to different working conditions. For example, the dynamic coordination function bypass can be selected under normal working conditions to reduce unnecessary control intervention; global or local parameter dynamic coordination is performed under complex working conditions to achieve precise control of the vehicle state.

[0022] The dynamic adjustment module selects the corresponding coordination control strategy (including lateral torque distribution strategy, yaw moment correction strategy, and lateral force compensation strategy) according to the evaluation results for dynamic adjustment to obtain the output compensation signal. Among them, the design of the first correction module and the second correction module further enhances the control ability of the system. The first correction module adopts corresponding correction strategies for different driving states (steady-state steering, transient lane change, low adhesion road surface, etc.) and parameter anomalies to achieve real-time correction of the control signal. For example, during steady-state steering, it is determined whether to correct the lateral torque distribution strategy by judging whether the yaw rate deviation exceeds the allowable range; during transient lane change, the yaw moment correction strategy is adjusted according to whether the difference between the tire side slip angle and the preset side slip angle exceeds the safety threshold; on a low adhesion road surface, the lateral force compensation strategy is dynamically adjusted in combination with the tire slip ratio data to ensure the vehicle can maintain stability under various working conditions. The second correction module activates the side slip angle compensation function for specific working conditions (such as high-speed cornering, emergency obstacle avoidance, and wet road surface) and performs non-linear compensation according to the real-time recorded control parameter change information, further improving the control accuracy and stability of the system under complex working conditions.

[0023] The signal synthesis module performs weighted fusion operation on the basic control signal and the output compensation signal to obtain the final control signal, ensuring the accuracy and effectiveness of the control signal. The execution output module outputs the final control signal to the actuator to achieve real-time control of the vehicle lateral stability. Brief Description of the Drawings

[0024] Figure 1 FIG. is the working principle diagram of the vehicle lateral stability coordination control system based on sensor fusion according to the present invention; Figure 2 FIG. is the working flow chart of the lateral control coprocessor; Figure 3 FIG. is the working flow chart of the control decision-making module; Figure 4 FIG. is the working flow chart of the dynamic adjustment module; Figure 5 FIG. is the working flow chart of the second correction module. Detailed Embodiment

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] Please refer to Figures 1 - 5 , the vehicle lateral stability coordination control system based on sensor fusion involved in the present invention includes: a main controller, a sensor fusion coprocessor, and a lateral control coprocessor. The specific steps are as follows: The core architecture of the system consists of a main controller, a sensor fusion coprocessor, and a lateral control coprocessor. As the decision-making center of the system, the main controller's core function is to retrieve the corresponding stability control instructions from the internal storage unit according to the real-time driving state of the vehicle and accurately send the instructions to the lateral control coprocessor. The "vehicle driving state" here covers multi-dimensional parameters such as vehicle speed, steering angle, and road surface adhesion coefficient. The main controller matches the control strategy corresponding to the current state through a preset logical judgment rule.

[0027] The sensor fusion coprocessor undertakes the key task of multi-source information fusion and is responsible for scheduling the lateral control instructions of the lateral control coprocessor during the execution of the fusion instructions. This module aligns the sampling periods of different sensors through a time synchronization unit to eliminate the timing deviation of multi-source signals, ensuring the consistency of signals from devices such as steering angle sensors, wheel speed sensors, and inertial measurement units in the time dimension and providing a reliable data basis for subsequent control.

[0028] The lateral control co-processor is the execution core of the system, and its processing flow is divided into three stages: First, it parses the lateral control parameters in the stability control instruction, such as the target value of lateral acceleration, the threshold value of yaw rate, etc., and generates a basic control signal; Second, it dynamically coordinates and monitors the lateral acceleration, yaw rate and tire sideslip angle, and real-time data of these three key parameters are collected through the built-in sensor interface; Finally, according to the monitoring results, the corresponding mode is selected from the preset coordinated control modes for dynamic adjustment, an output compensation signal is generated, and combined with the basic control signal to form the final actuator control instruction.

[0029] The present invention will be further described below in conjunction with Embodiments 1 to 5: Embodiment 1: In this embodiment, the lateral control co-processor includes a data fusion module, a state evaluation module and a control decision module. The data fusion module is responsible for parsing multi-source sensor signals, and the sensor signals include steering angle signals, wheel speed signals, inertial measurement signals, etc. Through a specific parsing algorithm, this module converts these original sensor signals into vehicle state parameters. For example, the signal collected by the steering angle sensor is parsed into the actual steering angle value, the signal of the wheel speed sensor is calculated into the linear velocity and angular velocity of each wheel, and the inertial measurement signal is converted into lateral acceleration, longitudinal acceleration and yaw rate, etc. These parameters after parsing and conversion are output to the control decision module as the basic control signal. It should be noted that the data fusion module is also provided with a time synchronization unit, whose function is to align the sampling periods of different sensors. Since the sampling frequencies of different sensors may vary, such as the wheel speed sensor may sample at a high frequency, while the sampling frequency of the inertial measurement unit is relatively low, the time synchronization unit calibrates and matches the timestamps of each sensor signal through methods such as hardware timer synchronization or software interpolation algorithm, eliminates the timing deviation between multi-source signals, ensures that multi-source data at the same moment can be synchronized, and provides an accurate and synchronized data basis for subsequent control processing.

[0030] The state evaluation module is separately deployed in the dynamic coordination module and the parameter correction module. Its core function is to conduct real-time evaluation on these two modules. For the dynamic coordination module, the state evaluation module continuously monitors parameters such as the change rate of lateral acceleration, yaw rate, and the dynamic trend of tire sideslip angle. By calculating the deviation value between the current state and the target state, it generates the first evaluation result, which is used to reflect the effectiveness and stability of the system's dynamic coordination process. For example, when the lateral acceleration changes during vehicle driving, the state evaluation module calculates the deviation between it and the target lateral acceleration in real time and takes this deviation into account in the first evaluation result. For the parameter correction module, the state evaluation module focuses on the correction amplitude of control parameters, correction frequency, and the impact degree of the corrected parameters on the vehicle state, etc., and generates the second evaluation result to judge the rationality of the parameter correction strategy. For instance, after correcting a certain control parameter, the state evaluation module monitors the change of vehicle state parameters, evaluates whether the correction has achieved the expected effect, and thus forms the second evaluation result.

[0031] Based on the first evaluation result and the second evaluation result generated by the state evaluation module, the control decision module selects the corresponding coordination control mode through the configuration register for dynamic adjustment. Different mapping relationships between evaluation results and coordination control modes are pre-stored in the register. For example, when the first evaluation result shows that the yaw rate deviation exceeds a certain range and the second evaluation result shows that the parameter correction amplitude does not reach the expectation, the dynamic coordination mode of global parameters may be selected to conduct overall adjustment on relevant control strategies; if the evaluation result shows that the deviation is small and the parameter correction is effective, the dynamic coordination mode of local parameters may be selected to only fine-tune the control strategy corresponding to the deviation parameter. After determining the coordination control mode, the control decision module generates an output compensation signal through corresponding algorithms and logics. This signal and the basic control signal perform weighted fusion operation in the signal synthesis module to obtain the final control signal, and finally the execution output module outputs the final control signal to the actuator, such as the electronic stability program (ESP), active suspension system, etc., to achieve the control of vehicle lateral stability.

[0032] Example 2: In this embodiment, the main controller has two instruction generation and sending mechanisms for different target stability tasks. The first mechanism corresponds to the first target stability task. The main controller retrieves preset control parameters from the data storage unit as the first target stability instruction. The data storage unit pre-stores control parameter groups under various typical working conditions, including straight driving on a dry road surface, steering on a wet road surface, and emergency braking on an icy road surface. Each group of parameters includes specific values such as the lateral acceleration threshold, the target value of the yaw rate, and the safe range of the tire side slip angle. When the vehicle is driving, the main controller receives multi-source sensor data such as the steering angle signal, the wheel speed signal, and the inertial measurement signal, and real-time identifies the current working condition. For example, when it is detected that the steering angle signal continuously exceeds a certain threshold and the inertial measurement signal shows the existence of lateral acceleration, it is determined that the vehicle is in a steering working condition. If at the same time the wheel speed signal and the road surface adhesion coefficient signal indicate that the road surface is wet, it is further identified as a steering working condition on a wet road surface. At this time, the main controller retrieves the preset control parameter group that matches this working condition from the data storage unit. After format verification and priority judgment, it is converted into the first target stability instruction that conforms to the communication protocol and sent to the lateral control co-processor through the internal bus. After receiving the instruction, the lateral control co-processor analyzes the lateral control parameters therein, such as the reduced lateral acceleration threshold, the adjusted target value of the yaw rate, etc., and generates corresponding basic control signals for controlling the actuator to initially adjust the vehicle's lateral dynamic state.

[0033] The second mechanism is for the second target stability task. The main controller uses the coordinated control algorithm based on multi-objective optimization as the second target stability instruction. This algorithm integrates multiple optimization objectives such as lateral stability, ride comfort, and tire wear balance. Through intelligent optimization algorithms such as particle swarm optimization and genetic algorithms, it seeks the optimal balance solution for each objective in real-time calculation. When the vehicle faces complex working conditions, such as sudden changes in road adhesion coefficient during continuous curve driving, simultaneous occurrence of emergency obstacle avoidance and braking, etc., the main controller first obtains the vehicle's real-time state parameters (such as lateral acceleration, yaw rate, tire sideslip angle, wheel speeds of each wheel, road adhesion coefficient) and environmental parameters (such as temperature, humidity) through multi-source sensors, and then starts the multi-objective optimization algorithm. The algorithm takes the current vehicle state as the initial condition and each optimization objective as the constraint condition, and generates a set of dynamically adjusted control parameters through iterative calculation, such as the lateral torque distribution ratio, yaw moment correction coefficient, lateral force compensation amplitude, etc. After being encapsulated by the main controller as the second target stability instruction, these parameters are sent to the lateral control co-processor. The lateral control co-processor analyzes the algorithm parameters in the instruction through the built-in dedicated calculation unit, and combines the real-time monitored vehicle state data to dynamically adjust the coordinated control strategy in each control cycle. For example, in continuous curves, the algorithm gives priority to ensuring lateral stability, and instructs the lateral control co-processor to increase the braking pressure of the inner wheels to generate a yaw moment and suppress vehicle sideslip; when it detects a sudden decrease in road adhesion coefficient, the algorithm automatically reduces the adjustment amplitude of the yaw moment correction coefficient to avoid exceeding the limit value of tire adhesion due to excessive intervention, and at the same time moderately increases the lateral force compensation amplitude to maintain the vehicle driving trajectory.

[0034] The two instruction mechanisms work together through the task scheduling module in the main controller. The task scheduling module automatically selects the corresponding instruction generation method according to the complexity of the real-time working conditions and the priority of control requirements. For example, for conventional known working conditions, the first target stability task mechanism is preferentially adopted to quickly respond by retrieving preset parameters; for unconventional complex working conditions or scenarios that cannot be covered by the preset parameters, the second target stability task mechanism is triggered, and an adaptive control instruction is generated using the multi-objective optimization algorithm. The combination of the two mechanisms enables the system to achieve efficient and stable control under common working conditions, and also improve the control accuracy and robustness through intelligent algorithms in complex scenarios, thereby comprehensively enhancing the vehicle's lateral stability control ability.

[0035] Embodiment 3: In this embodiment, the coordinated control mode includes three modes: dynamic coordination function bypass, global parameter dynamic coordination, and local parameter dynamic coordination. The dynamic coordination function bypass mode is used for system initialization or fault diagnosis scenarios. When the system starts up, each sensor and control module needs to perform self-check and parameter initialization. At this time, the system automatically enters the dynamic coordination function bypass mode, skipping the complex dynamic coordination and parameter correction processes, and directly controlling the actuator according to the basic control signal to ensure that the vehicle has basic maneuverability during the system initialization phase. During the fault diagnosis process, if a temporary fault occurs in the sensor fusion coprocessor or the lateral control coprocessor, such as abnormal sensor signals or communication interruptions, the system will also switch to this mode to avoid the impact of incorrect control instructions caused by the fault on vehicle safety. For example, when the inertial measurement unit fails briefly and the system cannot obtain accurate lateral acceleration and yaw rate data, it enters the bypass mode, performs open-loop control based on the wheel speed signal and the steering angle signal, maintains the basic driving state of the vehicle, and at the same time starts the fault diagnosis and repair process.

[0036] The global parameter dynamic coordination mode is applicable to scenarios where the vehicle state deviates significantly or complex working conditions occur. When the vehicle is in working conditions such as high-speed emergency obstacle avoidance and out-of-control warning on low-adhesion roads, the control decision-making module judges that comprehensive parameter adjustment is required according to the first evaluation result and the second evaluation result generated by the state evaluation module. In this mode, the dynamic adjustment module simultaneously activates the lateral torque distribution strategy, the yaw moment correction strategy, and the lateral force compensation strategy to comprehensively adjust the lateral dynamic parameters of the vehicle. For example, during high-speed emergency obstacle avoidance, the system first increases the braking pressure of the outer wheels through the lateral torque distribution strategy to generate a negative yaw moment to quickly steer the vehicle; at the same time, it uses the yaw moment correction strategy to adjust the correction amplitude according to the real-time yaw rate deviation to ensure that the vehicle steers along the expected trajectory; finally, through the lateral force compensation strategy combined with the tire side slip angle data, it compensates for the insufficient lateral force generated by the rapid steering to prevent the vehicle from rolling over. During the whole process, the control decision-making module selects the corresponding coordinated control parameters through the configuration register, and these parameters are optimized and calculated to balance the vehicle's stability, steering response, and ride comfort.

[0037] The local parameter dynamic coordination mode is used to handle scenarios of single parameter anomalies or minor operating condition changes. When the system detects an abnormal fluctuation in a certain parameter, such as the signal of a single wheel speed sensor being unstable, or the adhesion coefficient of a certain tire changing due to local road surface wetness, the control decision-making module only makes local corrections to the control strategy corresponding to the abnormal parameter, and the remaining parameters maintain the original control strategy. At this time, the single parameter adjustment module in the dynamic adjustment module is activated, and this module processes the abnormal parameter through the Kalman filter algorithm to eliminate noise interference and predict the parameter change trend. For example, when an abnormal fluctuation in the left front wheel speed signal is detected, the single parameter adjustment module first performs Kalman filter processing on this signal, and the calculation formula is: Where, is the state estimation value after filtering (i.e., the processed wheel speed value), is the predicted value based on the previous moment, is the Kalman gain, is the actual measured value (i.e., the abnormal wheel speed signal), is the observation matrix. The single parameter adjustment module makes local corrections to the lateral torque distribution strategy of the left front wheel according to the filtered wheel speed value, temporarily estimates using the wheel speed data of adjacent wheels, and at the same time starts the sensor fault diagnosis process to determine whether the wheel speed sensor really fails. During the correction process, the system will continuously monitor the changes in vehicle state parameters to ensure that the local correction will not have a negative impact on the overall control. If it is confirmed through diagnosis that the sensor is normal, the system will gradually resume the normal processing of this parameter; if it is confirmed that the sensor fails, the fault alarm mechanism will be triggered, and the alternative strategy will continue to be used to maintain the stable driving of the vehicle.

[0038] Example 4: In this embodiment, the dynamic adjustment module includes a first correction module and a second correction module. The first correction module superimposes a correction signal within the current control cycle for the scenario where the lateral acceleration corresponding to the basic control signal exceeds the threshold. This module is further subdivided into a steady-state coordination module, a transient coordination module, an extreme condition module, and a single parameter adjustment module. The steady-state coordination module is used for the vehicle steady-state steering scenario. When it is detected that the vehicle is in a steady-state steering state, it will continuously calculate the deviation value between the current yaw rate and the target yaw rate. If this deviation exceeds the allowable range, it will trigger the correction of the lateral torque distribution strategy, and change the yaw moment by adjusting the brake pressure of the inner wheel to make the yaw rate return to the target value; if the deviation is within the allowable range, the original control strategy will be maintained. For example, when the vehicle is driving on a curve at a constant speed, the steady-state coordination module will continuously monitor the yaw rate and compare it with the preset target value. If it is found that the deviation exceeds the range, the corresponding control parameters will be adjusted.

[0039] The transient coordination module is targeted at transient lane - changing scenarios. Before the start of a single - lane - changing action, it monitors in real - time the difference between the tire sideslip angle and the preset sideslip angle. If the difference exceeds the safety threshold, it makes a correction to the yaw moment correction strategy. By applying a lateral torque in advance, it suppresses the body roll during the lane - changing process. If the difference does not exceed the threshold, no correction is made. For example, when the vehicle is about to perform a lane - changing operation, the transient coordination module will monitor and analyze the tire sideslip angle in advance and decide whether to adjust the yaw moment correction strategy according to the monitoring results.

[0040] The extreme - condition module is used for low - adhesion road surface scenarios. It dynamically adjusts the lateral force compensation strategy in combination with tire slip ratio data. When the slip ratio exceeds the critical value, the module makes a correction to the compensation parameter, reducing the lateral force compensation amplitude to avoid complete tire locking caused by over - compensation. For example, when driving on low - adhesion road surfaces such as ice and water - logged roads, the extreme - condition module will closely monitor the change of the tire slip ratio. Once it finds that the slip ratio exceeds the critical value, it will promptly adjust the lateral force compensation strategy to ensure the vehicle's stability.

[0041] The second correction module activates the sideslip angle compensation function for specific working conditions. The compensation configuration module configures the sideslip angle compensation parameter register and the compensation action interval register for each working condition. After the compensation function is activated, the parameter recording module records in real - time the change information of the control parameters at each compensation point. When the control target is reached within the specified interval of a specific working condition, the non - linear compensation module makes a non - linear compensation according to the change information. For example, in the high - speed cornering working condition, when the vehicle's lateral acceleration drops to a certain range and the yaw rate tends to be stable, the non - linear compensation module will adjust the sideslip angle compensation amplitude in a non - linear manner according to the previously recorded control parameter change information to avoid under - steering caused by over - compensation. Throughout the process, the dynamic adjustment module ensures good lateral stability of the vehicle under various driving conditions by precisely adjusting and compensating the parameters under different working conditions.

[0042] Embodiment 5: In this embodiment, the time - synchronization unit of the data fusion module realizes the time alignment of multi - source sensor signals through two mechanisms: hardware synchronization and software synchronization. The hardware synchronization mechanism uses a unified clock source to provide synchronization signals for sensors. For example, a high - precision clock signal is generated by a field - programmable gate array (FPGA) and transmitted to the steering angle sensor, wheel speed sensor, and inertial measurement unit (IMU) through a synchronization bus, enabling each sensor to collect data at the same sampling moment and eliminating the timing deviation from the source. The advantage of hardware synchronization is high synchronization accuracy, which is suitable for high - frequency sampling scenarios with strict real - time requirements, such as the millisecond - level sampling period of the wheel speed sensor.

[0043] The software synchronization mechanism performs time alignment on sensor signals with different sampling frequencies through post - processing algorithms. Taking the wheel speed sensor (sampling period ms) and the inertial measurement unit (sampling period ms) as an example, the time synchronization unit first receives the raw data and timestamps of the two types of signals, and then based on the linear interpolation algorithm, within each sampling period of the IMU, according to the sampling values and of the wheel speed sensor at two adjacent moments before and after, estimates the wheel speed value corresponding to the sampling moment of the IMU. The interpolation formula is: where and are the adjacent sampling moments of the wheel speed sensor, and satisfy . Through this algorithm, the wheel speed signal is converted from a high - frequency sampling sequence to a low - frequency sequence synchronized with the IMU, forming a dataset with time alignment.

[0044] The processing flow of the time synchronization unit includes: First, calibrate the original timestamps of each sensor, and correct the error caused by the clock drift inside the sensor through a hardware timer or software calibration algorithm; Second, select a synchronization strategy (hardware synchronization or software synchronization) according to the sensor type and sampling characteristics, and perform resampling or interpolation processing on the signal; Finally, generate a vehicle state parameter stream with a unified time baseline, such as the synchronized steering angle , wheel speed , lateral acceleration and yaw rate etc., for the lateral control coprocessor to perform dynamic coordination monitoring and control decisions. For example, during the vehicle steering process, the time synchronization unit ensures that the steering angle signal corresponds to the yaw rate signal at the same time point, avoiding control lag or misjudgment caused by timing misalignment, thus providing an accurate data basis for lateral stability control.

[0045] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0046] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A vehicle lateral stability coordination control system based on sensing fusion, characterized in that, Comprising: A main controller, a sensing fusion coprocessor, and a lateral control coprocessor; The main controller is configured to retrieve a corresponding stability control instruction according to the vehicle driving state, and send the stability control instruction to the lateral control coprocessor; The sensing fusion coprocessor is configured to schedule the lateral control instruction of the lateral control coprocessor during the execution of the multi-source information fusion instruction; The lateral control coprocessor is configured to parse the lateral control parameters in the stability control instruction to obtain corresponding basic control signals; dynamically coordinate and monitor the lateral acceleration, yaw rate, and tire side slip angle, and select a corresponding coordinated control mode for dynamic adjustment according to the monitoring results to obtain an output compensation signal, and combine the basic control signal to perform control output to the actuator.

2. The vehicle lateral stability coordinated control system based on sensing fusion according to claim 1, characterized in that, The lateral control coprocessor includes: A data fusion module, configured to parse multi-source sensor signals and convert the parsed sensor signals into vehicle state parameters as the basic control signals; wherein, the sensor signals include steering angle signals, wheel speed signals, and inertial measurement signals; A state evaluation module, the state evaluation module is respectively deployed in the dynamic coordination module and the parameter correction module, and is configured to respectively perform real-time evaluation on the dynamic coordination module and the parameter correction module to obtain a first evaluation result corresponding to the dynamic coordination module and a second evaluation result corresponding to the parameter correction module; A control decision module, configured to select a corresponding coordinated control mode for dynamic adjustment according to the first evaluation result and the second evaluation result through a configuration register to obtain an output compensation signal, and combine the basic control signal to perform control output to the actuator.

3. The vehicle lateral stability coordination control system based on sensing fusion according to claim 1, wherein The main controller is further configured to retrieve a corresponding preset control parameter as a first target stability instruction through a data storage unit according to a first target stability task, and send the first target stability instruction to the lateral control coprocessor.

4. The vehicle lateral stability coordination control system based on sensor fusion according to claim 1, characterized in that, The main controller is further configured to use a coordinated control algorithm based on multi-objective optimization as a second target stability instruction according to a second target stability task, and send the second target stability instruction to the lateral control coprocessor.

5. The vehicle lateral stability coordination control system based on sensing fusion according to claim 1, characterized in that, The coordinated control mode includes: dynamic coordination function bypass, dynamic coordination of global parameters, and dynamic coordination of local parameters.

6. The vehicle lateral stability coordination control system based on sensing fusion according to claim 2, wherein The control decision module includes: A dynamic adjustment module, configured to select coordinated control strategies corresponding to the dynamic coordination module and the parameter correction module respectively according to the first evaluation result and the second evaluation result for dynamic adjustment to obtain the output compensation signal; wherein, the coordinated control strategies include lateral torque distribution strategy, yaw moment correction strategy, and lateral force compensation strategy; A signal synthesis module, configured to perform weighted fusion operation on the basic control signal and the output compensation signal to obtain a final control signal; An execution output module, configured to output the final control signal to the actuator.

7. The vehicle lateral stability coordination control system based on sensing fusion according to claim 6, wherein The dynamic adjustment module includes: The first correction module is used to, when it is detected that the lateral acceleration corresponding to the basic control signal exceeds the threshold, superimpose a correction signal within the current control period to obtain a corrected total control signal as the first target output compensation signal; The second correction module is used to perform non-linear correction according to the sideslip angle compensation table to obtain the second target output compensation signal.

8. The vehicle lateral stability coordinated control system based on sensing fusion according to claim 7, characterized in that, The first correction module includes: The steady-state coordination module is used to, when it is detected that the vehicle is in steady-state steering, determine whether the deviation between the current yaw rate and the target yaw rate exceeds the allowable range. If it exceeds, the lateral torque distribution strategy is corrected once. If it does not exceed, no correction is made; The transient coordination module is used to, when it is detected that the vehicle is in transient lane change, before the start of a single lane change action, determine whether the difference between the tire sideslip angle and the preset sideslip angle exceeds the safety threshold. If it exceeds, the yaw moment correction strategy is corrected once. If it does not exceed, no correction is made; The extreme condition module is used to, when it is detected that the vehicle is on a low-adhesion road surface, dynamically adjust the lateral force compensation strategy in combination with the tire slip ratio data, and correct the compensation parameters once when the slip ratio exceeds the critical value; The single-parameter adjustment module is used to, when it is detected that a single parameter is abnormal, locally correct the control strategy corresponding to the parameter within the control period, and the remaining parameters maintain the original control strategy.

9. The vehicle lateral stability coordinated control system based on sensing fusion according to claim 7, wherein The second correction module includes: The compensation configuration module is used to activate the sideslip angle compensation function for specific working conditions; wherein, the specific working conditions include high-speed cornering, emergency obstacle avoidance, and wet road surfaces, and each of the working conditions is configured with a sideslip angle compensation parameter register and a compensation action interval register; The parameter recording module is used to, after activating the compensation function, record in real time the change information of the control parameters at each compensation point under the specific working conditions; And the non-linear compensation module is used to perform a non-linear compensation once according to the change information when the control target is reached within the specified interval of the specific working condition.

10. The vehicle lateral stability coordination control system based on sensing fusion according to claim 2, wherein, The data fusion module further includes a time synchronization unit for aligning the sampling periods of different sensors to eliminate the timing deviation of multi-source signals.

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