Vehicle lateral stability coordinated control system based on sensor fusion

Abstract

The present invention relates to the field of vehicle engineering technology and discloses a vehicle lateral stability coordination control system based on sensor fusion, comprising a main controller, a sensor fusion coprocessor, and a lateral control coprocessor. The main controller retrieves and sends stability control instructions based on the driving state; the sensor fusion coprocessor dispatches lateral control instructions in multi-source information fusion; the lateral control coprocessor parses parameters to obtain basic control signals, dynamically monitors lateral acceleration, yaw rate, and tire slip angle, and generates output compensation signals by selecting dynamic coordination function bypass, global or local parameter dynamic coordination, and other mode adjustments. After fusion with the basic signals, the system controls the actuator. The system integrates data fusion, state assessment, and multi-strategy dynamic adjustment modules to achieve precise fusion of multi-source information and multi-parameter coordinated control, thereby improving the vehicle's lateral stability and driving safety under complex working conditions. The system is suitable for the field of vehicle engineering control.

Description

Technical Field

[0001] The present invention relates to the field of vehicle engineering technology, and in particular 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 growing demand for road transportation, vehicle safety and stability have become crucial technical issues. Lateral stability is a key factor affecting vehicle safety during driving. Lateral instability can lead to dangerous conditions such as skidding and tailspin, seriously threatening the lives of drivers and passengers and road traffic safety.

[0003] Traditional vehicle lateral stability control technologies primarily rely on single sensors or independent control systems, such as the electronic stability program (ESP) based on wheel speed sensors. However, single sensors suffer from incomplete information acquisition and insufficient reliability under complex driving conditions, making it difficult to accurately and in real time reflect the vehicle's actual driving state. Furthermore, traditional control systems typically employ fixed control strategies, lacking the flexibility to adapt to the vehicle's dynamic driving state and external environmental changes. This results in limited control effectiveness in complex driving conditions such as high-speed cornering, emergency obstacle avoidance, and slippery roads, making it difficult to achieve precise control of the vehicle's lateral stability.

[0004] With the development of multi-sensor technology and intelligent control algorithms, the application of sensor fusion technology in the automotive field has gradually attracted attention. Multi-sensor fusion can integrate multi-source information to provide a more comprehensive and accurate description of the vehicle's status. However, many technical challenges remain in effectively fusing multi-source information and achieving coordinated control of the vehicle's lateral stability based on the fused information. For example, the different sampling periods of different sensors lead to timing deviations in multi-source signals, affecting the accuracy of information fusion. In complex operating conditions, how to dynamically adjust the control strategy based on the vehicle's real-time status to achieve coordinated control of multiple parameters such as lateral acceleration, yaw rate, and tire slip angle to improve vehicle lateral stability remains a technical challenge that needs to be addressed.

[0005] Furthermore, existing vehicle lateral stability control systems lack effective coordination between the main controller, sensor fusion coprocessor, and lateral control coprocessor during the generation and execution of control commands. The main controller struggles to flexibly retrieve stability control commands based on the vehicle's driving state, while the sensor fusion coprocessor lacks the ability to schedule the lateral control coprocessor during information fusion. Furthermore, the lateral control coprocessor is unable to efficiently analyze control parameters and implement dynamic, coordinated monitoring and adjustment of multiple parameters. Consequently, the overall system's response speed and control accuracy struggle to meet the high safety and stability requirements of modern vehicles.

[0006] Therefore, developing a vehicle lateral stability coordination control system that can effectively integrate multi-source sensor information, dynamically coordinate monitoring and adjustment of multiple control parameters, and efficiently collaborate between modules has important practical significance and engineering application value for improving vehicle driving safety and stability. Summary of the Invention

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

[0008] To achieve the above objectives, the present invention provides the following technical solution: a vehicle lateral stability coordinated control system based on sensor fusion, the system comprising:

[0009] Main controller, sensor fusion coprocessor, and lateral control coprocessor;

[0010] The main controller is used to call the corresponding stability control instruction according to the vehicle driving state, and send the stability control instruction to the lateral control coprocessor;

[0011] The sensor fusion coprocessor is used to schedule the lateral control instructions of the lateral control coprocessor when executing the multi-source information fusion instructions;

[0012] The lateral control coprocessor is used to parse the lateral control parameters in the stability control command to obtain the corresponding basic control signal; dynamically coordinate and monitor the lateral acceleration, yaw angular velocity and tire sideslip angle, and select the corresponding coordinated control mode for dynamic adjustment based on the monitoring results to obtain an output compensation signal, and combine it with the basic control signal to control the actuator output.

[0013] Preferably, the lateral control coprocessor includes:

[0014] a data fusion module, configured to analyze multi-source sensor signals and convert the analyzed 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;

[0015] a state evaluation module, wherein the state evaluation module is respectively deployed in the dynamic coordination module and the parameter correction module, and is used to respectively evaluate the dynamic coordination module and the parameter correction module in real time to obtain a first evaluation result corresponding to the dynamic coordination module and a second evaluation result corresponding to the parameter correction module;

[0016] A control decision module is used to select a corresponding coordinated control mode through a configuration register for dynamic adjustment based on the first evaluation result and the second evaluation result, obtain an output compensation signal, and combine the basic control signal to control the output of the actuator.

[0017] Preferably, the main controller is also used to retrieve the corresponding preset control parameters as the first target stability instruction through the data storage unit according to the first target stability task, and send the first target stability instruction to the lateral control coprocessor.

[0018] 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.

[0019] Preferably, the coordination control mode includes: bypassing the dynamic coordination function, performing dynamic coordination of global parameters, and performing dynamic coordination of local parameters.

[0020] Preferably, the control decision module includes:

[0021] a dynamic adjustment module, configured to select, based on the first evaluation result and the second evaluation result, coordinated control strategies corresponding to the dynamic coordination module and the parameter correction module, respectively, for dynamic adjustment to obtain the output compensation signal; wherein the coordinated control strategies include a lateral moment distribution strategy, a yaw moment correction strategy, and a lateral force compensation strategy;

[0022] 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;

[0023] The execution output module is used to output the final control signal to the actuator.

[0024] Preferably, the dynamic adjustment module includes:

[0025] a first correction module configured to, when detecting that the lateral acceleration corresponding to the basic control signal exceeds a threshold, superimpose a correction signal within a current control cycle to obtain a corrected total control signal as a first target output compensation signal;

[0026] The second correction module is used to perform nonlinear correction according to the sideslip angle compensation table to obtain a second target output compensation signal.

[0027] Preferably, the first correction module includes:

[0028] The steady-state coordination module is used to determine whether the deviation between the current yaw rate and the target yaw rate exceeds the allowable range when the vehicle is detected to be in steady-state steering. If so, the lateral torque distribution strategy is corrected. If not, no correction is made.

[0029] The transient coordination module is used to determine whether the difference between the tire slip angle and the preset slip angle exceeds the safety threshold before the start of a single lane change when the vehicle is detected to be in a transient lane change. If so, the yaw moment correction strategy is modified; otherwise, no correction is performed;

[0030] The extreme operating condition module is used to dynamically adjust the lateral force compensation strategy based on tire slip data when the vehicle is detected on a low-adhesion road surface, and to make a correction to the compensation parameters when the slip rate exceeds a critical value;

[0031] The single parameter adjustment module is used to make local corrections to the control strategy corresponding to the parameter within the control cycle when a single parameter abnormality is detected, while maintaining the original control strategy for the remaining parameters.

[0032] Preferably, the second correction module includes:

[0033] A compensation configuration module, configured to activate the sideslip angle compensation function for specific operating conditions, including high-speed cornering, emergency obstacle avoidance, and slippery roads, each of which is configured with a sideslip angle compensation parameter register and a compensation action range register;

[0034] A parameter recording module is used to record the control parameter change information of each compensation point under the specific working condition in real time after the compensation function is activated;

[0035] and a nonlinear compensation module, which is used to perform a nonlinear compensation according to the change information when the control target is achieved within a specified interval of the specific working condition.

[0036] Preferably, the data fusion module further includes a time synchronization unit for aligning the sampling periods of different sensors to eliminate timing deviations of multi-source signals.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The vehicle lateral stability coordinated control system based on sensor fusion provided by the present invention realizes the efficient fusion of multi-source sensor information and precise control of vehicle lateral stability through the coordinated work of the main controller, sensor fusion coprocessor and lateral control coprocessor, thereby significantly improving the vehicle's driving safety and stability under complex working conditions.

[0039] The main controller can flexibly retrieve corresponding stability control commands based on the vehicle's driving state and send them to the lateral control coprocessor. This design enables the system to quickly generate and transmit appropriate control commands for different driving conditions and control tasks, improving system response speed and flexibility. For example, for the first target stability task, preset control parameters are retrieved as the first target stability command, while for the second target stability task, a coordinated control algorithm based on multi-objective optimization is used as the second target stability command, ensuring that the system can adopt appropriate control strategies in different scenarios.

[0040] When executing multi-source information fusion commands, the sensor fusion coprocessor dispatches lateral control commands from the lateral control coprocessor. The data fusion module analyzes multi-source sensor signals (including steering angle, wheel speed, and inertial measurement signals) and converts them into vehicle state parameters that serve as the basis for control signals. Furthermore, the time synchronization unit within the data fusion module aligns the sampling periods of different sensors, eliminating timing deviations among multi-source signals and improving the accuracy and reliability of information fusion, providing a solid foundation for subsequent control decisions.

[0041] The design of the lateral control coprocessor is one of the core highlights of this invention. Its state assessment module, deployed in both the dynamic coordination module and the parameter correction module, performs real-time evaluations of both modules, generating corresponding evaluation results that inform the control decision module's selection of coordinated control modes. Based on these evaluation results, the control decision module dynamically adjusts the coordinated control mode (including bypassing the dynamic coordination function, performing dynamic coordination of global parameters, and performing dynamic coordination of local parameters) through configuration registers, enabling flexible response to varying operating conditions. For example, under normal operating conditions, the dynamic coordination function can be bypassed to reduce unnecessary control interventions; under complex operating conditions, dynamic coordination of global or local parameters is performed to achieve precise control of vehicle status.

[0042] Based on the evaluation results, the dynamic adjustment module selects the corresponding coordinated control strategy (including lateral torque distribution, yaw moment correction, and lateral force compensation) and dynamically adjusts it to generate an output compensation signal. The design of the first and second correction modules further enhances the system's control capabilities. The first correction module applies corresponding correction strategies for different driving conditions (steady-state steering, transient lane changes, low-adhesion roads, etc.) and parameter anomalies, enabling real-time correction of control signals. For example, during steady-state steering, the lateral torque distribution strategy is modified by determining whether the yaw rate deviation exceeds the allowable range. During transient lane changes, the yaw moment correction strategy is adjusted based on whether the difference between the tire slip angle and a preset slip angle exceeds a safety threshold. On low-adhesion roads, the lateral force compensation strategy is dynamically adjusted based on tire slip data to ensure vehicle stability under various driving conditions. The second correction module further improves the system's control accuracy and stability under complex conditions by activating the sideslip angle compensation function for specific operating conditions (such as high-speed cornering, emergency obstacle avoidance, and slippery roads) and performing nonlinear compensation based on real-time recorded control parameter changes.

[0043] The signal synthesis module performs a 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, achieving real-time control of the vehicle's lateral stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a working principle diagram of the vehicle lateral stability coordinated control system based on sensor fusion according to the present invention;

[0045] Figure 2 This is the workflow diagram of the lateral control coprocessor;

[0046] Figure 3 This is the workflow diagram of the control decision module;

[0047] Figure 4 Workflow diagram for dynamic adjustment module;

[0048] Figure 5 This is the workflow diagram of the second correction module. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] See also Figure 1-Figure 5 The present invention relates to a vehicle lateral stability coordinated control system based on sensor fusion, which includes: a main controller, a sensor fusion coprocessor, and a lateral control coprocessor. Specifically, the system includes the following steps:

[0051] The system's core architecture consists of a main controller, a sensor fusion coprocessor, and a lateral control coprocessor. As the system's decision-making hub, the main controller's core function is to retrieve stability control commands from internal storage based on the vehicle's real-time driving state and accurately transmit them to the lateral control coprocessor. "Vehicle driving state" here encompasses multiple parameters such as vehicle speed, steering angle, and road adhesion coefficient. The main controller uses pre-set logical judgment rules to match the control strategy to the current state.

[0052] The sensor fusion coprocessor undertakes the critical task of fusing multi-source information. During the execution of fusion commands, it dispatches lateral control commands from the lateral control coprocessor. This module uses a time synchronization unit to align the sampling periods of different sensors, eliminating timing deviations among multi-source signals. This ensures temporal consistency of signals from devices such as the steering angle sensor, wheel speed sensors, and inertial measurement unit, providing a reliable data foundation for subsequent control.

[0053] The lateral control coprocessor 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 command, such as the lateral acceleration target value and yaw rate threshold, to generate the basic control signal. Second, it dynamically coordinates and monitors the lateral acceleration, yaw rate, and tire slip angle, and collects real-time data on these three key parameters through the built-in sensor interface. Finally, based on the monitoring results, it selects the corresponding mode from the preset coordinated control mode for dynamic adjustment, generates an output compensation signal, and combines it with the basic control signal to form the final actuator control command.

[0054] The present invention will be further described below in conjunction with Examples 1 to 5:

[0055] Example 1:

[0056] In this embodiment, the lateral control coprocessor includes a data fusion module, a state assessment module, and a control decision module. The data fusion module is responsible for parsing multi-source sensor signals, including steering angle signals, wheel speed signals, and inertial measurement signals. Using a specific parsing algorithm, this module converts these raw sensor signals into vehicle state parameters. For example, it parses the signal collected by the steering angle sensor into the actual steering angle value, resolves the wheel speed sensor signals into the linear and angular velocities of each wheel, and converts the inertial measurement signals into lateral acceleration, longitudinal acceleration, and yaw rate. These parsed and converted parameters serve as basic control signals and are output to the control decision module. Notably, the data fusion module also includes a time synchronization unit, which aligns the sampling periods of different sensors. Because different sensors may have different sampling frequencies—for example, wheel speed sensors may sample at a high frequency, while inertial measurement units have a relatively low sampling frequency—the time synchronization unit calibrates and matches the timestamps of each sensor signal through hardware timer synchronization or software interpolation algorithms. This eliminates timing deviations between the multiple signal sources and ensures synchronization of multiple data sources at the same time, providing an accurate and synchronized data foundation for subsequent control processing.

[0057] The state assessment module is deployed in both the dynamic coordination module and the parameter correction module. Its core function is to perform real-time evaluations of both modules. For the dynamic coordination module, the state assessment module continuously monitors parameters such as lateral acceleration, the rate of change of yaw rate, and the dynamic trend of tire slip angles. By calculating the deviation between the current state and the target state, it generates a first evaluation result, which reflects the effectiveness and stability of the system's dynamic coordination process. For example, when the lateral acceleration changes during vehicle operation, the state assessment module calculates the deviation from the target lateral acceleration in real time and incorporates this deviation into the first evaluation result. For the parameter correction module, the state assessment module focuses on the magnitude and frequency of control parameter corrections, as well as the impact of the corrected parameters on the vehicle's state, generating a second evaluation result to determine the rationality of the parameter correction strategy. For example, after a control parameter is corrected, the state assessment module monitors the changes in the vehicle's state parameters to assess whether the correction achieved the desired effect, thereby generating a second evaluation result.

[0058] Based on the first and second evaluation results generated by the state assessment module, the control decision module dynamically adjusts the coordinated control mode by selecting the appropriate coordinated control mode through a configuration register. The registers store a mapping between different evaluation results and coordinated control modes. For example, if the first evaluation result indicates that the yaw rate deviation exceeds a certain range and the second evaluation result indicates that the parameter correction amplitude does not meet expectations, the dynamic coordinated control mode for global parameters may be selected to adjust the relevant control strategy as a whole. If the evaluation result indicates that the deviation is small and the parameter correction is effective, the dynamic coordinated control mode for local parameters may be selected to fine-tune the control strategy only for the deviation parameter. After determining the coordinated control mode, the control decision module generates an output compensation signal using the corresponding algorithm and logic. This signal is then weighted and fused with the basic control signal in the signal synthesis module to generate the final control signal. Finally, the execution output module outputs the final control signal to actuators, such as the electronic stability program (ESP) and active suspension system, to achieve vehicle lateral stability control.

[0059] Example 2:

[0060] In this embodiment, the main controller has two mechanisms for generating and issuing commands for different target stability tasks. The first mechanism corresponds to the first target stability task. The main controller retrieves preset control parameters from a data storage unit as the first target stability command. The data storage unit contains pre-stored control parameter sets for various typical operating conditions, including straight-line driving on dry roads, steering on slippery roads, and emergency braking on icy and snowy roads. Each parameter set includes specific values such as a lateral acceleration threshold, a target yaw rate, and a tire slip angle safety interval. While the vehicle is driving, the main controller receives data from multiple sensors, including steering angle signals, wheel speed signals, and inertial measurement signals, to identify the current operating condition in real time. For example, if the steering angle signal is continuously greater than a certain threshold and the inertial measurement signal indicates lateral acceleration, the vehicle is determined to be in a steering condition. If both the wheel speed signal and the road adhesion coefficient signal indicate a slippery road, the vehicle is further identified as in a slippery steering condition. At this point, the main controller retrieves the preset control parameter set matching this operating condition from the data storage unit. After format verification and priority determination, it converts this parameter into a first target stability command that complies with the communication protocol. This command is then sent to the lateral control coprocessor via the internal bus. Upon receiving the command, the lateral control coprocessor parses the lateral control parameters, such as the reduced lateral acceleration threshold and the adjusted yaw rate target, and generates the corresponding basic control signals, which are used to control the actuators to initially adjust the vehicle's lateral dynamic state.

[0061] The second mechanism targets the second objective stability task. The main controller uses a coordinated control algorithm based on multi-objective optimization as the second objective stability instruction. This algorithm integrates multiple optimization objectives, such as lateral stability, ride comfort, and tire wear balance. Using intelligent optimization algorithms such as particle swarm optimization and genetic algorithms, it seeks the optimal balance between these objectives in real-time calculations. When the vehicle faces complex operating conditions, such as sudden changes in road adhesion during continuous curves or simultaneous emergency obstacle avoidance and braking, the main controller first obtains real-time vehicle state parameters (such as lateral acceleration, yaw rate, tire slip angle, wheel speed, road adhesion) and environmental parameters (such as temperature and humidity) from multiple sensors. It then initiates the multi-objective optimization algorithm. Using the current vehicle state as the initial condition and the optimization objectives as constraints, the algorithm iteratively calculates a set of dynamically adjusted control parameters, such as lateral torque distribution ratio, yaw moment correction coefficient, and lateral force compensation amplitude. These parameters are packaged by the main controller into a second objective stability instruction and sent to the lateral control coprocessor. The lateral control coprocessor uses a dedicated internal computing unit to analyze algorithm parameters within instructions and, combined with real-time vehicle status data, dynamically adjusts the coordinated control strategy within each control cycle. For example, in continuous curves, the algorithm prioritizes lateral stability, instructing the lateral control coprocessor to increase brake pressure on the inside wheel to generate a yaw moment and suppress vehicle slip. If a sudden drop in road adhesion is detected, the algorithm automatically reduces the yaw moment correction factor to prevent excessive tire adhesion from exceeding the limit. At the same time, it moderately increases the lateral force compensation to maintain the vehicle's trajectory.

[0062] The two command mechanisms work together in the main controller through the task scheduling module. The task scheduling module automatically selects the appropriate command generation method based on the complexity of the real-time working conditions and the priority of the control requirements. For example, for conventional known working conditions, the first target stability task mechanism is preferentially used, which responds quickly by calling the preset parameters; for unconventional and complex working conditions or scenarios that cannot be covered by the preset parameters, the second target stability task mechanism is triggered, and a multi-objective optimization algorithm is used to generate adaptive control commands. The combination of the two mechanisms enables the system to achieve efficient and stable control under common working conditions, and to improve control accuracy and robustness in complex scenarios through intelligent algorithms, thereby comprehensively improving the vehicle's lateral stability control capabilities.

[0063] Example 3:

[0064] 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 in system initialization or fault diagnosis scenarios. When the system starts, each sensor and control module needs to perform self-tests and parameter initialization. At this time, the system automatically enters the dynamic coordination function bypass mode, skipping the complex dynamic coordination and parameter correction process and directly controlling the actuators based on basic control signals to ensure basic vehicle controllability during the system initialization phase. During the fault diagnosis process, if a temporary fault is detected in the sensor fusion coprocessor or lateral control coprocessor, such as abnormal sensor signals or communication interruptions, the system will also switch to this mode to prevent erroneous control commands caused by the fault from affecting vehicle safety. For example, if the inertial measurement unit fails temporarily, the system cannot obtain accurate lateral acceleration and yaw rate data. At this time, it enters the bypass mode and performs open-loop control based on the wheel speed and steering angle signals to maintain the vehicle's basic driving state while initiating the fault diagnosis and repair process.

[0065] The global parameter dynamic coordination mode is suitable for scenarios with significant vehicle state deviations or complex operating conditions. When the vehicle is in situations such as high-speed emergency obstacle avoidance or low-adhesion road loss-of-control warning, the control decision module determines the need for comprehensive parameter adjustments based on the first and second evaluation results generated by the state assessment 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 vehicle's lateral dynamic parameters. For example, during high-speed emergency obstacle avoidance, the system first increases the brake pressure on the outer wheels using the lateral torque distribution strategy, generating a negative yaw moment to force the vehicle to turn quickly. Simultaneously, the yaw moment correction strategy adjusts the correction amplitude based on real-time yaw rate deviations to ensure the vehicle turns along the intended trajectory. Finally, the lateral force compensation strategy, combined with tire slip angle data, compensates for insufficient lateral force generated by the rapid turn, preventing the vehicle from rolling over. Throughout this process, the control decision module selects the corresponding coordinated control parameters through configuration registers. These parameters are optimized to balance vehicle stability, steering response, and ride comfort.

[0066] The local parameter dynamic coordination mode is used to handle scenarios where a single parameter is abnormal or the working conditions change slightly. When the system detects abnormal fluctuations in a certain parameter, such as unstable signals from a single wheel speed sensor, changes in the adhesion coefficient of a certain tire due to local wetness of the road surface, etc., the control decision 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. This module processes the abnormal parameters through the Kalman filter algorithm to eliminate noise interference and predict parameter change trends. For example, when an abnormal fluctuation in the left front wheel speed signal is detected, the single parameter adjustment module first performs Kalman filtering on the signal, and the calculation formula is:

[0067]

[0068] in, is the filtered state estimate (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. abnormal wheel speed signal), is the observation matrix. The single-parameter adjustment module makes a local correction to the lateral torque distribution strategy for the left front wheel based on the filtered wheel speed value, temporarily using the wheel speed data of adjacent wheels for estimation. Simultaneously, the sensor fault diagnosis process is initiated to determine whether the wheel speed sensor is truly faulty. During the correction process, the system monitors changes in vehicle state parameters in real time to ensure that the local correction does not negatively impact overall control. If the diagnosis confirms that the sensor is normal, the system gradually restores normal processing of that parameter. If a sensor fault is confirmed, the fault alarm mechanism is triggered and an alternative strategy is used to maintain stable vehicle driving.

[0069] Example 4:

[0070] 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 scenarios where the lateral acceleration corresponding to the basic control signal exceeds a threshold. This module is further subdivided into a steady-state coordination module, a transient coordination module, an extreme operating condition module, and a single parameter adjustment module. The steady-state coordination module is used in vehicle steady-state steering scenarios. When the vehicle is detected to be in a steady-state steering state, it continuously calculates the deviation between the current yaw rate and the target yaw rate. If the deviation exceeds the allowable range, the lateral torque distribution strategy is corrected, and the yaw torque is changed by adjusting the inner wheel brake pressure to return the yaw rate to the target value. If the deviation is within the allowable range, the original control strategy is maintained. For example, when the vehicle is driving on a curve at a constant speed, the steady-state coordination module monitors the yaw rate in real time and compares it with the preset target value. If the deviation is found to be out of range, the corresponding control parameter is adjusted.

[0071] For transient lane change scenarios, the transient coordination module monitors the difference between the tire slip angle and the preset slip angle in real time before a single lane change begins. If the difference exceeds a safety threshold, the yaw moment correction strategy is modified, preemptively applying lateral torque to suppress body roll during the lane change. If the difference does not exceed the threshold, no correction is applied. For example, when the vehicle is preparing to change lanes, the transient coordination module monitors and analyzes the tire slip angle in advance and, based on the monitoring results, determines whether the yaw moment correction strategy needs to be adjusted.

[0072] The Extreme Conditions module is designed for low-adhesion road conditions and dynamically adjusts the lateral force compensation strategy based on tire slip data. When the slip exceeds a critical value, the module adjusts the compensation parameters, reducing the lateral force compensation amplitude to prevent complete tire lock due to overcompensation. For example, when driving on low-adhesion surfaces such as ice, snow, and water, the Extreme Conditions module closely monitors changes in tire slip. If the slip exceeds a critical value, it promptly adjusts the lateral force compensation strategy to ensure vehicle stability.

[0073] The second correction module activates the sideslip angle compensation function for specific operating conditions. The compensation configuration module configures the sideslip angle compensation parameter register and compensation action range register for each operating condition. After the compensation function is activated, the parameter recording module records the control parameter changes at each compensation point in real time. When the nonlinear compensation module achieves the control target within the specified range for a specific operating condition, it performs nonlinear compensation based on this change information. For example, in high-speed cornering conditions, when the vehicle's lateral acceleration drops to a certain range and the yaw rate stabilizes, the nonlinear compensation module nonlinearly adjusts the sideslip angle compensation amplitude based on the previously recorded control parameter changes to avoid overcompensation and vehicle understeer. Throughout this process, the dynamic adjustment module precisely adjusts and compensates the parameters under different operating conditions to ensure the vehicle maintains good lateral stability under various driving conditions.

[0074] Example 5:

[0075] In this embodiment, the time synchronization unit of the data fusion module achieves time alignment of multi-source sensor signals through both hardware and software synchronization mechanisms. The hardware synchronization mechanism uses a unified clock source to provide synchronization signals for the sensors. For example, a high-precision clock signal is generated by a field-programmable gate array (FPGA) and transmitted via a synchronization bus to the steering angle sensor, wheel speed sensor, and inertial measurement unit (IMU). This ensures that each sensor collects data at the same sampling time, eliminating timing deviations at the source. The advantage of hardware synchronization lies in its high synchronization accuracy, making it suitable for high-frequency sampling scenarios with strict real-time requirements, such as the millisecond sampling period of wheel speed sensors.

[0076] The software synchronization mechanism uses a post-processing algorithm to time-align sensor signals with different sampling frequencies. ms) and the inertial measurement unit (sampling period ms) as an example, the time synchronization unit first receives the original data and timestamps of the two types of signals, and then based on the linear interpolation algorithm, in each sampling cycle of the IMU, according to the sampling values of the two adjacent moments before and after the wheel speed sensor and , estimate the IMU sampling time Corresponding wheel speed value The interpolation formula is: in, 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 time-aligned dataset.

[0077] The processing flow of the time synchronization unit includes: first, calibrating the original timestamp of each sensor and correcting the error caused by the internal clock drift of the sensor through hardware timer or software calibration algorithm; second, selecting the synchronization strategy (hardware synchronization or software synchronization) according to the sensor type and sampling characteristics, and resampling or interpolating the signal; finally, generating a vehicle state parameter stream with a unified time baseline, such as the steering angle after synchronization , wheel speed , lateral acceleration and yaw rate For example, during vehicle steering, the time synchronization unit ensures that the steering angle signal and yaw rate signal correspond to the same time point, avoiding control lag or misjudgment caused by timing misalignment, thereby providing an accurate data foundation for lateral stability control.

[0078] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0079] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A vehicle lateral stability coordinated control system based on sensor fusion, characterized in that: include: Main controller, sensor fusion coprocessor, and lateral control coprocessor; The main controller is used to call the corresponding stability control instruction according to the vehicle driving state, and send the stability control instruction to the lateral control coprocessor; The sensor fusion coprocessor is used to schedule the lateral control instructions of the lateral control coprocessor when executing the multi-source information fusion instructions; The lateral control coprocessor is used to analyze the lateral control parameters in the stability control command to obtain a corresponding basic control signal; dynamically coordinate and monitor the lateral acceleration, yaw rate and tire slip angle, and select a corresponding coordinated control mode based on the monitoring results for dynamic adjustment to obtain an output compensation signal, and combine it with the basic control signal to perform control output on the actuator; 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; The multi-objective optimization coordinated control algorithm uses the current vehicle state as the initial condition and the optimization objectives as the constraints. Through iterative calculation, it generates a set of dynamically adjusted control parameters, including the lateral torque distribution ratio, yaw moment correction coefficient, and lateral force compensation amplitude. And the coordinated control strategy is dynamically adjusted within each control cycle; in continuous curves, the algorithm prioritizes lateral stability and instructs the lateral control coprocessor to increase the braking pressure of the inner wheel to generate yaw torque and suppress vehicle skidding; when it detects that the road adhesion coefficient suddenly decreases during continuous curve driving, the multi-objective optimized coordinated control algorithm automatically reduces the adjustment range of the yaw moment correction coefficient to avoid excessive intervention that causes the tire adhesion to exceed the limit value, and at the same time moderately increases the lateral force compensation range to maintain the vehicle's driving trajectory.

2. The vehicle lateral stability coordinated control system based on sensor fusion according to claim 1, characterized in that: The lateral control coprocessor comprises: a data fusion module, configured to analyze multi-source sensor signals and convert the analyzed 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, wherein the state evaluation module is respectively deployed in the dynamic coordination module and the parameter correction module, and is used to respectively evaluate the dynamic coordination module and the parameter correction module in real time 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 is used to select a corresponding coordinated control mode through a configuration register for dynamic adjustment based on the first evaluation result and the second evaluation result, obtain an output compensation signal, and combine the basic control signal to control the output of the actuator.

3. The vehicle lateral stability coordinated control system based on sensor fusion according to claim 1, characterized in that: The main controller is further configured to retrieve corresponding preset control parameters 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 coordinated control system based on sensor fusion according to claim 1, characterized in that: The coordination control mode includes: bypassing the dynamic coordination function, performing dynamic coordination of global parameters, and performing dynamic coordination of local parameters.

5. The vehicle lateral stability coordinated control system based on sensor fusion according to claim 2, characterized in that: The control decision module includes: a dynamic adjustment module, configured to select, based on the first evaluation result and the second evaluation result, coordinated control strategies corresponding to the dynamic coordination module and the parameter correction module, respectively, for dynamic adjustment to obtain the output compensation signal; wherein the coordinated control strategies include a lateral moment 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; The execution output module is used to output the final control signal to the actuator.

6. The vehicle lateral stability coordinated control system based on sensor fusion according to claim 5, characterized in that: The dynamic adjustment module includes: a first correction module configured to, when detecting that the lateral acceleration corresponding to the basic control signal exceeds a threshold, superimpose a correction signal within a current control cycle to obtain a corrected total control signal as a first target output compensation signal; The second correction module is used to perform nonlinear correction according to the sideslip angle compensation table to obtain a second target output compensation signal.

7. The vehicle lateral stability coordinated control system based on sensor fusion according to claim 6, characterized in that: The first correction module includes: The steady-state coordination module is used to determine whether the deviation between the current yaw rate and the target yaw rate exceeds the allowable range when the vehicle is detected to be in steady-state steering. If so, the lateral torque distribution strategy is corrected. If not, no correction is made. The transient coordination module is used to determine whether the difference between the tire slip angle and the preset slip angle exceeds the safety threshold before the start of a single lane change when the vehicle is detected to be in a transient lane change. If so, the yaw moment correction strategy is modified; otherwise, no correction is performed; The extreme operating condition module is used to dynamically adjust the lateral force compensation strategy based on tire slip data when the vehicle is detected on a low-adhesion road surface, and to make a correction to the compensation parameters when the slip rate exceeds a critical value; The single parameter adjustment module is used to make local corrections to the control strategy corresponding to the parameter within the control cycle when a single parameter abnormality is detected, while maintaining the original control strategy for the remaining parameters.

8. The vehicle lateral stability coordinated control system based on sensor fusion according to claim 6, characterized in that: The second correction module includes: A compensation configuration module, configured to activate the sideslip angle compensation function for specific operating conditions, including high-speed cornering, emergency obstacle avoidance, and slippery roads, each of which is configured with a sideslip angle compensation parameter register and a compensation action range register; A parameter recording module is used to record the control parameter change information of each compensation point under the specific working condition in real time after the compensation function is activated; and a nonlinear compensation module, which is used to perform a nonlinear compensation according to the change information when the control target is achieved within a specified interval of the specific working condition.

9. The vehicle lateral stability coordinated control system based on sensor fusion according to claim 2, characterized in that: The data fusion module also 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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